Job Description

Opportunities may be available from time to time in any location in which the business is based for suitable candidates. If you are interested in a career with Citadel, please share your details and we will contact you if there is a vacancy available.

Researchers build and deploy models across equities, options, fixed income, and derivatives domains, often working with petabyte-scale data.

Models incorporate advanced techniques: deep learning, sequence / time-series models, representation learning, and methods to tame overfitting and ensure robustness in financial regimes.

The goal: each model isn't a theoretical exercise — it has a measurable P&L impact (or reduces risk / cost) when deployed in live market-making or trading contexts.

Key Responsibilities:

• Conduct cutting-edge research and development in machine learning, with a focus on large language models (LLMs) and Deep learning and their applications in quantitative finance.
• Design, implement, and optimize machine learning models for performance and scalability, particularly in financial contexts.
• Collaborate with cross-functional teams to integrate ML solutions into business processes and trading strategies.

Skillset Requirements:

• Proficiency in creating and using algorithms to meticulously investigate and work through large data or error-checking problems
• Deep knowledge of LLM architectures, including transformers.
• Familiarity with attention mechanisms, normalization techniques, and model architecture design.
• Training techniques (pre-training, fine-tuning, RLHF), and optimization methods.
• Understanding of low-level details like GPU memory management, precision types (float16, bfloat16), and parallelization techniques.
• Proficiency in advanced training techniques such as pre-training, fine-tuning, RLHF, and DPO.
• Expertise in Python and ML frameworks like PyTorch or TensorFlow.
• Familiarity with Retrieval Augmented Generation (RAG) systems and their implementation.
• Proven ability to approach open-ended problems and design end-to-end solutions in ML/AI.
• Strong mathematical and statistical foundations, particularly in areas relevant to quantitative finance.

About Citadel Securities

Citadel Securities is a technology-driven, next-generation global market maker. We provide institutional and retail investors with world-class liquidity, competitive pricing and seamless front-to-back execution in a broad array of financial products. Our teams of engineers, traders and researchers harness leading-edge quantitative research and the accelerating power of compute, machine learning and AI to power our analytics and tackle the market’s and our clients’ most critical challenges. Together, we are forging the future of capital markets. For more information, visit citadelsecurities.com.

Our formula for success is to hire exceptional people, encourage their ideas and reward their results.

As a Quantitative Research Intern you will have an opportunity to solve challenging problems arising in a trading environment while utilizing the latest statistical scientific algorithms, machine learning techniques and derivatives pricing theory. The teams focus on non-latency sensitive investment opportunities and multi-asset class derivatives strategies across geographies. Our teams emphasize cutting-edge innovative scientific research and collaboration, allowing you to gain a deeper understanding of quantitative trading. You will find great minds with diverse backgrounds, who are passionate about cultivating new ideas and exploring ways to bring them to life. You will use the team’s custom research infrastructure for simulation, back-testing, and validation of the proposed models. While your days will have you engrossed with complex technology projects, your evenings will be spent exploring the city with organized social events to truly discover what it’s like to live and work in Chicago or New York City.

How you will make an impact…

Create practical solutions to problems presented in the trading environment on either a systematic equity trading desk or a fixed income options desk | Conduct statistical analysis of market data, historical trends, and relationships across multiple asset classes | Formulate and apply mathematical modeling, quantitative methods and machine learning techniques to identify and capture trading opportunities | Work closely with traders and researchers to build and refine research infrastructure and tools

What you bring to the team…

Are pursuing a Bachelor’s, Master’s or PhD in a technical discipline with a focus on Statistics, Optimization, Machine Learning, Artificial Intelligence, Quantitative Finance or related fields graduating between December 2027 and August 2028 | Proficiency in Python programming experience using the Python machine learning stack: numpy, pandas, scikit-learn, etc. | Proficient programming skills with experience exploring large datasets | Strong analytical and problem-solving skills including a solid foundation of statistics knowledge | Working knowledge of probability theory, stochastic calculus and numerical algorithms such as finite differences, Monte Carlo simulation, etc. | Some exposure to Natural Language Processing and/or High-Performance Computing is a plus | Excellent written and verbal communication skills to report research results as well as methodologies | Added bonus if you have been published in a top tier journal focusing on Natural Language Processing or High-Performance Computing

What to expect during the internship

Meaningful projects: Each project, advised by a trader, promotes a comprehensive learning experience and provides you with real-world work experience. | Community: Throughout the summer, we host a variety of educational, social and team-building activities to explore the city, foster friendships and camaraderie. | Housing: DRW provides fully furnished apartments located close to the office – making your morning commute as easy as possible. | Mentorship: You’ll build a professional relationship with an experienced mentor in your field. Mentors and mentees meet to discuss goals, challenges and professional development and explore the city together at our mentor outings. | Education: As the trading industry continually evolves, both in terms of new products and transaction methods, the future will present us with unique opportunities and challenges. You’ll complete an options course taught by an experienced trader and participate in a technology immersion course to better understand how technology and trading intersect.

DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world.

Our formula for success is to hire exceptional people, encourage their ideas and reward their results.

As a Quantitative Researcher, you will develop mathematical models using advanced statistical learning methods to build automated trading strategies across multiple asset classes. Our research team collaborates on idea generation and strategy development, while encouraging independent exploration and original approaches. You will have access to clean data integrated with compute resources and the support of experienced researchers, traders and dedicated software developers. You will conduct quantitative analysis of market data to uncover relationships and identify historical trends. We do not require familiarity with or prior experience in financial markets for this role. We will give you the training you need to be successful.

How you will make an impact

Extract predictive signals from financial data through both traditional statistical analysis methods and cutting edge machine learning techniques | Assist software developers to translate research strategies into production software | Optimize the order execution and risk management of our trading system | Create robust solutions to problems presented in the trading environment | Formulate and apply mathematical modeling techniques to enhance existing trading strategies and perform innovative new research with the goal of identifying and capturing trading opportunities | Automate human-decision based trading strategies; prototype algorithmic trades from trading ideas

You will be right at home if you have…

A degree in a technical discipline with a focus on statistics, machine learning, signal processing, optimization and control graduating between December 2026 and August 2027 (Bachelor’s, Master’s, PhD) | A curiosity for model development and experience handling large data sets | Expertise in programming using Python, R, MATLAB, or C++ for conducting research | Can advocate for your beliefs in a concise and effective way with the team | Significant hands-on experience applying machine learning algorithms to real world problems | Strong problem-solving and statistics skills | The proactive ability to take the lead on assignments and deliver practical research results in a timely manner

DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.

Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets.

We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus.

The annual base salary range for this position is $250,000 to $300,000 depending on the candidate’s experience, qualifications, and relevant skill set. The position is also eligible for an annual discretionary bonus. In addition, DRW offers a comprehensive suite of employee benefits including group medical, pharmacy, dental and vision insurance, 401k (with discretionary employer match), short and long-term disability, life and AD&D insurance, health savings accounts, and flexible spending accounts.

For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice.

Multimodal Research Data Engineer (Generalist)

We have roles across 4 locations. Please follow the links below for full details and application page:

Beijing, China

London, UK

San Francisco, US

Sydney, AU

Vienna, Austria

Canva Research's mission is to develop AI technology powering Canva's Creative Operating System, enabling everyone to design. We combine research and practical methods to solve real-world challenges, building technology that makes Canva an AI-native creative platform. Established in June 2025, Canva Research has expanded rapidly across Sydney, London, Vienna, San Francisco, and Beijing. Our goal is to turn research breakthroughs into features that enhance design accessibility, capability, and enjoyment.

About the team

We focus on multimodal agentic architectures, scalable training and evaluation, and collaborate with product teams to integrate breakthroughs into our products. Our cutting-edge research spans all forms of multimodal modeling, training, and agent design.

About the role

You will manage the data lifecycle for agent research, from collection to preprocessing and delivery into training pipelines. This role involves working with image, video, 3D, text, and audio data. You will collaborate with research scientists to design and build reliable, scalable data systems, with autonomy over solving data problems while aligning with team priorities.

What you'll do

• Design and build data pipelines for agent training across various media.
• Maintain infrastructure for scalable data loading, storage, and retrieval.
• Collaborate with scientists to translate research needs into data specifications.
• Create evaluation datasets and benchmarks to expose failure modes.
• Develop tools for dataset construction and human annotation workflows.
• Ensure data quality through validation frameworks and monitoring.
• Document datasets comprehensively, covering provenance and limitations.
• Implement test coverage for data pipelines and ML workflows.
• Enhance codebase quality through reviews and best practices.
• Identify data bottlenecks and propose solutions to improve research velocity.

You're likely a match if you have

• Strong Python skills and experience with production-grade data pipelines and ML DevOps.
• A generalist mindset, quick to adapt to new data modalities.
• Experience with prompt engineering and ML data workflows.
• Hands-on experience with data pipelines for distributed ML training.
• Familiarity with annotation tools and human-in-the-loop data collection.
• Understanding of ML training requirements and good data characteristics.
• Experience with cloud infrastructure for large datasets.
• Strong communication skills to work with researchers on problem-solving.
• A collaborative approach, comfortable with ownership and iteration.

Flow Traders is looking for a Senior Research Engineer to join our London office. This is a unique opportunity to join a leading proprietary trading firm with an entrepreneurial and innovative culture at the heart of its business. We value quick-witted, creative minds and challenge them to make full use of their capacities.

As a Senior Research Engineer, you will be responsible for helping to lead the development of our trading model research framework and using it to conduct research to develop models for trading in production. You'll expand the framework to become global standard way of training, consuming, combining, and transforming any data source in a data-driven systematic way. You will then partner with Quantitative Researchers to build the trading models themselves.

What You Will Do

• Help to lead the development and global rollout of our research framework for defining and training models through various optimization procedures (supervised learning, backtesting etc.), as well as its integration with our platform for deploying and running those models in production
• Partner with Quantitative Researchers to conduct research: test hypotheses and tune/develop data-driven systematic trading strategies and alpha signals

What You Need to Succeed

• Advanced degree (Master's or PhD) in Machine Learning, Statistics, Physics, Computer Science or similar
• 8+ years of hands-on experience MLOps, Research Engineering, or ML Research
• A strong background in mathematics and statistics
• Strong proficiency in programming languages such as Python, with experience in libraries like numpy, pytorch, polars, pandas, and ray
• Demonstrated experience in designing and implementing end-to-end machine learning pipelines, including data preprocessing, model training, deployment, and monitoring
• Understanding of and experience with modern software development practices and tools (e.g. Agile, version control, automated testing, CI/CD, observability)
• Understanding of cloud platforms (e. g., AWS, Azure, GCP) and containerization technologies (e. g., Docker, Kubernetes)

Expression of Interest - CommBank AI Labs Scholars, 6-month onsite AI internship

Who are we?

CommBank AI Labs are a globally recognised applied AI research team, combining academic rigour with real-world impact CommBank is ranked #1 Asia Pacific bank for AI maturity by the 2026 Evident AI Index (#4 globally) Partnerships with global leaders including Anthropic, AWS, Google, Microsoft and OpenAI Publishers of research at top-tier conferences while building real-world AI products that improve our customer experience and ways of working.

You are

A PhD researcher from a leading institution, driven to take your work beyond theory by applying cutting-edge research to meaningful problems, publishing at top venues, and building a globally distinctive research profile.

You’re motivated by depth, rigour, and impact and excited by the opportunity to see your research translated into real-world systems used at scale.

Together we can

Take a research idea from concept to publication and see it evolve into real-world products and experiences that can impact millions of customers.

Bring your ambition and we’ll help you grow, belong and shape what’s next in applied AI research.

Do work that matters

At CommBank AI Labs, we focus on research that delivers both academic contribution and applied impact, spanning agentic systems, LLM evaluation, causal machine learning, and responsible AI.

This program is designed for researchers who want to:

Produce high-quality, publishable work Work on complex, unstructured, real-world datasets Understand how research transitions into live products and customer experiences

Over six months in Sydney, you’ll work on a defined research problem — with a clear pathway to submission at a top-tier conference, and visibility into how your work could translate into production systems used at scale.

See yourself in our team

You’ll join a global network of AI scientists, decision scientists and engineers, working together to solve complex challenges through advanced analytics and machine learning.

You’ll be surrounded by people who are equally committed to research excellence, innovation, and real-world outcomes.

What makes this different A six-month onsite residency designed to enable meaningful research outcomes Access to large-scale, real-world datasets and problem spaces Opportunity to contribute to AI-driven products impacting millions of customers Highly competitive compensation and salary (AUD) aligned to global benchmarks Relocation support, including visa sponsorship and initial accommodation

Mentorship & research environment You’ll be supported by a diverse set of experienced AI practitioners, researchers and decision scientists across multiple disciplines with an unmatched wealth of expertise from global banking and AI research. You will work within a team led by CommBank’s Chief AI Scientist, renowned AI researcher Professor Mary-Anne Williams.

This creates a faculty-style environment, where your work is shaped by multiple perspectives, strengthening both the quality and impact of your research.

What you’ll work on - We’re particularly interested in researchers working on:

Agentic AI systems and multi-agent orchestration LLM evaluation, alignment, and AI safety Causal inference and counterfactual reasoning Responsible AI, fairness, and interpretability Applied AI for financial services (risk, fraud, decisioning)

We’re interested in hearing from people who: Are currently enrolled in a PhD or within 12 months post-submission Have a strong research track record in Machine Learning, AI, Statistics, Computer Science or related fields Have first-author publications or submissions at leading conferences (NeurIPS, ICML, ICLR, AISTATS, EMNLP, IJCAI or similar) Are motivated to apply research to complex, real-world problems Can be based in Sydney for the duration of the program

Job Description

About Global Quantitative Strategies (GQS)

GQS is the quantitative investment business unit of Citadel. Founded in 2012, GQS has grown rapidly to become one of Citadel’s core investment strategies and one of the top quantitative investment teams in the world. Collaborative teams of researchers, engineers, and traders build robust systems that operate at scale and apply advanced quantitative and machine learning techniques to identify investment opportunities.

Machine Learning Researchers at GQS develop and deploy models across a variety of asset classes. Their work spans deep learning, sequence and time-series modeling, natural language processing, large language models, pre-training, reinforcement learning, and methods for improving robustness in complex financial regimes.

Location

New York, Singapore

Required Skills and Qualifications:

• Advanced degree in Computer Science, Machine Learning, Mathematics, Statistics, Engineering, Physics, or a related quantitative field
• Proven ability to conduct innovative and impactful research focused on solving real-world problems
• Deep expertise in machine learning and deep learning, such as sequence modeling, large language models, or reinforcement learning
• Experience with modern training techniques such as pre-training, fine-tuning, reinforcement learning, or related optimization methods
• Expertise in Python and machine learning frameworks such as PyTorch or JAX
• Strong mathematical and statistical foundations
• Ability to design, implement, and optimize machine learning models for performance, scalability, and robustness
• Demonstrated interest in financial markets and a drive to apply machine learning techniques to model price formation, market behavior, and risk

We collect and use personal data in accordance with our Privacy Policy. We retain data on prospective candidates and may consider suitability for alternative opportunities at Citadel. For more information, see our Privacy Policy.

In accordance with applicable law, the base salary range for this role is $300,000 to $350,000.

In addition, the employee who fills this role will be eligible to participate in a discretionary incentive compensation program, as well as a wide array of benefit programs, such as medical and life insurance, retirement and tax-free savings plans, and access to other healthcare programs.

About Citadel

Citadel is one of the world’s leading alternative investment managers. We manage capital on behalf of many of the world’s preeminent private, public and nonprofit institutions. We seek the highest and best use of investor capital in order to deliver market leading results and contribute to broader economic growth. For over 30 years, Citadel has cultivated a culture of learning and collaboration among some of the most talented and accomplished investment professionals, researchers and engineers in the world. Our colleagues are empowered to test their ideas and develop commercial solutions that accelerate their growth and drive real impact.

Department of Computer Science, Stony Brook University Stony Brook, NY 11794

Postdoctoral Associate

Required Qualifications: (as evidenced by an attached resume) Doctoral Degree (or foreign equivalent) in Computer Science or a closely related field in hand by the appointment start date.

https://stonybrooku.taleo.net/careersection/2/jobdetail.ftl?job=2602791

Preferred Qualifications:
Research experience in computer vision, NLP, geospatial data systems, or high-performance computing.

Brief Description of Duties: The Postdoctoral Associate will assist the Principal Investigator Dr. Steven Skiena in the Department of Computer Science at Stony Brook University. The incumbent will conduct cutting-edge research at the intersection of AI/ML and extreme weather forecasting, employing data science models as developed in computer vision and natural language processing (NLP). This project leverages the large-scale high-performance computing (HPC) infrastructure of New York State’s Empire AI enabling us to train large AI models that will outperform traditional numerical prediction models.

Duties:

● Design, train, and evaluate novel AI/ML models. Develop computational innovations to enhance model efficiency, architecture, and performance.

● Collaborate on additional projects using text and vision models building on their own research interests.

● Co-author manuscripts for submission to leading scientific journals.

● Present research at group meetings, seminars, and conferences.

● Other duties as assigned.

Special Notes: The Research Foundation of SUNY is a private educational corporation. Employment is subject to the Research Foundation policies and procedures, sponsor guidelines and the availability of funding. FLSA Exempt position, not eligible for the overtime provisions of the FLSA. Minimum salary threshold must be met to maintain FLSA exemption.

Visit The Office of Postdoctoral Affairs to learn more about our postdoctoral community.

Resume/CV and cover letter should be included with the online application.

Stony Brook University is committed to excellence in diversity and the creation of an inclusive learning, and working environment. All qualified applicants will receive consideration for employment without regard to race, color, national origin, religion, sex, pregnancy, familial status, sexual orientation, gender identity or expression, age, disability, genetic information, veteran status and all other protected classes under federal or state laws.

If you need a disability-related accommodation, please call the university Office of Equity and Access (OEA) at (631) 632-6280 or visit OEA.

In accordance with the Title II Crime Awareness and Security Act a copy of our crime statistics can be viewed here.

Visit our WHY WORK HERE page to learn about the total rewards we offer.

SUNY Research Foundation: A Great Place to Work.

The starting salary range (or hiring range) to be offered for this position is noted below, it represents SBU’s good faith and reasonable estimate of the range of possible compensation at the time of posting.

Our Machine Learning Engineering Internship is a 10-week immersive experience designed for students who are passionate about building the systems at the intersection of machine learning, large-scale data, and markets.

As a Machine Learning Engineering Intern, you'll work on high-impact projects that closely reflect the challenges and workflows of our full-time engineering team. You'll apply your software engineering skills to real machine learning systems while developing a deep understanding of how machine learning integrates into Susquehanna's research and trading systems. A model is useful only if it can be trained quickly, fed reliably, and run fast enough to act on - our engineers own the systems that make that true, and we scope intern projects the same way.

Susquehanna's proprietary datasets and computing infrastructure - including a rapidly growing cluster of thousands of high-end GPUs - support computationally intensive training, simulation, and rapid experimentation. Engineering teams here are small and highly collaborative, ideas are debated openly, and work that proves out reaches production quickly.

What You Can Expect • Build and optimize training pipelines that run across our GPU infrastructure, including distributed training for large models • Work on inference and deployment — the latency, throughput, and cost of models running in production trading systems • Develop the data infrastructure behind our research, moving large and noisy market datasets through ingestion, storage, and transformation • Profile and benchmark machine learning workloads, and contribute to the internal libraries and open-source tools our researchers rely on every day • One-on-one mentorship from experienced engineers and researchers • Participate in a comprehensive education program with deep dives into Susquehanna's ML, quant, and trading practices • No prior finance background required

What We’re Looking For • Currently pursuing a Bachelor's, Master's, or PhD in computer science, machine learning, electrical engineering, mathematics, physics, statistics, or a related technical field. Intention to graduate and begin full time employment by August 2028 • Strong programming skills in Python, working comfort in a systems language such as C++, a plus • Hands-on experience with machine learning frameworks such as PyTorch or JAX that goes beyond calling the API - training at scale, extending a library, or making an existing workflow measurably faster • Exposure to the systems machine learning runs on: distributed training, GPU programming, orchestration, or large-scale data processing. We're more interested in what you did with a tool and what it changed than in seeing it named on a list • Experience with GPU kernel programming in CUDA, Triton, or CuTe DSL • Solid computer science fundamentals: data structures, algorithms, concurrency, and an understanding of how software behaves on real hardware • A project, system, or open-source contribution you can walk through in detail - what you designed, what you decided, and what you specifically did • Deep interest in solving complex problems and a drive to innovate in a fast-paced, competitive environment

The Michael Smith Laboratories (MSL) and the Department of Biochemistry & Molecular Biology (BMB) at the University of British Columbia (UBC) invite applications for a Canada Impact+ Emerging Leader in AI-based Protein Modeling and Design. This is a tenure-track appointment at the rank of Assistant Professor, jointly appointed in MSL and BMB, with an anticipated start date no later than July 1, 2027.

AI-driven protein design is one of the most rapidly advancing frontiers in science, with transformative potential in medicine, biotechnology, and the life sciences. We seek outstanding candidates with an innovative and impactful research program integrating deep learning and AI-driven approaches across the protein design pipeline. Research programs may leverage structure-based models, protein language models, generative AI, or novel hybrid approaches. Areas of interest include, but are not limited to, developing predictive and generative models for protein structure and function, advancing AI-enabled design-build-test-learn workflows, and creating new tools for the characterization and validation of engineered proteins. Example application areas include the design of protein binders with tailored binding specificity and affinity, protein conformational switches that sense and respond to their environment, de novo enzymes for applications in medicine and sustainability, and proteins that interact selectively with non-protein molecules (e.g. nucleic acids, carbohydrates, small molecules).

About the Canada Impact+ Emerging Leaders Program The Canada Impact+ Emerging Leaders program is a one-time initiative designed to support institutions in attracting internationally based Early Career Researchers to Canada. Emerging Leaders will receive research funding and institutional supports to advance transformative research in Canada's strategic priority areas, build partnerships across sectors and borders, and translate their research into applications and impacts that benefit Canadians and the world. Canada Impact+ Emerging Leaders are six-year positions, renewable once for an additional six years, intended for exceptional emerging scholars who have the potential to lead in their fields. Applicants must be an early career researcher and be eligible to hold a full-time, tenure-stream appointment at the rank of Assistant Professor at UBC.

Under the Canada Impact+ Emerging Leaders program, nominees must be working and residing outside Canada when the Eddie Goldenberg Research Chairs of Canada award (https://www.canada.ca/en/impact-plus-chairs.html is formally accepted. For this specific award, the acceptance date was July 1, 2026. Applicants may apply regardless of their current location; however, candidates selected for nomination must meet this eligibility requirement on the award acceptance date in order to proceed. Expatriate Canadians wishing to relocate to Canada are welcome to apply.

For more details and how to apply, please refer to UBC Faculty Careers website: https://ubc.wd10.myworkdayjobs.com/en-US/ubcfacultyjobs/job/UBC-Vancouver-Campus---Vancouver-BC-Canada/Canada-Impact--Emerging-Leader-in-AI-based-Protein-Modeling-and-Design_JR26070