NeurIPS 2026 Career Opportunities

The interdisciplinary team at Emory University and Georgia Tech is recruiting a Postdoctoral Fellow in Machine Learning and Multimodal AI. The position is supported by a five-year NIH R01-funded study.

We are seeking a researcher interested in developing novel multimodal, continual, and human-in-the-loop learning methods for real-world healthcare applications, with a particular focus on understanding human movement and Parkinson’s disease. Our team brings together researchers across Biomedical Informatics, Biomedical Engineering, Neurology, Electrical and Computer Engineering, and Machine Learning. The fellow will work with Hyeok Kwon (PI, VITAL Lab; https://kwonvitallab.github.io/) and an interdisciplinary team of ML, engineering, and clinical collaborators.

Research directions include:

• Multimodal machine learning from video, sensor, behavioral, and clinical data
• Computer vision and human activity/movement recognition for Parkinson’s disease
• Continual learning, adaptation, and clinician-in-the-loop learning for evolving real-world data
• Learning and inference under distribution shifts and heterogeneous multimodal data
• Translation of ML methods to mobile, edge, and cloud AI systems deployed in clinical environments

The position provides an opportunity to work on fundamental machine learning problems while evaluating new methods using real-world longitudinal clinical data and deployments in Emory Movement Disorders clinics.

We welcome candidates with strong backgrounds in machine learning, multimodal learning, computer vision, representation learning, continual/test-time learning, human activity recognition, ubiquitous computing, or related areas.

Application: https://faculty-emory.icims.com/jobs/172904/post-doctoral-fellow/job

Lab: https://kwonvitallab.github.io/

Interested candidates are welcome to contact Hyeok Kwon directly at hyeokhyen.kwon@emory.edu or hyeokhyen.kwon@gatech.edu. Please feel free to forward this announcement to anyone who may be interested.

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)

Flow Traders is looking for a Junior Quantitative Researcher to join the quantitative trading team in our Amsterdam office. This is a unique opportunity to join a leading proprietary trading firm, working alongside some of the brightest minds in the industry. As a firm, we are committed to leveraging advances in computer science, statistics, and machine learning to generate value in the financial markets.

A successful Quantitative Researcher is an expert in mathematics and statistics, passionate about translating challenging quantitative problems into equations and models, and skilled at optimizing them using cutting-edge computational techniques. If you're at the top of your quantitative, modeling, and coding game, and excited to test these skills in competitive live markets, this opportunity is for you.

What you will do

• Design, build, optimize, and deploy state-of-the-art models and algorithms for systematic trading.
• Analyze large datasets to identify patterns and signals that can be translated into trading strategies.
• Collaborate with traders and technologists to bring models from research into live production.
• Monitor and refine existing models based on live market performance.

What you need to succeed

• A PhD degree in Mathematics, Physics, Computer Science or a related quantitative field.
• Strong statistical and linear algebra knowledge.
• Proficiency in implementing models and algorithms in programming languages such as Python or C++.
• Experience in Deep Learning is a plus.
• Strong analytical and problem-solving skills, with the ability to work independently on open-ended, ambiguous problems.
• Ownership and entrepreneurship.

Locations: London, United Kingdom; New York, NY, United States

Hudson River Trading (HRT) is seeking an AI Research Engineer (Pre-training) to join the HAIL team. HAIL (HRT AI Labs) is the team at HRT responsible for developing and maintaining our most powerful models, which are used by our trading teams to drive a significant fraction of our trading. We are building and deploying "foundation models for markets", that ingest and train on vast amounts of market and “alternative” data (such as language) to make predictions about future market state.

As a pre-training research engineer on HAIL, you will have a general mandate to improve all aspects of large-scale model training, including but not limited to kernel development, training data loading, parallelism, networking, and fault-tolerance. You will work closely with our researchers to co-design and improve our models, and shape the research agenda.

We have multiple large, modern, and rapidly growing GPU clusters, and we maintain a very high GPU-to-researcher ratio. We are simultaneously pursuing multiple strategies and developing many model types with different purposes, and we are strongly incentivized to squeeze as much as we can out of our systems. Your work will be directly, clearly, and highly impactful on the business, and it will be challenging: this is a field with no easy or obvious solutions.

Qualifications

Strong engineering skills, especially any of: CUDA/Triton/Pallas/CuTe DSL kernel development, lower-level PyTorch/JAX/XLA development, CUDA Graphs, FPGA/ASIC experience Must have two or more years work experience building deep learning systems, for any domain: robotics, biology, chemistry, physics, audio, video, recommendations, etc. Experience translating methods between areas of application is highly valued LLM experience is valuable, but not necessary Finance experience is not required The estimated base salary range for this position is 250,000 to 300,000 USD per year (or local equivalent). The base pay offered may vary depending on multiple individualized factors, including location, job-related knowledge, skills, and experience. This role will also be eligible for discretionary performance-based bonuses and a competitive benefits package.

Culture

Hudson River Trading (HRT) brings a scientific approach to trading financial products. We have built one of the world's most sophisticated computing environments for research and development. Our researchers are at the forefront of innovation in the world of algorithmic trading.

At HRT we welcome a variety of expertise: mathematics and computer science, physics and engineering, media and tech. We’re a community of self-starters who are motivated by the excitement of being at the cutting edge of automation in every part of our organization—from trading, to business operations, to recruiting and beyond. We value openness and transparency, and celebrate great ideas from HRT veterans and new hires alike. At HRT we’re friends and colleagues – whether we are sharing a meal, playing the latest board game, or writing elegant code. We embrace a culture of togetherness that extends far beyond the walls of our office.

Feel like you belong at HRT? Our goal is to find the best people and bring them together to do great work in a place where everyone is valued. HRT is proud of our diverse staff; we have offices all over the globe and benefit from our varied and unique perspectives. HRT is an equal opportunity employer; so whoever you are we’d love to get to know you.

Please be advised: Use of AI tools during interviews or assessments is strictly prohibited, unless otherwise instructed or agreed upon. We employ various methods to evaluate the authenticity of candidate responses. If we determine that AI assistance was used during any stage of the hiring process, we reserve the right to immediately disqualify your candidacy or rescind any job offers extended.

We offer opportunities to do your life’s work while helping solve one of the most important technical and moral challenges of our time.

Full-time, in-office in Emeryville, California.

Turn raw assay video into precise, reviewable measurements of what mosquitoes do over time. The work begins with detection and tracking, but the scientific outcome is a trustworthy behavioral record that can train and evaluate models.

Key Responsibilities

• Develop and validate methods for detecting and tracking multiple mosquitoes in top-mounted behavioral-assay video
• Derive cumulative landing-zone occupancy, trajectories, spatial distribution, entry and exit rates, dwell time, and other interpretable behavioral features
• Build representative labeled datasets and error analyses across labs, cameras, lighting conditions, arenas, mosquito densities, and occlusion patterns
• Quantify confidence and route uncertain or anomalous results to efficient human review rather than silently producing a score
• Design visual overlays and quality-control tools that let scientists inspect how each measurement was produced
• Work with entomologists and lab teams to improve camera placement, assay geometry, capture standards, and the behavior labels that matter scientifically

Qualifications

• Strong experience with object detection, multi-object tracking, segmentation, pose or trajectory analysis, or related computer-vision methods
• Strong Python skills and experience with PyTorch, OpenCV, or equivalent tools
• Experience building evaluation sets and choosing metrics that reflect the downstream use of a vision system
• Ability to build efficient video-processing pipelines and debug failures at the frame and sequence level
• Clear communication with domain scientists and software engineers

Desired Attributes

• Experience with small-object tracking, animal behavior, microscopy, or other visually difficult scientific video
• Experience with domain adaptation, weak supervision, active learning, or human-in-the-loop annotation
• Familiarity with camera calibration, experimental instrumentation, or cross-site capture standardization
• Interest in making scientific measurements interpretable and auditable

Full description

Apply

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

The Team

Our AI research team sits at the heart of our mission to unlock new dimensions of biological understanding. You will leverage state-of-the-art AI to accelerate discovery and drive transformative insights in biology—developing novel AI models purpose-built for biological research, engineering robust systems that enable breakthrough science at unprecedented scale, and translating these advances into practical tools that empower researchers worldwide. Our approach is comprehensive and integrated, bringing together world-class AI model development, exceptional engineering talent, high-quality biological data, powerful computing infrastructure, and strategic partnerships. Success requires excellence across five interconnected pillars: training frontier AI models specifically for biology; building engineering systems that maximize research velocity and efficiency; executing a sophisticated data strategy that fuels AI development; operating a world-class AI compute platform; and creating impactful products that transform AI capabilities into accessible scientific tools.

The Opportunity

This role is part of the Data team, which focuses on owning the strategy, sourcing and implementation for data supporting AI research and development. Our goal is to maximize the speed, agility, and capability of biological AI research by connecting public data resources and Biohub's experimental platforms to AI systems. The data that trains biological frontier models comes in dozens of modalities—sequences, images, spatial coordinates, time series, molecular structures, metadata, preprints and published papers—each with its own noise characteristics, biases, and information content. The question of how to represent this data for learning is one of the most important open problems in biological AI.

What You'll Do

Design data representations and tokenization strategies for imaging data that enable novel model architectures Coordinate Experimental, Data Science, Data Engineering and AI Research teams to translate biological structure into learnable representations—defining priorities and appropriate structures for metadata and data that information models can access and consume Work across those teams to guide data acquisition priorities, define quality criteria, and assess external datasets from a representation perspective Develop and validate approaches for combining heterogeneous data modalities into unified training frameworks, designing for robustness to noise, bias, and batch effects Evaluate how representation choices impact model performance, identifying which biological signals are captured or lost and iterating to improve

What You'll Bring

PhD in computational biology, bioinformatics, or a quantitative biological field Experience with tokenization strategies for non-text data (images, sequences, graphs, time series) Track record of novel methodological contributions (publications, open-source tools, or production systems) Familiarity with biological foundation models (ESM, scGPT, or similar) Deep understanding of imaging data, their underlying data characteristics, and how to transform raw data into ai-ready datasets. Experience designing data representations or feature engineering for machine learning, ideally in scientific or biological contexts Familiarity with modern ML architectures (transformers, diffusion models, or similar) and how data representation choices affect learning Strong computational skills (Python, scientific computing libraries); comfort working with large-scale datasets Creative, first-principles thinking about how to structure data for learning

Compensation The Redwood City, CA base pay range for a new hire in this role is $214,000 - $294,800. New hires are typically hired into the lower portion of the range, enabling employee growth in the range over time. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process.

Locations: London, United Kingdom; New York, NY, United States; Singapore

Hudson River Trading (HRT) is seeking exceptional full-time PhD students to join our Algorithm Development teams in New York, London, and Singapore. Algorithm Developers at HRT are responsible for building and maintaining the models that drive our trading. A typical day involves applying rigorous statistical analysis to vast quantities of market and financial data to produce predictive trading models.

In this role, you will work alongside fellow Algorithm Developers and Software Engineers to research, develop, and test novel order execution and model training methods to increase trading efficiency. This will involve running models live on our high-performance trading infrastructure and analyzing daily performance to maintain ongoing profitability. You can expect to apply your advanced academic research experience and expertise to impactful real world problems in trading across time horizons and machine learning strategies.

Ideal candidates are excited to apply their research expertise to identify new opportunities in worldwide markets, enjoy both self-guided research and collaborating with others to analyze and fix problems efficiently, and are critical thinkers who can learn and implement new skills in a fast-changing environment.

Qualifications

You are a full-time PhD student in a quantitative discipline (math, physics, computer science, statistics, or a related program) who is eligible for full-time roles in 2027 Fluency in Python Experience with statistical analysis, numerical programming, or machine learning in Python, Pandas/Numpy, R, and/or MATLAB Brilliant analytical and problem-solving skills Ability to work creatively and independently on long-term technical problems Base salary for US is $300,000. Other locations have similarly locally competitive base salaries. A sign-on and discretionary performance bonus will be provided as part of the total compensation package, in addition to company-paid medical and/or other benefits.

We do not allow multiple applications. Please apply to the ONE role you are most interested in and we will consider you for all open positions when reviewing your application.

Culture

Hudson River Trading (HRT) brings a scientific approach to trading financial products. We have built one of the world's most sophisticated computing environments for research and development. Our researchers are at the forefront of innovation in the world of algorithmic trading.

At HRT we welcome a variety of expertise: mathematics and computer science, physics and engineering, media and tech. We’re a community of self-starters who are motivated by the excitement of being at the cutting edge of automation in every part of our organization—from trading, to business operations, to recruiting and beyond. We value openness and transparency, and celebrate great ideas from HRT veterans and new hires alike. At HRT we’re friends and colleagues – whether we are sharing a meal, playing the latest board game, or writing elegant code. We embrace a culture of togetherness that extends far beyond the walls of our office.

Feel like you belong at HRT? Our goal is to find the best people and bring them together to do great work in a place where everyone is valued. HRT is proud of our diverse staff; we have offices all over the globe and benefit from our varied and unique perspectives. HRT is an equal opportunity employer; so whoever you are we’d love to get to know you.

Please be advised: Use of AI tools during interviews or assessments is strictly prohibited, unless otherwise instructed or agreed upon. We employ various methods to evaluate the authenticity of candidate responses. If we determine that AI assistance was used during any stage of the hiring process, we reserve the right to immediately disqualify your candidacy or rescind any job offers extended.

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 Hong Kong 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)