Expression of Interest: Senior Data Scientist – ML, Gen AI

Do work that matters

At CommBank, we're investing in AI to create better customer experiences and meaningful value for the communities we serve. We welcome international talent who would love the experience of working in Australia. We encourage you to apply if you're open to diversifying your global experience and solving world-class AI problems across the Group in collaboration with our own AI Research Labs.

We operate with a fintech mindset as we grow our global footprint and build our people's careers, including a diversified culture of international Tech Hubs established in Seattle and San Francisco, USA, working alongside unique partnerships with OpenAI, Anthropic, AWS and Microsoft to accelerate safer AI outcomes for Australia. Join us to experience this first-hand!

As a Senior Data Scientist, you’ll work on opportunities connected to the Customer Service Network, supporting branch and customer service experiences through traditional machine learning, GenAI and Agentic AI. You’ll be hands-on with delivery, while also helping shape and influence strategic conversations around the data landscape, data quality, data risk and governance as well as identify where AI can transform and innovate.

See yourself in our team

This key role will focus on helping teams move from experimentation to production with greater speed, rigour and impact. We’re equipping data scientists with AI-enabled tools and reusable assets that support feature engineering, model development, validation, deployment and monitoring.

In this role, you’ll partner with product, technology and business stakeholders to identify where machine learning, GenAI and Agentic solutions can automate, simplify and improve processes. You’ll bring strong technical judgement and the ability to explain complex ideas clearly to senior stakeholders.

You’ll do this by:

• Designing, building and productionising machine learning, GenAI and Agentic AI solutions that solve practical business problems.

• Translating branch and customer service challenges into clearly defined predictive, optimisation or Agentic solution approaches.

• Shaping GenAI and Agentic AI opportunities across customer service and branch-related journeys.

• Translating complex business questions into well-defined machine learning or Agentic solution approaches.

• Supporting the data science lifecycle, from ideation and discovery through model build, validation, deployment, monitoring and ongoing governance.

• Influencing the data landscape, data governance, data quality and data risk considerations.

• Helping define the data science strategy and technical capability required to support Customer Service Network priorities.

• Partnering with product, engineering, risk and business teams to assess opportunities, constraints, trade-offs and expected value.

Working with us

At CommBank, we foster a culture of inclusion and respect, celebrating diversity across cultures, abilities, capabilities, skills, global locations, genders, gender expressions and sexual orientations. If this role excites you but you don’t meet every requirement, we still encourage you to apply as you may be the right fit for this role or another opportunity with us.

We’re committed to making a meaningful difference for Australia’s First Nations peoples and welcome all applicants. We are open to international talent that would love to experience working in Australia.

APPLY NOW to live your best life and do your best work with the world's #4 AI Banking leader(Evident AI Index 2025) and Australia's #1 Employer of Choice for growing careers(LinkedIn Top Companies 2026)

Job Description

About Global Quantitative Strategies (GQS)

Global Quantitative Strategies is the quantitative investment business of Citadel. Founded in 2012, GQS has rapidly grown into 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 work together to develop robust systems and advanced quantitative models that allow us to operate at scale and identify investment opportunities across global markets.

Machine Learning Engineers (MLEs) in GQS work at the intersection of deep learning, quantitative research, and high-performance computing. In this role, you will collaborate closely with Quantitative Researchers and Quantitative Research Engineers to design, build, optimize, and scale the models and modeling systems that power research and production workflows.

This is not a traditional infrastructure engineering role. MLEs are deeply embedded in the research process, partnering with researchers to understand modeling challenges, translate research ideas into scalable model architectures, and improve the performance, reliability, and efficiency of machine learning systems. You will work on model architecture, distributed training, inference optimization, research tooling, and internal ML libraries that enable the development and deployment of models across major asset products globally. The work directly supports the research and productization of machine learning models used in systematic investing, including the development of new modeling approaches, the optimization of large-scale training workflows, and the creation of tools that help researchers experiment faster and more effectively.

Skills and Qualifications

• Bachelor’s, Master’s, or PhD degree in Computer Science, Engineering, Mathematics, Statistics, Machine Learning, or an equivalent technical field
• Strong programming skills in Python and experience with C++, CUDA, or other performance-oriented technologies
• Experience designing, implementing, training, or optimizing machine learning models, particularly deep learning models
• Strong understanding of model architecture, training dynamics, optimization techniques, and performance tradeoffs
• Experience working with PyTorch, TensorFlow, JAX, or similar machine learning frameworks
• Experience building or extending machine learning libraries, research tooling, model training systems, or distributed training workflows
• Ability to optimize ML workflows for training speed, inference performance, scalability, reliability, and cost efficiency
• Experience developing on a Linux stack and working in modern high-performance computing or distributed computing environments
• Ability to collaborate closely with researchers, understand open-ended research problems, and translate modeling needs into robust technical solutions
• Proven track record of solving complex technical problems with creativity, strong judgment, and attention to research impact
• Strong communication skills and the ability to work effectively across research, engineering, and infrastructure teams
• Interest in financial markets and applying machine learning to systematic investing

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 $275,000 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’

The School of Informatics at the University of Edinburgh is recruiting for a faculty position in Artificial Intelligence (AI).

Research focus The candidate's research focus may align with the following, non-exhaustive, list of topics (in no particular order): - Perception (e.g., computer vision) and action (e.g., interactive decision-making systems)
- Continual, open-ended, self-improving AI
- Agentic AI, world models, and generative models
- Embodied perception and action
- Learning from interaction and reinforcement
- Decision-making under uncertainty - Long-duration autonomy

School of Informatics The School of Informatics was ranked #1 in the UK for research power in Computer Science and Informatics Times Higher Education ranking, based on the 2021 Research Excellence Framework (REF). It is the highest-ranked UK institution and one of the top two in Europe for AI, according to the CSRankings website. For more information, please check out the School's website -- https://informatics.ed.ac.uk

Edinburgh Edinburgh is an attractive city to live in; it is walkable, enjoys proximity to nature, and features a world-class cultural scene that includes attractions such as the Edinburgh International Festival or the Edinburgh Fringe. Edinburgh was ranked 1st in the 2022 Time-Out Index of the best cities in the world, which considered everything from scenic beauty, community spirit, public transport, walk-ability, restaurants, through to cleanliness.

How to apply Deadline for applications is Oct 20th, 2026

Lecturer (i.e., Assistant Professor) in Artificial Intelligence - https://elxw.fa.em3.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1001/job/14761

Reader (i.e., Associate Professor) in Artificial Intelligence - https://elxw.fa.em3.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1001/job/14760

Master & Fully Funded PhD & Postdoctoral Positions

SMILE Lab | Medical Foundation Models · Agentic AI · Brain-Inspired AI

Location: United States
PI: Prof. Ruogu Fang
Positions: Fully Funded PhD Students & Postdoctoral Fellows

About SMILE Lab

The Smart Medical Informatics Learning and Evaluation (SMILE) Lab, led by Prof. Ruogu Fang, develops cutting-edge AI for medical imaging, neuroscience, and precision healthcare.

Our achievements include pioneering brain foundation models (BrainSegFounder), brain stimulation digital twins, and AI-driven neurodegenerative disease prediction.

We publish in leading venues including ICCV, MICCAI, Nature Computational Science, JAMA Neurology, The Lancet Digital Health, Medical Image Analysis, and IEEE TMI. Our research is supported by NIH, NSF, NVIDIA, Meta, and AFRL.

Research Opportunities

We are recruiting researchers in four directions:

• Multimodal Medical Foundation Models: Representation learning across medical imaging, neuroimaging, and clinical data.
• Agentic Medical AI: Multi-agent LLM systems for clinical reasoning, neurological diagnosis, and precision brain health.
• Human-AI Collaboration & Metacognition: AI confidence, uncertainty, trust, and human-AI decision-making.
• Brain-Inspired AI: Neuroscience-guided architectures and generative AI for cognition and emotion.

Featured Research Projects

1. Generative AI for Emotion & Brain Science (NIH R01)
Closed-loop generative AI integrating affective neuroscience, neuroimaging, and emotion modelling.

2. Human-AI Collaboration & Trustworthy Decision-Making (NSF)
Understanding how AI uncertainty, confidence, and communication influence human decisions.

3. Multi-Agent AI for Precision Brain Health
Developing collaborative AI agents for multimodal clinical reasoning, diagnosis, and treatment planning.

Why Join Us?

• Exceptional Computing: Access to 500+ NVIDIA B200 GPUs, RTX Pro 6000 GPUs, and 60,000 CPUs.
• World-Class Collaborations: Harvard, Stanford, NVIDIA, Sakana AI, Vanderbilt University Medical Center, Mass General Brigham, NYU, and others.
• Strong Research Impact: Publications in leading AI, medical imaging, and biomedical venues.
• Excellent Career Outcomes: Alumni at Microsoft, Amazon, Mayo Clinic, Ivy League institutions, and leading medical research centres.
• Full Financial Support: Competitive salaries and benefits; PhD tuition, stipend, and health insurance; conference travel and career mentorship.

About the PI

Prof. Ruogu Fang is an Associate Professor, Flowers Family Endowed Faculty Fellow, and National Academy of Medicine Victor J. Dzau Emerging Scholar. She has served as President of Women in MICCAI and Associate Editor of Medical Image Analysis. Her research has been featured by Forbes, The Washington Post, and ABC News.

Qualifications

Postdoctoral Applicants - Strong publication record in NeurIPS, ICML, ICLR, CVPR, ICCV, MICCAI, or leading journals. - Expertise in machine learning, computer vision, foundation models, or medical AI. - Independent research ability and strong motivation.

PhD Applicants - Strong Python/PyTorch and machine learning foundations. - Background in AI, computer vision, medical imaging, or neuroscience. - Research experience, curiosity, and collaborative mindset.

Prior medical or neuroscience experience is not required for applicants with strong AI backgrounds.

How to Apply

Email smilefanglab@gmail.com with:

• CV and publication list
• Transcripts (PhD applicants)
• Contact details of 2–3 references
• Optional links to papers, GitHub, or research projects

Subject: SMILE Lab Postdoc/PhD Application_Your Name

**Interested applicants attending NeurIPS 2026 in Sydney are warmly invited to arrange an in-person discussion with Prof. Ruogu Fan

Postdoctoral Fellow in AI for Cardiovascular Imaging (Holste Lab, Weill Cornell Medicine)

Location: New York, NY Start: Flexible, available now Full consideration: applications by Feb 1, 2027 (rolling)

The Holste Lab at Weill Cornell Medicine (Radiology, Population Health Sciences, Cardiology) is hiring a postdoc to lead projects building clinically translatable AI for echocardiography (cardiac ultrasound video). Representative work includes PanEcho (JAMA, 2025).

Research directions

• Foundation models for echocardiography video
• Multimodal integration of echo with CT, MRI, ECG, EHR, and clinical reports
• Rigorous validation that moves models toward clinical use

You'll work with cardiologists, sonographers, and researchers across Weill Cornell Medicine and NewYork-Presbyterian. No cardiology experience required. As an early member of a new lab, you'll help shape its research directions.

What you'll do

• Lead design, training, and evaluation of deep learning models on large-scale, real-world clinical data
• Develop self-supervised, foundation, and multimodal models
• Design translation-oriented evaluations: external and multi-site validation, subgroup and fairness analyses, prospective deployment studies
• Build reproducible DICOM and clinical data pipelines (IRB/HIPAA compliant)
• Publish in top AI venues and/or clinical journals; release open-source code and models where possible
• Pursue independent early-career funding

Required

• PhD in CS, EE, BME, or a related field (MD or MD/PhD with equivalent technical depth, e.g., first-author methods papers, considered)
• Demonstrated deep learning and computer vision expertise via first-author publications or substantial open-source work
• Proficiency in Python and PyTorch, TensorFlow, or JAX
• Experience training models at scale on shared multi-GPU clusters
• Strong interdisciplinary communication and willingness to dig into a real clinical problem

Preferred

• Medical imaging or health AI experience, especially ultrasound or cardiology (echo, ECG, cardiac CT/MR)
• Experience with large raw clinical datasets (DICOM, EHR)
• Publications at NeurIPS, ICML, ICLR, CVPR, ICCV, MICCAI, MIDL, MedIA, and/or high-impact clinical journals
• Self-supervised learning, foundation models, video/spatiotemporal modeling, or vision-language models
• Record of fellowships or other funding

Terms

• Salary: minimum $75,812, scaled by experience per the postdoc Collective Bargaining Agreement (UAW Local 4100)
• Appointment: one year, renewable annually; funding for up to three years
• Visas: J-1 and H-1B eligible
• Benefits: WCM benefits

How to apply

Submit the application form (CV, links to key papers, two references). Shortlisted candidates will interview and give a research talk.

Questions, or can't use the form? Email Dr. Gregory Holste at grh4006@med.cornell.edu with subject "Echo AI Postdoc Applicant."

Full posting: https://postdocs.weill.cornell.edu/opportunities/postdoctoral-positions/postdoctoral-fellow-ai-cardiovascular-imaging-holste-lab

Weill Cornell Medicine is an equal opportunity employer committed to diversity and inclusion.

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

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. Compensation includes equity.

Build the reliable systems that carry our data from an assay recording to a reproducible model, an evaluated prediction, and a usable recommendation for the next experiment.

Key Responsibilities

• Own pipelines for ingesting, validating, versioning, and joining assay videos, metadata, compound records, model features, and experimental outcomes
• Build reproducible training and evaluation infrastructure with clear data lineage, model versioning, automated tests, and auditable outputs
• Design interfaces between computer vision, molecular models, active-learning systems, and the lab workflow

Qualifications

• Strong production software engineering experience in Python and modern machine-learning or data systems
• Experience deploying and operating model-training, feature, evaluation, or inference pipelines in a cloud environment
• Fluency with testing, observability, data validation, version control, and reproducible computational workflows

Full description, additional qualifications, and Desired Attributes

Apply

We want to crack the hardest challenges in systematic investing and we're recruiting across all levels of experience.

We're looking for scientific minds, those with a passion for rigorous research, those with exceptional skills in technology and development, and those who approach problem solving with curiosity and persistence.

Ideal candidates will thrive in a collegial and multidisciplinary environment where people with deep expertise complement each other, learn from each other and help each other grow professionally.

Advanced degree in science, technology, engineering, or mathematics (STEM) preferred.

No investment experience required.

Job Description

At Citadel Securities, a leading global market maker, our team of quantitative researchers models the markets and brings trading strategies to life every day. Specifically, the goal of this team is to leverage and tailor the state-of-the-art machine learning and AI algorithms to modernize the quantitative trading industry. We’re looking for extraordinary and highly motivated researchers who are excited about solving challenging problems and iterating in a fast-paced environment.

As an intern, you’ll get to challenge the impossible in research through an 11 week program that will allow you to collaborate and connect with senior team members. In addition, you’ll get the opportunity to network and socialize with peers throughout the internship.

Our signature internship program takes place June through August. Occasionally, we can be flexible to other times of the year. You will be able to indicate your timing preference in the application.

Your Objectives

• Use statistics, machine learning or AI (e.g. deep learning, NLP) to extract patterns from various kinds of datasets through innovative and rigorous research
• Implement algorithms in high-quality code
• Work with large data sets, including unconventional and unstructured data sources
• Back-test models and document research findings

Your Skills & Talents

• PhD degree in mathematics, statistics, physics, computer science, or another highly quantitative field
• Advanced training and strong research track record in statistics, machine learning, AI, or another highly quantitative field
• Hands on programming experience in scripting (e.g. Python) and/or compiled languages (e.g. C++)
• A background demonstrating strong problem-solving skills
• An ability to communicate advanced concepts in a concise and logical way
• Proficiency in creating and using algorithms to meticulously investigate and work through large data or error-checking problems

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 Securities, please share your details and we will contact you if there is a vacancy available.

In accordance with applicable law, the base salary range for this role is $4,500 to $5,800 per week.

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.

We have roles across 4 locations:

Beijing, China

London, UK

San Francisco, US

Sydney, AU

Vienna, Austria

About the team

Canva Research develops AI technology powering Canva's Creative Operating System, focusing on making design accessible and innovative. We transform research into practical features, expanding rapidly since 2025 with teams in Sydney, London, Vienna, San Francisco, and Beijing. Our work spans generative image, video, audio, agents, evaluations, on-device, and foundation models.

About the role

You'll create systems for evaluating Canva's generative models, translating user judgments into measurable metrics. This hands-on role involves building agentic evaluators, MLLM-as-judge pipelines, and benchmarking infrastructure to ensure model improvements. You'll implement evaluation research into production-ready systems, offering quick feedback to research teams.

What you'll do

• Develop scalable evaluation systems for MLLM-as-a-judge, providing automated feedback
• Experiment with alignment techniques like prompt engineering and in-context learning to enhance model outputs
• Maintain benchmarking pipelines to monitor model performance, avoiding benchmark saturation
• Build infrastructure for reward modeling and preference learning
• Analyze evaluation data to identify model failures and recommend improvements
• Conduct offline evaluations and online monitoring to detect quality drops
• Collaborate with researchers and ML engineers to integrate evaluation systems into development

You're likely a match if you have

• Strong understanding of generative AI models (diffusion models, GANs, transformers) and evaluation systems
• Experience with agentic AI systems and automated evaluation approaches
• Knowledge of inference-time alignment techniques: prompt engineering, RAG, in-context learning
• Experience in reward modeling or alignment methods with human feedback
• Solid machine learning fundamentals, hands-on PyTorch experience, and code optimization skills
• Experience managing large-scale distributed model training across GPUs
• Disciplined coding practices and familiarity with evaluation frameworks

Nice to have

• Linking evaluation metrics to product outcomes
• Knowledge of data visualization tools
• Interest in human-computer interaction, design principles, or AI ethics