NeurIPS 2025 Career Opportunities

Toronto

Description - Bloomberg’s Engineering AI department comprises over 350 AI experts dedicated to building cutting edge, market-leading products. Leveraging advanced technologies including transformers, large language models, and dense vector databases, we are transforming search, discovery, and workflow solutions across the financial industry. As we expand our group, we are seeking highly skilled Machine Learning (ML) and Software Engineers who will contribute innovative solutions to AI-driven customer-facing products.

At Bloomberg, we foster transparency and efficiency in global financial markets. Our technology powers search and discoverability, bringing actionable insights from news, research, financial data, and analytics covering more than 35 million financial instruments. Since 2009, Bloomberg has been at the forefront of applying artificial intelligence to organize the vast volumes of structured and unstructured data that inform critical financial decisions, uncover market signals, and deliver clarity precisely when our clients need it most.

In Toronto, our Machine Learning Engineers are central to advancing Bloomberg’s efforts in financial query understanding and code generation. They bridge the gap between pioneering research and practical solutions, developing models to address complex financial queries and automate code writing. They engineer state-of-the-art code generation systems and apply LLM techniques like CoT, SFT or RLHF to drive iterative model refinement.

Join the AI Group as a Senior ML Research Engineer and you will have the opportunity to: -Collaborate with colleagues on production systems and write, test, and maintain production quality code -Design, train, experiment, and evaluate ML models, algorithms and solutions -Demonstrate technical leadership by owning cross-team projects -Stay current with the latest research in ML and incorporate new findings into our models and methodologies -Represent Bloomberg at scientific and industry conference and in open-source communities -Publish product and research findings in documentation, whitepapers or publications to leading academic venues

We are looking for Senior ML Research Engineers with the following experience: -Practical experience with solving Machine Learning problems and techniques -Ph.D. in ML, Statistics or a relevant field or MSc in CS, ML, Math, Statistics, Engineering, or related fields and 2+ years of relevant work experience -Experience with machine learning and deep learning frameworks -Proficiency in software engineering -An understanding of Computer Science fundamentals such as data structures and algorithms and a data oriented approach to problem-solving -Excellent communication skills and the ability to collaborate with engineering peers as well as non-engineering stakeholders. -A track record of authoring publications in top conferences and journals is a strong plus

ABOUT POOLSIDE

In this decade, the world will create Artificial General Intelligence. There will only be a small number of companies who will achieve this. Their ability to stack advantages and pull ahead will define the winners. These companies will move faster than anyone else. They will attract the world's most capable talent. They will be on the forefront of applied research, engineering, infrastructure and deployment at scale. They will continue to scale their training to larger & more capable models. They will be given the right to raise large amounts of capital along their journey to enable this. They will create powerful economic engines. They will obsess over the success of their users and customers.

poolside exists to be this company - to build a world where AI will be the engine behind economically valuable work and scientific progress.

ABOUT OUR TEAM

We are a remote-first team that sits across Europe and North America and comes together once a month in-person for 3 days and for longer offsites twice a year.

Our R&D and production teams are a combination of more research and more engineering-oriented profiles, however, everyone deeply cares about the quality of the systems we build and has a strong underlying knowledge of software development. We believe that good engineering leads to faster development iterations, which allows us to compound our efforts.

ABOUT THE ROLE

You would be working on our pre-training team focused on building out our distributed training of Large Language Models and major architecture changes. This is a hands-on role where you'll be both programming and implementing LLM architectures (dense & sparse) and distributed training code all the way from data to tensor parallelism, while researching potential optimizations (from basic operations to communication) and new architectures & distributed training strategies. You will have access to thousands of GPUs in this team.

YOUR MISSION

To train the best foundational models for source code generation in the world in minimum time and with maximum hardware utilization.

RESPONSIBILITIES

• Follow the latest research on LLMs and source code generation. Propose and evaluate innovations, both in the quality and the efficiency of the training
• Do LLM-Ops: babysitting and analyzing the experiments, iterating
• Write high-quality Python, Cython, C/C++, Triton, CUDA code
• Work in the team: plan future steps, discuss, and always stay in touch

SKILLS & EXPERIENCE

• Experience with Large Language Models (LLM)
• Deep knowledge of Transformers is a must
• Knowledge/Experience with cutting-edge training tricks
• Knowledge/Experience of distributed training
• Trained LLMs from scratch
• Coded LLMs from scratch
• Knowledge of deep learning fundamentals
• Strong machine learning and engineering background
• Research experience
• Author of scientific papers on any of the topics: applied deep learning, LLMs, source code generation, etc. - is a nice to have
• Can freely discuss the latest papers and descend to fine details
• Is reasonably opinionated
• Programming experience: Linux, Strong algorithmic skills, Python with PyTorch or Jax, C/C++, CUDA, Triton
• Use modern tools and are always looking to improve
• Strong critical thinking and ability to question code quality policies when applicable
• Prior experience in non-ML programming, especially not in Python - is a nice to have

PROCESS

• Intro call with one of our Founding Engineers
• Technical Interview(s) with one of our Founding Engineers
• Team fit call with the People team
• Final interview with Eiso, our CTO & Co-Founder

BENEFITS

• Fully remote work & flexible hours
• 37 days/year of vacation & holidays
• Health insurance allowance for you and dependents
• Company-provided equipment
• Wellbeing, always-be-learning and home office allowances
• Frequent team get togethers
• Great diverse & inclusive people-first culture

The Department of Materials Science and Engineering (DMSE) together with the Schwarzman College of Computing (SCC) at Massachusetts Institute of Technology (MIT) in Cambridge, MA, seeks candidates at the level of tenure-track Assistant Professor to begin July 1, 2026 or on a mutually agreed date thereafter.

Materials engineering has always benefitted from theoretical and computational approaches to unveil relationships between structure, properties, processing, and performance. Recent advances in computing, including but not limited to artificial intelligence, are poised to dramatically advance the understanding and design of complex matter. DMSE and SCC jointly seek candidates with experience and interest in combining fundamental scientific principles with algorithmic innovations to empower discovery, understanding, and synthesis of materials with applications across critical societal domains --- healthcare, manufacturing, energy, sustainability, climate, and next-generation computing. This search encompasses all materials classes and scales, and is open to candidates with industry and start-up experience. Candidates are expected to develop research programs that target innovation in computational approaches well-suited to materials science and engineering research.

The successful candidate will have a shared appointment in both the Department of Materials Science and Engineering and SCC in either the Department of Electrical Engineering and Computer Science (EECS) or the Institute for Data, Systems, and Society (IDSS), depending on best fit.

Faculty duties include teaching at the undergraduate and graduate levels, advising students, conducting original scholarly research, and developing course materials at the graduate and undergraduate levels. Candidates are expected to teach in both the Department of Materials Science and Engineering and in the educational programs of SCC. The normal teaching load is two subjects per year.

Candidates should hold a Ph.D. in Materials Science and Engineering, Computer Science, Physics, Chemical Engineering, Chemistry, Applied Mathematics, or a related field. A PhD is required by the start of employment. The pay range for a 9-month academic appointment at the entry-level Assistant Professor rank (excluding summer salary): $140,000 - $150,000. The pay offered to a selected candidate during hiring will be based on factors such as (but not limited to) the scope and responsibilities of the position, the individual's work experience and education/training, internal peer equity, and applicable legal requirements. These factors impact where an individual's pay falls within a range. Employment is contingent upon the completion of a satisfactory background check, including verifying any finding of misconduct (or pending investigation) from prior employers.

Applications should include: (a) curriculum vitae, (b) research statement, (c) a teaching and mentoring plan. Each candidate should also include the names and contact information of 3 reference letter writers, who should upload their letters of recommendation by November 30, 2025.

Please submit online applications to https://faculty-searches.mit.edu/dmse_scc/register.tcl. To receive full consideration, completed applications must be submitted by November 30, 2025.

MIT is an equal opportunity employer. We value diversity and strongly encourage applications from individuals from all identities and backgrounds. All qualified applicants will receive equitable consideration for employment based on their experience and qualifications and will not be discriminated against on the basis of race, color, sex, sexual orientation, gender identity, pregnancy, religion, disability, age, genetic information, veteran status, or national or ethnic origin. See MIT's full policy on nondiscrimination. Know your rights.

Global: United States, Europe and Asia

At Citadel, our mission is to be the most successful investment team in the world. Quantitative Researchers play a key role in this mission by developing next-generation models and trading approaches for a range of investment strategies. You’ll get to challenge the impossible in quantitative research by applying sophisticated and complex statistical techniques to financial markets, some of the most complex data sets in the world. 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.

About Handshake AI Handshake is building the career network for the AI economy. Our three-sided marketplace connects 18 million students and alumni, 1,500+ academic institutions across the U.S. and Europe, and 1 million employers to power how the next generation explores careers, builds skills, and gets hired. Handshake AI is a human data labeling business that leverages the scale of the largest early career network. We work directly with the world’s leading AI research labs to build a new generation of human data products. From PhDs in physics to undergrads fluent in LLMs, Handshake AI is the trusted partner for domain-specific data and evaluation at scale. This is a unique opportunity to join a fast-growing team shaping the future of AI through better data, better tools, and better systems—for experts, by experts.

Now’s a great time to join Handshake. Here’s why: Leading the AI Career Revolution: Be part of the team redefining work in the AI economy for millions worldwide. Proven Market Demand: Deep employer partnerships across Fortune 500s and the world’s leading AI research labs. World-Class Team: Leadership from Scale AI, Meta, xAI, Notion, Coinbase, and Palantir, just to name a few. Capitalized & Scaling: $3.5B valuation from top investors including Kleiner Perkins, True Ventures, Notable Capital, and more.

About the Role As a Staff Research Scientist, you will drive frontier research on how we define intelligence of frontier models, i.e. develop benchmarks and measurements that help the research community to understand how large language models (LLMs) understand, reason, and interact with human knowledge. You will: Lead teams of researchers to produce original research in LLM evaluation methodologies, interpretability, and human-AI knowledge alignment. Develop novel frameworks and assessment techniques that reveal deep insights into model capabilities, limitations, and emergent behaviors. Collaborate with engineers to translate research breakthroughs into scalable benchmarks, evaluation systems, and standards. Pioneer new approaches to measuring reasoning, alignment, and trustworthiness in frontier AI systems. Author high-quality code to enable large-scale experimentation, reproducible evaluation, and knowledge assessment workflows. Publish in top-tier conferences and journals, establishing new directions in the science of AI evaluation. Work cross-functionally with leadership, engineers, and external partners to set industry standards for responsible AI evaluation and alignment.

Desired Capabilities PhD or equivalent research experience in machine learning, computer science, cognitive science, or related fields with focus on AI evaluation, interpretability, or model understanding. 6+ years of academic or industry experience post-doc in a research-first environment Strong background in LLM research, evaluation methodologies, and/or foundational AI assessment techniques. Proven ability to independently design, lead, and execute evaluation research programs with novel data types end-to-end. Deep proficiency in Python and PyTorch for large-scale model analysis, benchmarking, and evaluation. Experience building or leading novel benchmark development, systematic model assessment, or interpretability studies. Strong publication record in post-training, evaluation, or interpretability that demonstrates field-defining contributions. Ability to clearly communicate complex insights and influence both technical and non-technical stakeholders.

Extra Credit Experience with RLHF, agent modeling, or AI alignment research. Familiarity with data-centric AI approaches, synthetic data generation, or human-in-the-loop systems. Understanding of challenges in scaling foundation models (training stability, safety, inference efficiency). Contributions to open-source libraries or research tooling. Interest in the societal impact, deployment ethics, and governance of frontier AI systems.

Pittsburgh, PA

US Citizenship required (green card or visa does not suffice)

Work with the world leaders in computational game theory on software products for real problems of importance! Positions are available for working on the nation's best fighter pilot AI, on wargaming, on command and control, on missile defense, and on optimizing the world's nuclear stability. Work on the most important problems in the world! The work leverages the leading course-of-action generation and execution AI system, which we have developed.

Required qualifications:

• Degree as indicated in the position announcement roles

• Strong software development skills

• Excitement to change the world with AI products

• Desire to work with the world's leading experts in a fast-moving environment

• US citizenship (green card or visa does not suffice), and eligibility to obtain Top Secret clearance

Why apply?

• The company is the world leader in computational game theory AI

• Unique opportunity to apply game theory-based software products to the real world

• Ability to work directly with world-leading AI experts

• The company is already profitable

• CMU startup in close proximity to CMU

• Competitive compensation, including equity in a fast-moving, profitable startup

• The company has a no-jerks policy

** Our Founder, President, and CEO, Dr. Tuomas Sandholm, will be available to conduct interviews personally at NeurIPS between December 4th and 7th, 2025, and additional positions will be available thereafter as well. **

Amsterdam

If you enjoy mathematical challenges and writing computer programs, you could be instrumental in the success of Optiver’s dynamic trading floor as our next Graduate Quantitative Researcher. With your statistics knowledge and top-tier analytical abilities, you’ll create the insights that drive our trading strategies. Get ready to collaborate with world-class Traders and Software Engineers from more than 50 countries to improve financial markets across the globe. This is your chance to get involved and see how valuable research and data are to the future of electronic trading.

WHAT YOU’LL DO: Quantitative Research acts as the foundation upon which Optiver’s trading activities are built. Our research teams – experts in a variety of STEM subjects – utilise a scientific approach to research and design our world-class trading algorithms. This means applying and developing state-of-the-art stochastic models to price options and predict market volatility, as well as utilising Monte Carlo methods. It also means developing statistical arbitrage strategies by working with petabytes of low latency, high-frequency market data sets, an extensive high-powered computing back-testing framework and much more. Optiver Researchers believe in academic discourse, and therefore invite their teammates and Traders to challenge each hypothesis. Constant testing, analysis, refinement and innovation ensures our quantitative models remain at the cutting-edge of constantly evolving capital markets – you will play a key role in keeping us there.

WHO YOU ARE: We’re looking for aspiring Quantitative Researchers who are versatile and creative in innovating and suggesting new solutions. In return, we’ll give you the freedom to pursue your ideas and implement them right into our production systems. In terms of skills and qualifications, we’re looking for: • An academic degree in Engineering, Physics, Maths, Econometrics, Computer science or equivalent, with outstanding academic achievements • Programming experience in any language (preferably Python, but C, C++, Basic, JAVA, etc. are also a plus) • Ability to apply concepts of probability, calculus and linear algebra • Competitive attitude and eagerness to constantly improve • Ability to learn quickly • Excellent verbal and written English language skills

WHAT YOU’LL GET: You’ll join a culture of collaboration and excellence, where you’ll be surrounded by curious thinkers and creative problem solvers. Motivated by a passion for continuous improvement, you’ll thrive in a supportive, high-performing environment alongside talent colleagues, working collaboratively to tackle the toughest problems in the financial markets. In addition, you’ll receive: • A performance-based bonus structure, enabling all of our employees to benefit from our global profit pool • The opportunity to work alongside best-in-class professionals from over 50 countries • 25 paid vacation days in your first year, increasing to 30 from your second year onwards • Training opportunities, discounts on health insurance, and fully paid first-class commuting expenses • Extensive office perks, including breakfast, lunch and dinner, world-class barista, in-house physio and chair massages, organised sports and leisure activities, and Friday afternoon drinks • Training and continuous learning opportunities, including access to conferences and tech events • Competitive relocation packages and visa sponsorship where necessary for expats

HOW TO APPLY: Are you interested in furthering your career on one of the most dynamic and exciting trading floors in Europe? Apply directly via the form below for the position of Graduate Quantitative Researcher. Please provide us with a CV in English. Unfortunately we cannot accept applications via email for data protection reasons.

London

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)

D. E. Shaw Research welcomes applications for our summer 2026 internship program from undergraduate and graduate students who are pursuing degrees in scientific and technical disciplines, including machine learning and other areas of computer science, electrical and computer engineering, applied mathematics, physics, chemistry, and chemical or biomolecular engineering. Programming experience in Python or C/C++ is required, and experience with Linux systems, high-performance computing, PyTorch, and/or CUDA would be a plus.

Interns spend 12 to 15 weeks (with flexible start and end dates) working closely with members of our computer science and machine learning team. They are fully immersed in a challenging project involving, for example, applying ML (including large language models) to drug discovery, building and deploying AI-powered systems, developing scientific software, or working on various other engineering and research problems to advance our drug discovery goals. At the end of the summer, interns have the opportunity to give group-wide presentations of their work.

Throughout the summer, interns are invited to attend scientific and technical seminars and workshops, and to join in lively social programming in and around New York City.

For more information, visit www.DEShawResearch.com.

Please apply using the link below:

https://apply.deshawresearch.com/careers/Register?pipelineId=657&source=NeurIPS_1

The expected monthly salary for our internships is USD 11,700 - USD 20,600. The applicable monthly salary paid to a successful applicant will be determined based on multiple factors, including the nature and extent of prior experience and educational background. We also provide our interns with a generous housing allowance.

D. E. Shaw Research, LLC is an equal opportunity employer.

San Francisco / New York / Toronto

About Ideogram

Ideogram’s mission is to make world-class design accessible to everyone, multiplying human creativity. We build proprietary generative media models and AI native creative workflows, tackling unsolved challenges in graphic design. Our team includes builders with a track record of technology breakthroughs including early research in Diffusion Models, Google’s Imagen, and Imagen Video. We care about design, taste, and craft as much as research and engineering – shipping experiences that creatives actually love.

We’ve raised nearly $100M, led by Andreessen Horowitz and Index Ventures. Headquartered in Toronto with a growing team in NYC, we're scaling fast, aiming to triple over the next year. We're a flat team with a culture of high ownership, collaboration, and mentorship.

Explore Ideogram 3.0, Canvas, and Character blog posts, and try Ideogram at ideogram.ai.

The Opportunity

In this role, you will develop the post-training pipeline for our text-to-image foundation models end to end, from data strategy to deployment, advancing techniques such as RLHF, RLAIF, and work on personalization/customization. You will contribute to post-training research that drives measurable gains, and implement and maintain high-throughput fine-tune/eval pipelines. You'll work with a creative and ambitious team of engineers and researchers who are building the future of the creative economy.

What We're Looking For

• 5+ years of experience in developing machine learning models in JAX, PyTorch, or TensorFlow.

• Experience in implementing Machine Learning foundations (e.g., Transformer, VAE, Denoising Diffusion models) from scratch.

• Track record in machine learning innovation and familiarity with Deep Learning and advanced Machine Learning.

• End-to-end understanding of generative media applications and excitement for pushing the state-of-the-art in generative AI.

• Ability to debug machine learning models to iteratively improve model quality and performance.

• Nice to have: Familiarity with Kubernetes and docker.

• Optional: Experience in low-level machine learning optimization, e.g., writing CUDA kernel code.

Our Culture

We’re a team of exceptionally talented, curious builders who love solving tough problems and turning bold ideas into reality. We move fast, collaborate deeply, and operate without unnecessary hierarchy, because we believe the best ideas can come from anyone.

Everyone at Ideogram rolls up their sleeves to make our products and our customers successful. We thrive on curiosity, creativity, and shared ownership. We believe that small, dedicated teams working together with trust and purpose can move faster, think bigger, and create amazing things.

Ideogram is committed to welcoming everyone — regardless of gender identity, orientation, or expression. Our mission is to create belonging and remove barriers so everyone can create boldly.

What We Offer

💸Competitive compensation and equity designed to recognize the value and impact of your contributions to Ideogram’s success. 🌴 4 weeks of vacation to recharge and explore. 🩺 Comprehensive health, vision, and dental coverage starting on day one. 💰 RRSP/401(k) with employer match up to 4% to invest in your future from the moment you join. 💻 Top-of-the-line tools and tech to fuel your creativity and productivity. 🔍 Autonomy to explore and experiment — whether you’re testing new ideas, running large-scale experiments, or diving into research, you’ll have access to compute/resources you need when there’s a clear business or creative use case. We encourage curiosity and bold thinking. 🌱 A culture of learning and growth, where curiosity is encouraged and mentorship is part of the journey. 🏡 Fully remote flexibility across North America, with regular in-person team meetups and collaboration opportunities.