NeurIPS 2025 Career Opportunities

Postdoctoral Scholar: Computational Medicine Research Group, University of California, Irvine (NIH Funded)

The Computational Medicine Research Group directed by Prof. Pratik Shah at the University of California, Irvine, invites applications for an NIH-funded Postdoctoral Scholar position. We seek outstanding Ph.D. candidates in computer science, biomedical informatics, statistics, or related fields to develop novel deep learning and AI technologies for digital biopsies from medical images and clinical decision-making from non-imaging datasets. Research areas include:

• Generative AI for Medical Imaging & Digital Biopsies: Developing and interpreting DNNs for automated tissue analyses using high-parameter images (pathology, MRI, CT, RGB) and validating these models in collaboration with hospitals nationwide.

• Generative & Predictive AI for Clinical Decision Support: Developing biologically informed statistical methods and uncertainty estimation generative models for explainable clinical decision-making from EMRs and genetic data.

Responsibilities include data preprocessing, training and real-world validation of generative deep learning models (GANs, Diffusion models, Transformers), developing novel statistical models, and publishing research in leading journals and conferences. Comprehensive training in publication, fellowship and grant writing, and career development for roles in academia, industry, or government will be provided.More information about the lab can be found at https://faculty.sites.uci.edu/pratikshahlab/

Various locations available

Adobe Firefly is redefining creativity by bringing the power of generative AI to millions of users worldwide. The Evaluation Systems team builds the ML foundation that ensures Firefly’s creations are safe, high-quality, and aligned with evolving human needs.

We are seeking a Machine Learning Engineer with a passion for vision and multimodal understanding to help us advance the frontier of evaluating generative content. You will design, train, and deploy models that assess the quality, aesthetics, and safety of images and videos generated by foundation models. Your work will directly shape how creators engage with AI responsibly and at scale.

This is an opportunity to work at the intersection of state-of-the-art research, large-scale data, and production systems, in a team that values human-in-the-loop learning and model alignment as core principles.

What You’ll Do - Model Development: Build and fine-tune models (e.g., ViTs, VLMs, multimodal encoders) to evaluate generative content across quality, safety, and user alignment dimensions. - Human-in-the-Loop Training: Leverage large-scale, noisy human feedback data to train robust evaluation and reward models. - Production Deployment: Ship models as real-time services that gate content and provide quality guardrails, continuously monitoring and improving their performance. - Collaboration: Partner with product, research, and engineering teams to integrate evaluation signals into Firefly products and new creative experiences. - Exploration: Stay on top of the latest ML research (e.g., diffusion models, alignment methods, multimodal evaluation) and translate advances into practical solutions.

What You Need to Succeed - MS or PhD in Computer Science, Statistics, Electrical Engineering, Applied Math, Operations Research, Econometrics or equivalent experience required - Strong understanding of machine learning and deep learning concepts, especially in vision and multimodal domains. - Experience with model training, finetuning, and evaluation. Proficiency in Python and familiarity with frameworks like PyTorch. Familiarity with large-scale data pipelines and distributed training is a plus. - Ability to translate research concepts into scalable, production-ready systems. Prior exposure to vision-language models or human feedback training is a plus. - Strong analytical and quantitative problem-solving ability. - Excellent communication, relationship skills and a strong team player.

San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US; Remote, US

About Pinterest:

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible.

With more than 600 million users around the world and 400 billion ideas saved, Pinterest Machine Learning engineers build personalized experiences to help Pinners create a life they love. With just over 4,000 global employees, our teams are small, mighty, and still growing. At Pinterest, you’ll experience hands-on access to an incredible vault of data and contribute large-scale recommendation systems in ways you won’t find anywhere else.

We are seeking talented Staff Machine Learning Engineers for multiple openings across our Core Engineering organization, including teams such as Search, Notifications, and Content & User Engineering. In these roles, you will drive the development of state-of-the-art applied machine learning systems that power core Pinterest experiences.

What you’ll do:

• Design features and build large-scale machine learning models to improve user ads action prediction with low latency
• Develop new techniques for inferring user interests from online and offline activity
• Mine text, visual, and user signals to better understand user intention
• Work with product and sales teams to design and implement new ad products

What we’re looking for:

• Degree in computer science, machine learning, statistics, or related field
• 6+ years of industry experience building production machine learning systems at scale, data mining, search, recommendations, and/or natural language processing
• 2+ years of experience leading projects/teams
• Strong mathematical skills with knowledge of statistical methods
• Cross-functional collaborator and strong communicator

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.

Amsterdam

As a Quantitative Research Intern, you will get to work with our research team of mathematicians, scientists and technologists, to help develop the models that drive Optiver’s trading. You will tackle a practical research challenge that has impact and directly influences Optiver’s trading decisions. In our business, where the markets are always evolving, you will use your skills to predict its movements.

What you’ll do Led by our in-house education team, you will delve into trading fundamentals and engage in research projects that make a real difference. You will enhance your understanding of trading principles and gain hands-on experience by trading on live markets using real Optiver technology, with simulated capital. For the ten-week internship, you will get support from experienced researchers during your research project work, providing you exposure to a variety of areas, including: • Deep dive into trading and research fundamentals, from theoretical concepts to financial markets, strategies and cutting-edge technology • Using statistical models and machine learning to develop trading algorithms • Leveraging big data technologies to analyse trading strategies and financial instruments to identify trading opportunities • Combining quantitative analysis and high-performance implementation to ensure efficiency and accuracy of your models • Gain exposure to various trading and research desks and experience the financial markets first-hand Based on your performance during the internship, you could receive an offer to join our firm full-time after your studies.

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 talented colleagues, working collectively to tackle the toughest problems in the financial markets. In addition, you’ll receive: • A highly competitive internship compensation package • Optiver-covered flights and accommodation in the city centre for the duration of the internship • Extensive office perks, including breakfast and lunch, world-class barista coffee and Friday afternoon drinks • The opportunity to participate in sports and leisure activities, along with social events exclusively organised for your intern cohort

Who you are • Penultimate year student in Mathematics, Statistics, Computer Science, Physics or a related STEM field, with the ability to work full time upon graduation in 2027 • Solid foundation in mathematics, probability and statistics • Excellent research, analytical and modelling skills • Independent research experience • Proficiency in any programming language • Knowledge of machine learning, time-series analysis and pattern recognition is a plus • Strong interest in working in a fast-paced, collaborative environment • Fluent in English with strong written and verbal communication skills

Diversity statement Optiver is committed to diversity and inclusion. We encourage applications from candidates from any and all backgrounds, and we welcome requests for reasonable adjustments during the process to ensure that you can best demonstrate your abilities. Please let us know if you would like to request any reasonable adjustments by contacting the Recruitment team via the contact form, selecting “Reasonable Adjustments” as the subject of your inquiry.

For answers to some of our most frequently asked questions, refer to our Campus FAQs.

For applicants based in India, our entry route is via the placement office internship hiring season (July/August).

*We accept one application per role per year. If you have previously applied to this position during this season and have been unsuccessful, you can reapply once the next recruitment season begins in 2026.

Successful hires will expand the group's efforts applying machine learning to drug discovery, biomolecular simulation, and biophysics. Areas of focus include generative models to help identify novel molecules for drug discovery targets, predict PK and ADME properties of small molecules, develop more accurate approaches for molecular simulations, and understand disease mechanisms. Ideal candidates will have strong Python programming skills. Relevant areas of experience might include deep learning techniques, systems software, high performance computation, numerical algorithms, data science, cheminformatics, medicinal chemistry, structural biology, molecular physics, and/or quantum chemistry, but specific knowledge of any of these areas is less critical than intellectual curiosity, versatility, and a track record of achievement and innovation in the field of machine learning. For more information, visit www.DEShawResearch.com.

Please apply using this link: https://apply.deshawresearch.com/careers/Register?pipelineId=597&source=NeurIPS_1

The expected annual base salary for this position is USD 300,000 - USD 800,000. Our compensation package also includes variable compensation in the form of sign-on and year-end bonuses, and generous benefits, including relocation and immigration assistance. The applicable annual base 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 follow a hybrid work schedule, in which employees work from the office on Tuesday through Thursday, and have the option of working from home on Monday and Friday.

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

Redwood City, CA

Biohub is leading the new era of AI-powered biology to cure or prevent disease through its 501c3 medical research organization, with the support of the Chan Zuckerberg Initiative.

Biohub supports the science and technology that will make it possible to help scientists cure, prevent, or manage all diseases by the end of this century. While this may seem like an audacious goal, in the last 100 years, biomedical science has made tremendous strides in understanding biological systems, advancing human health, and treating diseour organization and research partners all for the purpose of contributing to greater understanding of human cell function.

You will have the opportunity to work closely with teams of scientists, computational biologists, engineers and to collaborate with our grantees, with our institutes, and other external labs and organizations. Your work will inspire and enhance the production and analysis of datasets by teams and collaborators. Scientific focus areas could include single cell biology, imaging, genomics, and proteomics.

What You'll Do Working with the AI Research Scientists, iterate on, optimize, deploy, and maintain innovative machine learning models, systems, and software tools that enable the analysis and interpretation of AI models for Biology Work with cross-functional team members to quickly iterate on system performance to meet/stay ahead of users’ needs - e.g. we get feedback that the model doesn't scale to X million so working with our user researcher/scientist/product team to iterate on the solution. Partner with research scientists to build robust data loader pipelines for scalable distributed training and evaluation. Serve as an interface to product and engineering teams to understand how models may need to evolve to support multiple use cases. Develop model evaluation and interpretability frameworks that help biologists understand which data features drive model predictions Build reusable engineering utilities that can unlock experimentation velocity across research initiatives in the organization Optimize model architectures to enhance performance, fine-tune accuracy, and efficiently manage infrastructure resources

What You'll Bring Experience in working with a highly interactive and cross-functional collaborative environment with a diverse team of colleagues and partners solving complex problems through applied deep learning. A track record and expertise in developing deep learning models on large-scale GPU clusters, using techniques of distributing training such as DDP, FSDP, Model parallelism, low-precision training, profiling and optimizing AI/ML code, fine tuning models. Expertise in leading end-to-end experimentation pipelines for training and evaluating deep learning models, with particular focus on experiment tracking and reproducibility. A good working knowledge of Python-based ML libraries and frameworks such as PyTorch, JAX, TensorFlow, NumPy, Pandas, and Scikit-learn. Experience in using modern frameworks for distributed computing and infrastructure management, particularly as related to ML models such as PyTorch Lightning, Deepspeed, TransformerEngine, RayScale etc. Ability to effectively balance exploratory research with robust engineering practices. A good working knowledge of general software engineering practices in a production environment. The ability to work independently and as part of a team, and have excellent communication and interpersonal skills. Have a Masters in computer science with a focus on machine learning & data analytics, or equivalent industry experience and at least 6-8 years of experience developing and applying machine learning methods.

Open positions in Mathematical, Statistical and Computational aspects of Artificial Intelligence and Machine Learning.

The Faculty of Mathematics and the Institute for Mathematical and Computational Engineering (IMC) from the Pontificia Universidad Católica de Chile (UC) are offering up to two full-time professor positions at the assistant or associate level. The successful candidate will have a joint appointment between the Faculty of Mathematics and the IMC. We welcome applications from highly qualified candidates with a track record of excellent research and a solid commitment to excellence in teaching and supervision. This position is intended for those researchers interested in advancing the mathematical, statistical and computational aspects of modern artificial intelligence, machine learning and its applications. Candidates conducting research in areas including pure and computational mathematics, mathematical modeling, statistics, optimization, and theoretical computer science, that exhibit a convincing path towards AI research are also encouraged to apply.

The successful candidate will contribute to teaching courses at the Institute for Mathematical and Computational Engineering and the Faculty of Mathematics. The typical teaching load is three semester-long courses a year, at the graduate or undergraduate level, split between the two academic units. Command of Spanish is not required for applying; however, the selected candidate is expected to start teaching in Spanish in at most two years. Applicants who do not speak Spanish will receive support from the University to learn the language adequately. To support the initial academic career stages, the incorporation of Assistant Professors can benefit from an initial reduction of the teaching load and a starting fund. The applicant position and salary will be adjusted, according to the policies and regulations at UC, on the applicant starting date. These will be determined according to the previous experience of the candidate. UC is committed to the values of diversity regarding origin, gender, and ethnicity to build a more diverse and inclusive community. For the current call, women are especially encouraged to apply.

APPLICATIONS

Applications are received through MATHJOBS platform at the link https://www.mathjobs.org/jobs/list/27675 or can be sent by email at vacancysearch.imc@uc.cl. The documentation submitted must include at least:

• Cover letter.
• Curriculum vitae.
• Publication list.
• Research statement.
• Teaching statement.
• Three (3) letters of recommendation, with ideally at least one of them referring to the teaching experience.

The deadline for the applications is December 16th, 2025. Queries can be made by email to Prof. Cristóbal Rojas at vacancysearch.imc@uc.cl.

VISA

If selected for a position, a foreigner without permanent residency in Chile or applying from abroad will need to request a visa in the consulate of their country of origin to be incorporated as academic staff at UC Chile.

Further information about IMC and the Faculty of Mathematics can be found at https://imc.uc.cl and www.mat.uc.cl.

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.

Location Beijing CHINA

Description

1. About Us: The Beijing Academy of Artificial Intelligence (BAAI), established in November 2018, is a non-profit research institute dedicated to becoming a global leader in AI innovation. We strive to create the world's premier ecosystem for academic and technological advancement, tackling the most fundamental and critical challenges in the field. BAAI aims to be the source of academic thought, foundational theory, top talent, industrial innovation, and policy for artificial intelligence, fostering sustainable development for humanity, our environment, and intelligence itself.

2. Open Research Tracks:

3. Multimodal Large Model Researcher:
4. Focus on exploring next-generation vision and multimodal foundation models (e.g., the Emu series). You will research novel algorithms and data systems, dedicated to solving core challenges in multimodal perception and generation.
5. Embodied AI Researcher:
6. Research and develop Vision-Language-Action (VLA) models and hierarchical architectures. You will work on the full pipeline from simulation and synthetic data to real-world deployment, aiming to build powerful embodied AI base models with exceptional generalization capabilities, enabling robots to understand and execute long-horizon, complex instructions in novel environments.
7. Researcher (AI for Science):

8. Leverage AI methods to solve cutting-edge problems in life sciences. You will design and develop new models and algorithms, participate in world-class scientific collaborations, and pioneer breakthroughs from 0 to 1 in the field of biological computation.

9. We Are Looking For:

10. A Ph.D. or outstanding Master's degree in Computer Science, Artificial Intelligence, Electronic Engineering, Life Sciences, or related fields.
11. Solid foundation and research experience in at least one of the following areas:
12. Multimodal: Deep understanding of mainstream large models and strong algorithm implementation skills.
13. Embodied AI: Familiarity with VLA models, mainstream simulators, and experience with pre-training, fine-tuning, or real-world deployment.
14. AI for Science: Strong mathematical foundation and machine learning knowledge, with a passion for solving life science problems.

15. Proven Research Excellence: A track record of publications at top-tier conferences such as NeurIPS, ICML, ICLR, CVPR, ICRA, RSS, or experience in leading high-impact open-source projects.

16. What We Offer:

17. Work on the Cutting Edge: Confront the field's most challenging problems. Your work will directly contribute to breakthroughs in next-generation AI.
18. Mentorship & Collaboration: Work alongside and receive guidance from renowned scientists and senior researchers within a world-class team.
19. Freedom & Resources: Enjoy an atmosphere of academic freedom and access to abundant, state-of-the-art computational resources to support your ambitious research ideas.
20. Global Impact: Publish your research at leading global conferences and see it potentially transformed into projects that advance industry and science.

How to Apply: Please send your CV, representative papers, or project portfolio to: [Zstar@baai.ac.cn] Use the email subject line: "NeurIPS - Z star - [Your Desired Track] - [Your Name]" (e.g., NeurIPS - Z star- Multimodal Large Model - Xiao Zhi)