NeurIPS 2025 Career Opportunities

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.

Austin, TX

About the Team

Avride builds autonomous solutions from the ground up, using machine learning as the core of our navigation pipeline. We are evolving our stack to support the next generation of self-driving, leveraging efficient CNNs, Transformers, and MLLMs to solve complex perception and planning challenges. Our goal is to apply the right approach to the right problem, laying the groundwork for unified, data-driven approaches.

About the Role

We are seeking a Machine Learning Engineer to build the infrastructure and ML foundations for advanced autonomous behaviors. You won't just optimize isolated models; you will architect scalable training workflows and high-fidelity components.

This is a strategic position: You will contribute to the critical infrastructure that paves the way for future end-to-end capabilities. You will translate relevant research ideas into production-ready improvements when they prove beneficial, helping prepare our stack for a transition toward unified, learned behaviors.

What You'll Do

• Strengthen Core Modules: Design and refine models for perception, prediction, or planning, enhancing reliability to support future holistic learning approaches.
• Architect Data Foundations: Build scalable pipelines for multimodal datasets, ensuring they support both current needs and future large-scale E2E experiments.
• Advance Training Infra: Develop distributed training workflows capable of handling massive model architectures for next-gen foundation models.
• Bridge Research & Production: Analyze research in relevant fields, identifying specific opportunities to introduce these techniques into our production stack.
• System Integration: Collaborate with engineering teams to ensure individual ML improvements translate into better system-level performance.

What You'll Need

• Strong ML Fundamentals: Mastery of processing and fusing self-driving modalities (multiview camera, sparse LiDAR, vector maps).
• Architectural Expertise: Deep knowledge of modern architectures like Transformers and Attention Mechanisms.
• Applied Experience: 5+ years of combined experience in industry or applied research settings, with a strong grasp of the full lifecycle from data to deployment.
• Technical Proficiency: Python, PyTorch/JAX/TensorFlow, and distributed computing (PySpark, Ray).
• Systems Mindset: Ability to visualize how modular systems evolve into end-to-end learners and the practical challenges of deploying them.
• Research Capability: Ability to distill complex papers into practical engineering roadmaps.

Nice to Have

• Advanced degree in CS, ML, Robotics, or related field.
• Familiarity with World Models, Occupancy Networks, or Joint Perception-Planning.
• Experience with inference optimization (Triton, TensorRT) and embedded hardware.

NVIDIA is developing the NVIDIA DRIVE AV Solution (NDAS), powered by the latest advancement in AI and accelerated computing. We are seeking a highly motivated software expert to join our Autonomous Vehicles (AV) Drive-Alpha team in US Santa Clara. You will be driving the engineering execution of feature development or exceeding the meaningful metric requirements, especially for L2++ and L3/L4.

Drive-Alpha consists of proficient domain-experts spanning the full stack of autonomous driving, including perception, fusion, prediction, planning and control, autonomous model, many with proven development experiences for the highly competitive market in key functions like Highway NOA (Navigation on Autopilot) and Urban NOA (Navigation on Autopilot), as well as p2p driving (including parking). This team is responsible for the integration and sign-off of NDAS component teams' merge request/change lists, promotes validated changes to merge into stable branch, analyzes the root-cause of the regression identified, and drive the corrective actions taken by component engineering teams for a productive CI/CD process of SW development. Also, the team members tightly integrate into component teams' development, acting as a dependency resolver for the component teams to deliver cross-function improvement that are most impactful to NDAS product. We nurture teamwork among component teams' engineers and establish positive relationships and communications with partner organizations.

What you’ll be doing: Provide in-depth and insightful technical feedback on the quality of NDAS L2++/L3/L4 SW stack, based on the performance metrics proven through offline-replay and in-car testing. Identify the weak link of the L2++/L3/L4 SW stack and make it strong. Integrate, test and sign-off SW stack's code change and model update, and drive the Root-Cause-Corrective-Action (RCCA) process to continuously improve the quality of NDAS SW. Decompose a complicated cross-function problem into actionable items and coordinate a concerted effort among multiple collaborators. Join force with component team developers, when necessary, provide your domain expert input to a solution for hard problems, and produce production-quality code to component code base.

What we need to see: BS/MS in Electrical Engineering, Computer Science, or related fields or equivalent experience. 5+ years related experience in software development, with hands-on dev experience in AD for automotive. Great coding skills in modern C++ and scripting languages like Python. Deep understanding of L2++/L3/L4 product features in the market. Hands-on experience in debugging AD SW problems. Excellent communication and interpersonal skills with ability to strive in a cross-disciplinary environment.

Ways To Stand Out From The Crowd: Experience of working as a hands-on tech lead for one or more autonomous driving components. Hands-on development experience of an SOP-ed AD and/or ADAS product. Rich experience of in-car testing with great intuition of first-level triaging (from symptom to component). Familiar with CI/CD process, test automation, Jenkins, Log-Sim reply.

NVIDIA has some of the most forward-thinking and hardworking people in the world. If you're creative and autonomous, we want to hear from you! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 136,000 USD - 212,750 USD.

You will also be eligible for equity and benefits.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other char

Location Beijing CHINA

Description

1. Program and Vision： BAAI launches its "Rising Star" Researcher Program, designed to recruit exceptional young scholars who have demonstrated outstanding research potential in AI and related fields. We provide a world-class research platform and robust development support, enabling you to launch your academic career from a high starting point, collaborate with leading scientists, and rapidly grow into a future leader in your field.

2. Qualifications：

3. A record of notable early-career research achievements in AI, Computer Science, Mathematics, or related interdisciplinary fields, demonstrating significant potential.
4. A soon-to-graduate outstanding Ph.D. candidate, a postdoctoral fellow, or an early-career scholar with a pure passion for scientific inquiry and innovation.

5. Strong independent research capabilities and a collaborative spirit.

6. We Offer：

7. A market-competitive salary and benefits package with a clear path for career advancement.
8. Ample start-up research funding and shared access to top-tier computing resources.
9. A clear career development path, with support to grow into an independent researcher.
10. Access to subsidized talent apartments and support for Beijing residency registration.

11. A comprehensive supplementary health insurance plan.

12. How to Apply： Please send your full CV, representative publications, and reference letters or contact information for references to: [recruiting@baai.ac.cn] Use the email subject line: "Researcher Application - [Name] - [Specific Research Focus]"

Bala Cynwyd (Philadelphia Area), Pennsylvania United States & New York, New York United States

Overview

Our Machine Learning PhD Internship is a 10-week immersive experience designed for PhD candidates who are passionate about solving high-impact problems at the intersection of data, algorithms, and markets.

As a Machine Learning Intern at Susquehanna, you’ll work on high-impact projects that closely reflect the challenges and workflows of our full-time research team. You’ll apply your technical expertise in machine learning and data science to real-world financial problems, while developing a deep understanding of how machine learning integrates into Susquehanna’s research and trading systems. You will leverage vast and diverse datasets and apply cutting-edge machine learning at scale to drive data-informed decisions in predictive modeling to strategic execution.

What You Can Expect

 • Conduct research and develop ML models to identify patterns in noisy, non-stationary data

 • Work side-by-side with our Machine Learning team on real, impactful problems in quantitative trading and finance, bridging the gap between cutting-edge ML research and practical implementation

 • Collaborate with researchers, developers, and traders to improve existing models and explore new algorithmic approaches

 • Design and run experiments using the latest ML tools and frameworks

 • One-on-one mentorship from experienced researchers and technologists

 • Participate in a comprehensive education program with deep dives into Susquehanna’s ML, quant, and trading practices

 • Apply rigorous scientific methods to extract signals from complex datasets and shape our understanding of market behavior

 • Explore various aspects of machine learning in quantitative finance from alpha generation and signal processing to model deployment and risk-aware decision making

What we're looking for

 • Currently pursuing a PhD in Computer Science, Machine Learning, Statistics, Physics, Applied Mathematics, or a closely related field

 • Proven experience applying machine learning techniques in a professional or academic setting

 • Strong publication record in top-tier conferences such as NeurIPS, ICML, or ICLR

 • Hands-on experience with machine learning frameworks, including PyTorch and TensorFlow

 • Deep interest in solving complex problems and a drive to innovate in a fast-paced, competitive environment

Why Join Us?

 • Work with a world-class team of researchers and technologists

 • Access to unparalleled financial data and computing resources

 • Opportunity to make a direct impact on trading performance

 • Collaborative, intellectually stimulating environment with global reach

ABOUT THE ROLE

You would be working as part of our Applied Research team, focused on turning pre-trained LLMs into well-aligned and highly capable AI systems for coding and software development. This is a hands-on role where you'll work across a variety of efforts, including: Building data pipelines for coding use cases, researching and implementing fine-tuning algorithms, training reward models, and more. You will have access to thousands of GPUs in this team.

YOUR MISSION

To turn pre-trained LLMs into well-aligned and highly capable AI systems.

RESPONSIBILITIES

• Research and experiment on ways to specialize foundational models to coding use cases
• Build and maintain data and training pipelines
• Keep up with latest research, and be familiar with state of the art in LLMs, alignment, synthetic data generation, code generation
• Design, analyze, and iterate on training/fine-tuning/data generation experiments
• Write high-quality, pragmatic code
• Work as part of a team: plan future steps, discuss, and communicate clearly with your peers

SKILLS & EXPERIENCE

• Experience with Large Language Models (LLM)
• Deep knowledge of Transformers
• Strong deep learning fundamentals
• Good taste in data
• Fine-tuning experience with LLMs
• Extensively used and probed LLMs, familiarity of their capabilities and limitations
• Knowledge of distributed training
• Strong machine learning and engineering background
• Research experience
• Experience in proposing and evaluating novel research ideas
• Familiar with, or contributed to the state of the art in multiple of the following topics: Fine-tuning and alignment of LLMs, synthetic data generation, continual learning, RLHF, code generation
• Is comfortable in a rapidly iterating environment
• Is reasonably opinionated
• Programming experience: Linux, Strong algorithmic skills, Python with PyTorch or Jax. 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 one of our Founding Engineers

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

We are Bagel Labs - a distributed machine learning research lab working toward open-source superintelligence.

Role Overview

We encourage curiosity-driven research and welcome bold, untested concepts.
You will push the boundaries of diffusion models and distributed learning systems, testing hypotheses at the intersection of generative AI and scalable infrastructure.
We love novel, provocative, and untested ideas that challenge conventional paradigms.

Key Responsibilities

• Prototype AI methodologies that can redefine distributed machine learning.
• Pioneer next-generation diffusion architectures including rectified flows, EDM variants, and latent consistency models that scale across distributed infrastructures.
• Develop novel sampling algorithms, guidance mechanisms, and conditioning strategies that unlock new capabilities in controllable generation.
• Partner with cryptographers and economists to embed secure, incentive-aligned protocols into model pipelines.
• Publish papers at top-tier ML venues, organize workshops, and align our roadmap with the latest academic advances.
• Share insights through internal notes, external blog posts, and conference-grade write-ups (for example, blog.bagel.com).
• Contribute to open-source code and stay active in the ML community.

Who You Might Be

You are extremely curious and motivated by discovery.
You actively consume the latest ML research - scanning arXiv, attending conferences, dissecting new open-source releases, and integrating breakthroughs into your own experimentation.
You thrive on first-principles reasoning, see potential in unexplored ideas, and view learning as a perpetual process.

Desired Skills (Flexible)

1. Deep expertise in modern diffusion models, score matching, flow matching, consistency training, and distillation techniques.
2. Hands-on experience with distributed training frameworks such as FairScale, DeepSpeed, Megatron-LM, or custom tensor and pipeline parallelism implementations.
3. Strong mathematical foundation in SDEs, ODEs, optimal transport, and variational inference for designing novel generative objectives.
4. Clear and concise communication skills.
5. Bonus: experience with model quantization (QLoRA, GPTQ), knowledge distillation for diffusion models, or cryptographic techniques for secure distributed training.

What We Offer

• Top-of-market compensation and time to pursue open-ended research
• A deeply technical culture where bold ideas are debated, stress-tested, and built
• Remote flexibility within North American time zones
• Ownership of work shaping decentralized AI
• Paid travel to leading ML conferences worldwide

Apply now - help us build the infrastructure for open-source superintelligence.

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.

Pinely is a privately owned algorithmic trading firm specializing in high-frequency and mid-frequency trading. We’re based in Amsterdam, Cyprus, and Singapore, and we’re experiencing rapid growth. Pinely is a high-frequency algorithmic trading firm based in Amsterdam. We develop robust and adaptive strategies across diverse markets and actively support the Olympiad movement; many team members are award-winning mathematicians, researchers, and engineers.

Researchers work in a fast-paced HFT environment where ideas quickly reach production. They are supported by a strong infrastructure team enabling large-scale experiments and reliable deployment. Our flat structure encourages autonomy, creativity, and direct impact. We value an informal, idea-driven culture.

We are opening a position for a Junior Deep Learning Researcher in our Amsterdam office.

Responsibilities:

• Conduct research in AI, machine learning, and related quantitative fields
• Develop and experiment with modern deep learning architectures
• Analyze large, unstructured, noisy datasets
• Collaborate with developers and researchers on optimizing trading strategies
• Explore new methods and technologies to improve research outcomes

Requirements:

• Publications in ICML, NeurIPS, ICLR, CVPR, ICCV
• Degree in mathematics, physics, computer science, or another quantitative field (or expected within a year)
• Knowledge of ML, probability theory, and statistics
• Strong Python skills
• Some C++ experience
• Practical experience with modern DL architectures
• Background in working with large noisy datasets

What we offer:

• High base salary with substantial biannual bonuses
• Relocation package to Amsterdam with flexible terms
• Flexible workflow and schedule
• Team of top mathematics and programming competition winners
• Cutting-edge hardware, strong engineering support, and fast idea implementation
• Internal training, comprehensive health insurance, sports reimbursement, and biannual corporate events

Location USA, WA, Seattle USA, NY, New York USA, CA, Palo Alto

Description The Sponsored Products and Brands (SPB) team at Amazon Ads is reimagining the advertising landscape through generative AI, revolutionizing how millions of customers discover products and engage with brands on Amazon and beyond. We are at the forefront of redefining advertising experiences—bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle, from ad creation and optimization to performance measurement and customer insights.

We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance advertiser needs, enhance the shopping experience, and strengthen the Amazon marketplace. If you are energized by solving complex challenges and pushing the boundaries of what’s possible with AI, join us in shaping the future of advertising.