NeurIPS 2025 Career Opportunities

Pittsburgh

We are seeking creative and energetic candidates with strong experience in multimodal machine learning and human behavior analysis and modeling for a one-year Postdoctoral position. Using recent progress in machine learning and artificial intelligence, the successful candidate will have primary responsibility to develop, implement, and test multimodal machine learning algorithms to analyze and recognize multimodal human behavior in real world settings (e.g., Affective Computing, AI for Healthcare: pain measurement, monitoring mental health disorders). The successful candidate will have primary responsibility for all facets of the project, including papers writing and students mentoring. Preference will be given to candidates with a proven track record, demonstrating relevant skills and extensive experience in multimodal machine learning and human behavior analysis (e.g., facial and gesture analysis and acoustic signal processing). The candidate should be enthusiastic about collaborating with partners from multiple disciplines and institutions.

San Jose, CA, USA

Adobe is looking for a Senior Applied Researcher to use Generative AI and Machine Learning techniques to help Adobe better understand, lead, and optimize the experience of Adobe’s Digital Experience customers. Partnering with Adobe Research and other business units, the candidate will be building products that transform the way companies approach audience creation, journey optimization, and personalization at scale. You will join a diverse, lively group of engineers and scientists long established in the ML space. The work is dynamic, fast-paced, creative, collaborative and data-driven.

NOTE: This role is in the San Jose office. You must be in SJ or willing to relocate for this position.

What you'll do - Partner with Adobe Research to develop cutting edge models! - Design and build applications powered by generative AI, including working on traditional engineering problems such as defining APIs, integrating with UIs, deploying Cloud services, CICD, etc., as well as implementing ML- and LLM-Ops best practices.
- Engage in the product lifecycle, design, deployment, and production operations. - Provide technical leadership in everything from architectural design and technology choices to holistic evaluation of ML models.

What you need to succeed - The ideal candidate will have the following background: - PhD or MS degree in Computer Science, Data Science or related field required.
- 10+ years of applied research experience in software industry/academic research with 5+ years of shown experience developing, evaluating ML models, and deploying models into production. - Deep understanding of statistical modeling, machine learning, or analytics concepts, and a track record of solving problems with these methods; ability to quickly learn new skills and work in a fast-paced team. - Proficient in one or more programming languages such as Python, Scala, Java, SQL. Familiarity with cloud development on Azure/AWS. - Fluent in at least one deep learning framework such as TensorFlow or PyTorch. - Experience with LLMs and emerging area of prompt-engineering. - Recognized as a technical leader in related domain.
- Experience working with both research and product teams.
- Excellent problem-solving and analytic skills - Excellent communication and relationship building skills.

Location Chicago; New York

Description:

Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems.

Our trading teams are each comprised of a dynamic group of traders, quantitative researchers, and engineers who work together to examine the global markets, seeking to understand the complexities of various traded products and exchanges. They leverage their impeccable statistical analysis and data mining skills, using the results of their research to make forecasts and develop profitable predictive trading models.

We are seeking research scientists with a demonstrated ability to apply machine learning to achieve state-of-the-art capabilities in complex and challenging domains. The ideal person for this role will be capable of implementing an open-ended research project from concept to production and continuously improving model design, tools, and infrastructure. Potential projects may target any area of the quantitative research and monetization process. We believe that successful research efforts require a fluid mix of skills including ML expertise, engineering pragmatism, statistics and market intuition.

Other duties as assigned or needed.

Skills You’ll Need:

• Strong publication record at ICML, ICLR, AAAI, NeurIPS, UAI, KDD, or equivalent and/or contributions to open-source AI research
• Strong general ML background with exposure to modern deep learning techniques and/or language modeling architectures (e.g. transformers, SSMs)
• Solid development skills in Python and/or C++
• Familiarity with ML libraries/frameworks such as PyTorch, TensorFlow, and/or JAX
• Intellectual curiosity, versatility, and originality combined with a pragmatic outlook
• Ability to thrive in a collaborative, team-oriented environment
• Ability to reason through quantitative problems and communicate effectively with trading researchers
• Reliable and predictable availability required

Bonus Points:

• Experience with HPC and distributed large model training
• Experience with GPU performance optimization (CUDA or ROCm)
• Experience with end-to-end model development
• Strong opinions on best practices in ML research, tooling, and/or infrastructure

INTERNATIONAL STUDENTS are encouraged to apply. We accept students eligible for CPT/OPT and we sponsor work visas for full-time positions.

The estimated base salary for this role is $300,000 per year.

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.

We are looking for a Principal Machine Learning Engineer, a senior technical visionary, to be the Principal Technical Lead for our Content Engineering team setting up overall technical strategy, unified technical architecture and defining a roadmap for industry‑leading methodology. Strong hands-on machine learning background including deep learning architectures, generative AI, low-resource ML (zero shot, few shot), responsible AI and large scale deployment and measurement is required.

As the Principal Tech Lead for Content you'll be responsible for the technical direction, strategy and health of our Content Engineering org. You'll ensure that our technology can deliver on the business/product requirements necessary to keep Pinterest safe and positive. This means working with other leads to set and execute a long-term strategy for Trust, aligning the strategy with other clients where it makes sense and communicating to leadership our current status and path to having world-class Trust capabilities. You'll also foster a healthy community where all Trust engineers can learn best practices, collaborate effectively and understand our technical direction.

What you’ll do

• Develop strong partnerships with product teams to understand and proactively address future technology needs and current developer pain points.
• Champion and drive large-scale, cross-functional initiatives that improve the trust and safety of our platform.
• Act as the ultimate “customer representative” for engineers on Trust, including representing needs to leadership and prioritizing projects on the platform teams that ensure high quality capabilities and a world-class Pinner experience.
• Scale your leadership through both direct mentorship and via best practices, processes, training and tools.
• Ensure solid technical plans are in place for projects within Trust via direct review or delegation.
• Be the technical point of contact for decisions that impact the whole Pinterest platform and for cross-functional partners like policy, operations and legal.

What we’re looking for:

• Deep expertise building large scale ML systems at scale with modern frameworks.
• Knowledge of (and a passion for) building responsible and quality‑first discovery surfaces.
• Track record of delivering large, cross-functional projects across multiple organizations.
• Strong written and verbal communication skills and proven ability to collaborate cross-functionally.

Amsterdam

Flow Traders is committed to leveraging the most recent advances in machine learning, computer science, and AI to generate value in the financial markets. We are looking for Quantitative Researchers to join this challenge.

As a Quantitative Researcher at Flow Traders, you are an expert in mathematics and statistics. You are passionate about translating challenging problems into equations and models, and have the ability to optimize them using cutting-edge computational techniques. You collaborate with a global team of researchers and engineers to design, build, and optimize our next generation of models and trading strategies.

Are you at the top of your quantitative, modeling, and coding game, and excited by the prospect of demonstrating these skills in competitive live markets? Then this opportunity is for you.

USA or International

The Renaissance Philanthropy Engineering Hub provides on-demand development technical assistance in support of grant-funded educational technology projects. Our support allows mission-driven teams to overcome hurdles and achieve their technical and impact goals. As a Full Stack Software Engineer, you will act as a full-service consultant and developer to support projects through strategy advice, technical reviews, prototyping, evaluation, and development of new product functionality.

Remote - Americas

Applied Machine Learning Engineer - Gen AI/LLM 

Join Shopify's innovative team as we develop an AI Personal Shopper to transform the online shopping experience. Leveraging cutting-edge AI, including Large Language Models (LLM) and advanced machine learning algorithms, you'll play a pivotal role in delivering personalized recommendations and insightful suggestions tailored to individual preferences. Our goal is to redefine e-commerce by creating a concierge service that enhances how customers interact with Shop and Storefronts. As a Machine Learning Engineering (MLE) lead or individual contributor, you'll be at the forefront of implementing AI systems at scale, directly empowering merchants and creating tangible solutions with real-world impact.

Key Responsibilities:

• Develop and deploy Generative AI, natural language processing, and machine learning models.
• Design and produce scalable AI/ML system architectures.
• Implement data pipelines for fine-tuning LLMs.
• Solve high-impact data problems, delivering business impact through data and machine learning products.
• Prioritize and communicate effectively with both technical and non-technical audiences.

Qualifications:

• Mastery in building data products using generative AI, RLHF, and fine-tuning LLMs.
• End-to-end experience in training, evaluating, testing, and deploying machine learning products at scale.
• Experience in building data pipelines and driving ETL design decisions using disparate data sources.
• Proficiency in Python, shell scripting, streaming and batch data pipelines, vector databases, DBT, BigQuery, BigTable, or equivalent, and orchestration tools.
• Experience with running machine learning in parallel environments (e.g., distributed clusters, GPU optimization).

At Shopify, we pride ourselves on moving quickly—not just in shipping, but in our hiring process as well. If you’re ready to apply, please be prepared to interview with us within the week. Our goal is to complete the entire interview loop within 30 days. You will be expected to complete a pair programming interview, using your own IDE.



This role may require on-call work



Ready to redefine e-commerce through AI innovation? Join the team that’s making commerce better for everyone.

New York

Quantitative Analyst Ph.D. Intern (New York) – Summer 2026

The D. E. Shaw group seeks talented Ph.D. candidates with impressive records of academic and/or professional achievement to join the firm as quantitative analyst interns. Ph.D. interns explore how the analytical skills gained from their graduate programs may relate to the work done at the firm while interacting with fellow interns and employees of similar academic backgrounds in a collegial working environment. This 12-week program will take place in New York and is expected to run from June to August 2026. 

What you'll do day-to-day

You’ll spend the summer working on a research project that typically involves exploring a variety of statistical modeling techniques and writing software to analyze financial data. You’ll have a dedicated mentor in one of our quantitative research groups and are encouraged to attend our academic speaker series and track academic progress in various areas that may be of interest.

Who we're looking for

• Individuals with impressive records of academic achievement, including advanced coursework in fields such as math, statistics, physics, engineering, computer science, or other technical and quantitative programs. 
• Applicants should have notable research productivity in their respective areas of study as well as a track record of creativity in their field(s). 
• Interest or experience working in a data-driven research environment, including manipulation of data using high-level programming languages such as Python, is preferred. 
• An exceptional aptitude for abstract reasoning, problem solving, and quantitative thinking, in addition to prior probability or statistics knowledge, is a plus. 
• No previous finance experience is necessary, though candidates should have an interest in learning about quantitative finance.
• Students who apply to this internship are usually approaching their final year of full-time study.
• The position offers a monthly base salary of 25,000USD, overtime pay, a sign-on bonus of 25,000USD, travel coverage to and from the internship, and choice of furnished summer housing or a 10,000USD housing allowance. It also includes a 3,300USD stipend for self-study materials and a 4,000USD stipend for personal technology equipment. If you have any questions about the compensation, please ask one of our recruiters.

Toronto or remote

Mission: We are seeking a highly skilled Machine Learning Engineer to join our advanced model development team. This role focuses on pre-training, continued training, and post-training of models, with a particular emphasis on draft model optimization for speculative decoding and quantization-aware training (QAT). The ideal candidate has deep experience with training methodologies, open-weight models, and performance-tuning for inference.

Responsibilities & opportunities in this role: Lead pre-training and post-training efforts for draft models tailored to speculative decoding architectures. Conduct continued training and post-training of open-weight models for non-draft (standard) inference scenarios. Implement and optimize quantization-aware training pipelines to enable low-precision inference with minimal accuracy loss. Collaborate with model architecture, inference, and systems teams to evaluate model readiness across training and deployment stages. Develop tooling and evaluation metrics for training effectiveness, draft model fidelity, and speculative hit-rate optimization. Contribute to experimental designs for novel training regimes and speculative decoding strategies.

Ideal candidates have/are: 5+ years of experience in machine learning, with a strong focus on model training. Proven experience with transformer-based architectures (e.g., LLaMA, Mistral, Gemma). Deep understanding of speculative decoding and draft model usage. Hands-on experience with quantization-aware training, including PyTorch QAT workflows or similar frameworks. Familiarity with open-weight foundation models and continued/pre-training techniques. Proficient in Python and ML frameworks such as PyTorch, JAX, or TensorFlow.

Preferred Qualifications: Experience optimizing models for fast inference and sampling in production environments. Exposure to distributed training, low-level kernel optimizations, and inference-time system constraints. Publications or contributions to open-source ML projects.

Attributes of a Groqster: Humility - Egos are checked at the door Collaborative & Team Savvy - We make up the smartest person in the room, together Growth & Giver Mindset - Learn it all versus know it all, we share knowledge generously Curious & Innovative - Take a creative approach to projects, problems, and design Passion, Grit, & Boldness - no limit thinking, fueling informed risk taking

Stellenbosch University, South Africa

The Department of Mathematical Sciences at Stellenbosch University (South Africa) has a 2-year postdoctoral position available in the area of machine learning for wildlife monitoring and conservation. The project will look at:

zero-shot capabilities of foundation models on challenging real-world datasets typical in African wildlife and environment monitoring (e.g., camera trap imagery);

few-shot learning and generative modelling to deal with these large, unlabelled, long-tailed, noisy image sets.

Applicants must have obtained a PhD degree within the last 4 years, in a field related to the project's themes. The fellowship must commence by 1 March 2026 (preferably sooner).

Applications and supporting documents can be submitted through this online form.

Applications close 15 December 2025.

Enquiries: Prof. Willie Brink (wbrink@sun.ac.za).