NeurIPS 2025 Career Opportunities

Hong Kong

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.

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 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 data team focused on the quality of the datasets being delivered for training our models. This is a hands-on role where your #1 mission would be to improve the quality of the pretraining datasets by leveraging your previous experience, intuition and training experiments. This includes synthetic data generation and data mix optimization.

You would be closely collaborating with other teams like Pre-training, Fine-tuning and Product to define high-quality data both quantitatively and qualitatively.

Staying in sync with the latest research in the field of dataset design and pretraining is key for being successful in a role where you would be constantly showing original research initiatives with short time-bounded experiments and highly technical engineering competence while deploying your solutions in production. With the volumes of data to process being massive, you'll have at your disposal a performant distributed data pipeline together with a large GPU cluster.

YOUR MISSION

To deliver massive-scale datasets of natural language and source code with the highest quality for training poolside models.

RESPONSIBILITIES

• Follow the latest research related to LLMs and data quality in particular. Be familiar with the most relevant open-source datasets and models
• Closely work with other teams such as Pretraining, Fine-tuning or Product to ensure short feedback loops on the quality of the models delivered
• Suggest, conduct and analyze data ablations or training experiments that aim to improve the quality of the datasets generated via quantitative insights

SKILLS & EXPERIENCE

• Strong machine learning and engineering background
• Experience with Large Language Models (LLM)
• Good knowledge of Transformers is a must
• Knowledge/Experience with cutting-edge training tricks
• Knowledge/Experience of distributed training
• Trained LLMs from scratch
• Knowledge of deep learning fundamentals
• Experience in building trillion-scale pretraining datasets, in particular: Ingest, filter and deduplicate large amounts of web and code data
• Familiar with concepts making SOTA pretraining datasets: multi-linguality, curriculum learning, data augmentation, data packing, etc
• Run data ablations, tokenization and data-mixture experiments
• Develop prompt engineering pipelines to generate synthetic data at scale
• Fine-tuning small models for data filtering purposes
• Experience working with large-scale GPU clusters and distributed data pipelines
• Strong obsession with data quality
• 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
• Programming experience: strong algorithmic skills, Linux Git, Docker, k8s, cloud managed services, Data pipelines and queues, Python with PyTorch or Jax Nice to have:
• Prior experience in non-ML programming, especially not in Python
• C/C++, CUDA, Triton

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

Location CAN, ON, Toronto

Description Are you a passionate scientist in the computer vision area who is aspired to apply your skills to bring value to millions of customers? Here at Ring, we have a unique opportunity to innovate and see how the results of our work improve the lives of millions of people and make neighborhoods safer.

As a Principal Applied Scientist, you will work with talented peers pushing the frontier of computer vision and machine learning technology to deliver the best experience for our neighbors.

This is a great opportunity for you to innovate in this space by developing highly optimized algorithms that will work at scale. This position requires experience with developing Computer Vision, Multi-modal LLMs and/or Vision Language Models. You will collaborate with different Amazon teams to make informed decisions on the best practices in machine learning to build highly-optimized integrated hardware and software platforms.

Palo Alto

Mission: Design and build the real-time data infrastructure that powers GroqCloud’s global revenue engine, processing hundreds of billions of events each day, sustaining millions of writes per second, and enabling a multi-billion-dollar business to operate in real time. Drive the intelligence layer that fuels global billing, analytics, and real-time business operations at worldwide scale.

Responsibilities & opportunities in this role: Architect high-performance data pipelines to ingest, process, and transform millions of structured and semi-structured events daily. Build distributed, fault-tolerant frameworks for streaming data from diverse sources. Create data services and APIs that make usage and billing data easily accessible across the platform. Develop lightweight tools and dashboards to monitor and visualize data ingestion, throughput, and system health.

Ideal candidates have/are: Strong background in real-time data processing, distributed systems, and analytics infrastructure. Hands-on experience with streaming technologies such as Kafka, Flink, Spark Streaming, or Redpanda and real-time analytics databases such as Clickhouse, Druid, or Pinot. Deep understanding of serialization, buffering, and data flow optimization in high-throughput systems.

Bonus points: Experience deploying and managing workloads on Kubernetes. A passion for systems performance, profiling, and low-latency optimization. Familiarity with gRPC and RESTful API design.

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

The Deep Learning for Precision Health Lab (www.montillolab.org ), a part of the Biodata Engineering Program of the Biomedical Engineering Department at the University of Texas Southwestern in Dallas, TX seeks a talented and motivated Computational Research Scientist to support large-scale multimodal neuroimaging and biomedical data analysis initiatives and to support advanced AI. The successful candidate will play a key role in curating and analyzing multimodal datasets, preparing resources for foundation model development, and supporting NIH-funded projects at the intersection of machine learning, medical image analysis, neuroscience, and oncology. This is a full-time, long-term staff scientist position focused on technical excellence, reproducible data management, and collaborative research in a dynamic academic environment. The successful candidate’s work will directly inform AI-driven discovery in neurological and oncologic diseases.

With cutting-edge computational infrastructure, access to leading neurology, neuroscience, and cancer experts, and an unparalleled trove of high-dimensional imaging and multi-omic data, our machine learning lab is poised for success in these research endeavors.

Primary Responsibilities

• Configure/develop and run existing foundation or large-scale deep learning models for benchmarking.
• Contribute to manuscript writing and code documentation.
• Curate and manage large neuroimaging & bioimaging datasets that include structural, diffusion, and functional MRI, dynamic PET, EEG, fluorescence microscopy, and multi-omic or clinical data drawn from NIH-supported consortia.
• Develop and maintain automated pipelines for data quality control and reproducibility.
• Clean and prepare datasets for downstream ML and deep-learning workflows.

Qualifications

• M.S. or Ph.D. in Computer Science, Biomedical Engineering, Electrical Engineering, Physics, Statistics, or a closely related computational field.
• Candidates must have extensive neuroimaging and biomedical image analysis experience.
• Candidates must have existing mastery of one or more mainstream DL frameworks (e.g., PyTorch, TensorFlow) and be able to explain intricacies of the DL models they have constructed.
• Experience running and managing batch jobs on SLURM or similar HPC systems.
• Preferred: familiarity with neuroimaging data formats (DICOM, NIfTI, HDF5, MP4, EEG) and web-scraping or data-discovery scripting.

Compensation and Appointment

• Term and Location: Full-time, On-site in Dallas, TX (5 days/week)
• Salary: Highly competitive and commensurate with experience.
• Work Authorization: Must be legally authorized to work in the U.S.
• Mentorship: Direct supervision by Dr. Albert Montillo with opportunities for co-authorship and professional growth in mentoring junior team members and leading publications.

For consideration:

Reach out for an in-person meeting in San Diego at NeurIPS 2025 (or virtually afterwards) via email to Albert.Montillo@UTSouthwestern.edu with the subject “ComputationalResearchScientist-Applicant-NeurIPS” and include: (1) CV, (2) contact information for 3 references, (3) up to three representative publications, and (4) your start window. Positions are open until filled; review begins immediately.

Noumenal Labs | Remote-friendly | Full-time

Noumenal’s Probabilistic Perception Lab builds systems capable of navigating outdoor environments through probabilistic spatial reasoning and structured uncertainty reduction. We are looking for a Research Engineer with deep experience in probabilistic inference, spatial AI, and structured generative models to drive applied breakthroughs in perception of outdoor environments. You will work closely with researchers, systems engineers, commercial software engineers, and roboticists to build models that integrate 3D geometry, scene composition, uncertainty, and adaptive inference grounded in generative representations. This role is ideal for someone who has operated at the intersection of probabilistic computing, 3D scene understanding, computational neuroscience, and machine learning research, with experience spanning both foundational research and scalable, applied engineering.

What You’ll Do

~ Develop and deploy probabilistic generative models for perception, scene understanding, and spatial reasoning (structured generative models, inverse graphics, Bayesian scene reconstruction) on hardware in a commercial product. ~ Build inference engines for SLAM, 3D reconstruction, object-centric scene modeling, and spatial world models, leveraging MCMC, variational inference, or novel structured inference techniques. ~ Design systems that combine topological, geometric, and probabilistic methods for robust representation of spatial and conceptual structure. ~ Lead and engage in directed engineering efforts to translate novel algorithms into performant systems suited for real-time or near–real-time perception. ~ Collaborate with researchers in probabilistic computing, robotics, and AI to prototype, test, and iterate on models using synthetic and real sensory data.

Required Skills

~ Experience building perception systems in robotics. ~ Ability to translate research concepts into robust, scalable engineering implementations. ~ Strong coding ability in Python and modern ML frameworks (PyTorch, JAX, or TensorFlow). ~ Expertise in probabilistic inference, structured generative models, or Bayesian approaches (MCMC, variational inference, factorized models, hierarchical generative models). ~ Experience in 3D perception and spatial AI, including at least one of: SLAM, object-centric modeling, structured scene representations, or probabilistic inverse graphics frameworks. ~ Commitment to open-source contributions and internal cross-lab collaborations.

Ideal Background

~ Experience with topological data analysis, geometric representations, or mathematical structure in learning systems (e.g., planning in latent spaces). ~ Strong mathematical background (geometry, topology, optimization, or probabilistic modeling). ~ Background working in interdisciplinary research groups (AI, neuroscience, robotics, mathematics). ~ Publications in machine learning, probabilistic modeling, computational neuroscience, or mathematical methods for perception.

What We Offer

~ Close collaboration with researchers in robotics, physics-inspired AI, and spatial intelligence. ~ Access to real-world data for 3D perception and inference experiments. ~ A remote-friendly environment, flexible work culture, competitive salary + equity.

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

Within the Ads Delivery team, we try to connect the dots between the aspirations of pinners and the products offered by our partners. In this role, you will be responsible for developing and executing a vision for the evolution of the Pinterest marketplace. You will design and implement systems that improve the ads delivery funnel, experiments that shape the utility function, auction mechanism design, ad allocation and for deriving new insights through analysis of the marketplace dynamics. In short, this is a unique position, where you’ll get the freedom to work to bring together pinners and partners in this unique marketplace.

What you’ll do:

• Build and improve backend systems and statistical models that underlay the marketplace to maximize value for Pinners, Partners and Pinterest.
• Define and implement experiments to understand long term Marketplace effects.
• Develop strategies to balance long and short term business objectives.
• Drive multi-functional collaboration with peers and partners across the company to improve knowledge of marketplace design and operations.

What we’re looking for:

• Degree in Computer Science, Machine Learning, Statistics or related field.
• 8+ years of industry experience.
• 2+ years of experience leading projects/teams.
• Strong software engineering and mathematical skills with knowledge of statistical methods.
• Hands-on experience with large-scale online e-commerce systems is a plus.
• Background in computational advertising is preferred, but not required.

New York, New York

WRITER is experiencing an incredible market moment as generative AI has taken the world by storm. We're looking for a cloud platform engineer to establish our cloud platform team, focusing on building and scaling our multi-cloud architecture across AWS, GCP and Azure regions. In this founding role, you'll architect and implement highly scalable systems that handle complex multi-tenant workloads while ensuring proper tenant isolation and security.

As a cloud platform engineer, you'll work closely with our development teams to build robust, automated solutions for environment buildout, tenant management, and cross-region capabilities. You'll design and implement systems that ensure proper tenant isolation while enabling efficient environment lifecycle management. This is a unique opportunity to establish our cloud platform team and have a direct impact on our platform's scalability and security, working with cutting-edge technologies and solving complex distributed systems challenges.

Your responsibilities:

• Establish and lead the cloud platform team
• Architect and implement multi-cloud infrastructure across AWS, GCP and Azure regions
• Design and build highly scalable, distributed systems for multi-tenant workloads
• Define and implement best practices for automated infrastructure across cloud providers
• Architect and implement tenant isolation mechanisms to ensure data security and compliance
• Create and manage environment lifecycle automation for dev/qa/demo environments
• Design and implement cross-region capabilities and active-active deployments
• Develop tenant migration and pod management solutions
• Collaborate with development teams to understand tenant requirements and constraints
• Implement infrastructure as code for region-level deployments
• Monitor and optimize tenant isolation and security
• Document tenant management processes and best practices
• Stay current with industry trends in multi-tenant architectures
• Participate in on-call rotation for critical platform services
• Contribute to technical decisions around tenant isolation and region management
• Mentor and grow the cloud platform team
• Implement and maintain deployment automation and CI/CD pipelines
• Design and optimize Kubernetes infrastructure for multi-tenant workloads
• Drive infrastructure cost optimization and efficiency

Is this you?

• Have 8+ years of experience in cloud platform engineering or related role (site reliability engineering)
• Have 3+ years of experience leading engineering teams
• Are passionate about building secure, scalable multi-tenant platforms
• Have extensive experience with cloud platforms (AWS, GCP, or Azure)
• Are proficient in infrastructure as code (Terraform, CloudFormation, etc.)
• Have deep experience with multi-tenant architectures and tenant isolation
• Can write clean, maintainable code in Python, Go, or similar languages
• Understand containerization and orchestration (Docker, Kubernetes)
• Have proven experience with cross-region deployments and active-active architectures
• Are comfortable working with multiple development teams
• Can communicate technical concepts clearly to both technical and non-technical audiences
• Take ownership of projects and drive them to completion
• Are excited about building automated infrastructure solutions
• Have a strong focus on security and tenant isolation
• Are comfortable with on-call responsibilities
• Have experience with agile development methodologies
• Have experience establishing new teams and best practices
• Can balance technical leadership with hands-on implementation
• Are excited about solving complex distributed systems challenges
• Have experience with multi-cloud architectures and hybrid deployments

Salesforce Research is looking for outstanding research interns. Ideal candidates have a strong background in one or more of the following fields:

Conversational AI Multimodal Data Intelligence Multimodal Content Generation Fundamentals of Machine Learning and AI Responsible & Trusted AI Natural Language Processing Areas of Application:

Software Intelligence AI for Operations AI for Availability & Security Environment & Sustainability Candidates that have published in top-tier conferences or journals (e.g. NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR,) are preferred. As a research intern, you will:

Work with a team of research scientists and engineers on a project that ideally leads to a submission to a top-tier conference

Learn about exciting research and applications outside your expertise

Focus on pure research that incorporates into your PhD focus area and contributes to the AI Community

Attend conferences with our researchers to showcase your accepted papers

Requirements:

PhD candidate in a relevant research area Excellent understanding of deep learning techniques, i.e., CNN, RNN, LSTM, GRU, GAN, attention models, and optimization methods Experience with one or more deep learning libraries and platforms, e.g. PyTorch, TensorFlow, Caffe, or Chainer Strong background in machine learning, natural language processing, computer vision, or reinforcement learning Strong algorithmic problem solving skills Programming experience in Python, Java, C/C++, Lua, or a similar language This internship is a minimum of 12 weeks