AI Scientist - Formal methods for science

Position overview:

As an AI Research Scientist with focus on formal methods for science, you will join a focused team, playing a key role in building new formal verification tools for science and engineering. Your responsibilities will include developing AI tools that will enable the systematic adoption of formal methods in quantitative scientific fields such as physics and engineering. You will also manage data curation, conduct benchmarking to evaluate performance, analyze reasoning flaws and propose solutions. Close collaboration with our dedicated cross-functional team - consisting of Mathematicians, AI Engineers, Software Engineers, Lean4 developers, Physicists and AI scientists - will be essential to the success of the project.

Your mission:

• Project contribution and technical execution: Play an active role in designing and developing autoformalization and validation strategies specifically tailored for scientific applications.

• Solution design & implementation: Engage in hands-on development to drive shorter-term impactful solutions in collaboration with our Business Development team. Propose, discuss, and implement technical solutions that address complex challenges, ensuring they are are well-structured, efficient, and aligned with industry best practices.

• Innovation & continuous learning: Stay updated with state-of-the-art techniques and advancements in the field. Continuously integrate the latest research and technologies to enhance the products’ impact.

• Cross-team collaboration: Work closely with AI and development teams to ensure seamless integration of solutions. Promote open communication and cooperation to enhance productivity and technical excellence.

• Knowledge sharing and developing the team: Foster a collaborative culture by actively sharing knowledge, insights, and best practices. Encourage teamwork and continuous learning to strengthen overall expertise within the company.

Key requirements:

• PhD in Computer Science, Artificial Intelligence, Physics, Machine Learning for scientific applications or related fields.
• Experience in applications of machine learning to relevant projects
• Proficient in Python, with the ability to write clean and efficient code
• Experienced with agentic AI and major deep learning framework
• Solid understanding of statistics and probability

About you:

• You excel as a team player, thriving in multidisciplinary and multicultural environments.
• You are highly motivated, hard-working, and committed to personal and professional growth through constant learning, new challenges and advancements.
• You quickly grasp new concepts and technologies, adapting efficiently to evolving requirements.
• You have a real passion for science and maths, and have a deep curiosity for understanding concepts from first principles.
• You like to try new technologies and quickly build exploratory prototypes.
• You thrive in a dynamic, fast-paced environment, embracing change with a proactive and solution-oriented approach.

Nice to have:

• Background in Physics, Engineering or other related computational science.
• Publications relevant to the company domain at top-tier conferences.
• Internship or work experience in one top AI company.
• Strong experience in at least one of the following: reinforcement learning, representation learning, program synthesis, NLP, graph machine learning, knowledge graphs, applied machine learning and data mining, optimization, machine learning for theorem proving, agentic LLMs and RAG, machine learning for science
• Proven contribution to open source projects.

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.

Global

Description

Qualcomm is proud to be attending NeurIPS 2025 in our home city San Diego, California! Qualcomm is powering efficient AI from edge to cloud, conducting novel foundational, platform, and applied AI research to enable intelligent computing everywhere.

We're inviting all those who have a passion for AI and are interested in opportunities in generative AI, visual AI, computer vision, and foundational machine learning to please follow the steps below.

1. Please visit the Qualcomm NeurIPS home page by clicking the URL to apply.

2. Register by clicking on the blue link. This allows us to identify your application as someone we met at NeurIPS.

3. Apply to any of the linked positions on our Qualcomm NeurIPS home page. Make sure you REGISTER first before applying. Your resume will stand out.

Palo Alto

Our formula for success is to hire exceptional people, encourage their ideas and reward their results.

As an AI Research Intern, you will be an integral member of a team of experienced technologists, quantitative researchers, and traders. You will collaborate closely with other researchers to solve challenging AI and machine learning problems. Your projects will vary depending on priorities at the start of your employment and could include solving forecasting problems or developing large language models (LLMs). We are looking for individuals eager to learn new AI technologies, create innovative solutions, and choose the right tools to directly impact our business. You will be surrounded by cutting-edge technology, given immediate responsibility, mentored by industry-leading experts, and attend a robust training program to ensure your success at DRW.

How you will make an impact… Algorithm Development: Creating and testing new AI models and algorithms to solve specific problems or improve existing methods. Data Engineering: Building data pipelines for model training and evaluation, including data collection, cleaning, preprocessing, and labeling, while contributing to better data management practices. Model Testing & Evaluation: Designing and implementing rigorous testing frameworks to assess model performance and identify areas for improvement. Collaboration: Working closely with team members to establish and refine research methodologies, promoting peer reviews, testing, and thorough documentation. Research & Learning: Staying updated on the latest AI techniques and advancements, sharing insights, and actively bringing improvements to research processes.

What you bring to the team… Are pursuing a PhD in artificial intelligence, machine learning, computer science, or a related field graduating between December 2026 and June 2027. Strong foundation in AI concepts. Strong knowledge of machine learning. Solid technical and programming skills (Python, Java, GitHub). Familiarity with machine learning framework (Spark, PyTorch, etc.). Excellent analytical, problem-solving, and communication skills. Deep interest in financial markets.

Preferred Skills: Experience with NLP tasks Knowledge of TensorFlow or PyTorch. Basic understanding of MLOps principles (monitoring, versioning, model serving).

Learning Opportunities: Gain in-depth experience with cutting-edge ML/AI techniques and model deployment. Develop robust machine learning research skills, from data engineering to model evaluation, while contributing to advancements in AI methodologies and practices. Contribute to research projects with potential impact on financial decision-making and other applied domains. Engage in fostering a collaborative research culture, driving improvements in research quality, and interdisciplinary collaboration.

What to expect during the internship Meaningful projects: Each project, advised by a trader, promotes a comprehensive learning experience and provides you with real-world work experience. Community: Throughout the summer, we host a variety of educational, social and team-building activities to explore the city, foster friendships and camaraderie. Housing: DRW provides fully furnished apartments located close to the office – making your morning commute as easy as possible. Mentorship: You’ll build a professional relationship with an experienced mentor in your field. Mentors and mentees meet to discuss goals, challenges and professional development and explore the city together at our mentor outings. Education: As the trading industry continually evolves, both in terms of new products and transaction methods, the future will present us with unique opportunities and challenges. You’ll complete an options course taught by an experienced trader and participate in a technology immersion course to better understand how technology and trading intersect.

JR2003228

NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Our work in AI and digital twins is transforming the world's largest industries and profoundly impacting society — from gaming to robotics, self-driving cars to life-saving healthcare, climate change to virtual worlds where we can all connect and create.

Our internships offer an excellent opportunity to expand your career and get hands on experience with one of our industry leading Generative AI teams. We’re seeking strategic, ambitious, hard-working, and creative individuals who are passionate about helping us tackle challenges no one else can solve.

What you will be doing: Design and implement algorithms that push the boundaries of generative AI, computer vision, robotics, and other technology domains relevant to NVIDIA’s business.

Collaborate with other team members, teams, and/or external researchers.

Transfer your research to product groups to enable new products or types of products. Deliverable results include prototypes, patents, products, and/or publishing original research.

What we need to see: Must be actively enrolled in a university pursuing a PhD degree in Computer Science, Electrical Engineering, or a related field, for the entire duration of the internship.

Depending on the internship, prior experience or knowledge requirements could include the following programming skills and technologies: Python, C++, CUDA, Deep Learning Frameworks (PyTorch, JAX, Tensorflow, etc.)

Strong background in research with publications at top conferences.

Excellent communication and collaboration skills.

Experience with large-scale model training is a plus.

Potential internships require research experience in at least one of the following areas: Multimodal Foundation Models

Diffusion Models

World Models

Image, Video, or Audio Generation

Large Language Models

Vision-Language Models

Action-Based Transformers

Long Context Methods

Physics-Based Simulation

Flow Based Generative Models

Synthetic Data Generation

AI for Science

Protein/Molecule Generation

Climate Modeling and Weather Forecasting

Partial Differential Equations (PDEs)

Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience. The hourly rate for our interns is 30 USD - 94 USD.

You will also be eligible for Intern benefits. ​

Applications are accepted on an ongoing basis.

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 characteristic protected by law.

Location USA, MA, North Reading

Description Amazon is looking for talented Postdoctoral Scientists to join the Research and AI Development team at Amazon Robotics for a one-year, full-time research position. This Postdoctoral Scientist will innovate in the areas of multi-agent planning and reinforcement learning for robotic systems. They will have the opportunity to address challenges related to the control and optimization of the world’s largest fleet of mobile robots under uncertainty, including policy learning for resource management in robotic storage systems.

At Amazon, we experiment and innovate relentlessly. Science is core in our offering to shoppers, advertisers and customers. Our scientists apply machine learning, optimization, causal modeling and game theory at scale to enhance the customer experience, help advertisers reach relevant audiences, and support brand building. We are seeking talented scientists to invent cutting-edge techniques in a variety of areas and innovate on behalf of shoppers, advertisers, and customers.

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.

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.

Pinterest helps Pinners discover and do what they love. Homefeed is literally the first surface Pinners see when they open the app and so it forms the front-and-center of the Pinterest experience for 400M+ pinners every month. The Homefeed Relevance team’s mission is to recommend inspiring & engaging pins for all our Pinners. We are looking for a Tech Lead Architect who can drive cross-team engineering efforts for shipping ML-driven product experiences to our pinners. You'll have the opportunity to work on various innovative projects of new product experiences, build large-scale low-latency systems and state-of-the-art machine learning models, and deliver great impact to our pinners and business metrics.

What you'll do:

• Improve relevance and the user experience on Homefeed.
• Work on state-of-the-art large-scale applied machine learning projects.
• Improve the efficiency and reliability of large-scale data processing and ML inference pipeline.
• Improve our engineering systems to improve the latency, capacity, stability and reduce infra cost.

What we're looking for:

• Languages: Python, Java.
• Machine Learning: PyTorch, TensorFlow.
• Big data processing: Spark, Hive, MapReduce.
• 7+ years’ experience with recommender systems or user modeling, implementing production ML systems at scale.
• 7+ years’ experience with large-scale distributed backend services.
• Experience working with deep learning and generative AI models.
• Experience closely collaborating with product managers/designers to ship ML-driven user-facing products.
• Bachelor’s in computer science or equivalent experience.

New. York, NY

Applications are invited for postdoctoral Flatiron Research Fellowships (FRFs) at the Center for Computational Mathematics (CCM) in the Flatiron Institute. FRF positions are initially two-year appointments, renewable for a third year contingent on performance. Fellows will be based, and have a principal office or workspace, at the Simons Foundation’s offices in New York City. Fellows may also be eligible for subsidized housing within walking distance of the Flatiron Institute. The start date is between July and October 2026.

To apply and for more details: https://apply.interfolio.com/173401

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.