As a Machine Learning Researcher at IMC, your work will directly impact our global trading strategies. You will leverage your superior analytical, mathematical, and computing skills to improve existing models and develop new ones. We will empower you to discover your unique niche and excel, taking on responsibility and ownership from the start. Machine Learning Researchers work closely with Traders and Developers in an environment where problem solving, innovation and teamwork are recognized and rewarded.

The Chan Zuckerberg Biohub Network (https://www.czbiohub.org/) is a group of nonprofit research institutes that bring together scientists, engineers, and physicians with the goal of pursuing grand scientific challenges on 10- to 15-year time horizons. The CZ Biohub Network focuses on understanding underlying mechanisms of disease and developing new technologies that will lead to actionable diagnostics and effective therapies.

We pursue large scientific challenges that cannot be pursued in conventional environments We enable individual investigators to pursue their riskiest and most innovative ideas The technologies developed at the CZ Biohub Network facilitate research by scientists and clinicians at our home institutions and beyond Diversity of thought, ideas, and perspectives are at the heart of CZ Biohub Network and enable disruptive innovation and scholarly excellence. We are committed to cultivating an organization where all colleagues feel inspired and know their work makes an important contribution.

The Biohub Network is seeking an accomplished computational biologist and machine learning/AI specialist to join our interdisciplinary team. This role requires experience in research settings, a background in biology, and a proven ability to design, evaluate, and publish innovative computational methodologies that leverage machine learning, statistics, language modelling, and AI to advance biological research and discovery. Research projects to accelerate the rate of scientific discovery will be assigned by the President of the New York location, and in collaboration with research teams across the organization.

The ideal candidate will have a strong track record of accomplishments and a dedication to collaborative work within a highly interdisciplinary environment.

This role is based out of the New York location.

What You'll Do - Contribute to a dynamic, innovative, and collaborative program that aligns with the mission of CZ Biohub NY. - Develop and evaluate cutting-edge computational / AI methodologies using data generated from across all research groups and incorporating relevant available datasets for to develop predictive models. - Collaborate within an interdisciplinary research environment to develop, test, and validate models. - Engage with colleagues throughout the Biohub to uphold our values of scholarly excellence, innovation, open communication, hands-on hacking, and partnership. - Communicate progress and results with colleagues inside and outside of your team. - Publish and disseminate impactful findings through preprints (medRxiv, bioRxiv) and/or software repositories (e.g., GitHub). - Work with the CZ Biohub team to patent and license technologies resulting from your research.

What You'll Bring - PhD in Computational Biology, AI / Machine learning, Applied Statistics or a MS plus relevant job experience. - Background in relevant areas of biomedical science, demonstrating a deep understanding of cellular biology, transcription and protein signal transduction. - 2-4 years of post-doctoral and/or industry experience demonstrating the ability to implement, evaluate, and create new computational methodologies that leverage machine learning, statistics, and AI for biological research and discovery. - Experience in building and evaluating machine learning and/or neural network models on biological data, with a deep understanding of feature selection, regularization, model introspection, and interpretability. - Proficiency in using and modifying probabilistic learning or deep learning models such as RNNs, GNNs, protein sequence models, or natural language processing models. - Proven track record of individual innovation, as well as a strong ability to work collaboratively. - Outstanding interpersonal and communication skills. - Demonstrated commitment to open science and alignment with the mission and values of CZ Biohub.

Senior Software Engineer (Backend)

Location: Boston (US) / Barcelona (Spain)

About us:

Axiomatic_AI is dedicated to accelerating R&D by developing the next generation of Automated Interpretable Reasoning, a verifiably truthful AI model built for reasoning in science and engineering, empowering engineers specifically in hardware design and Electronic Design Automation (EDA), with a mission to revolutionize the fields of hardware design and simulation in the photonics and semiconductor industry. We seek highly motivated professionals to help us bring these innovations to life, driving the evolution from development to commercial product.

Position Overview

As a Senior Software Engineer (Backend) at Axiomatic, you will:

• Design and build scalable backend services (FastAPI, FastMCP, Python)
• Own key features end-to-end: from API design to database schema to deployment
• Integrate AI capabilities (LLMs, Agents) into production systems
• Collaborate with frontend, AI, and infrastructure teams on architecture and delivery
• Ensure system reliability, performance, and security
• Mentor other engineers as the team grows

Key Responsibilities

Architecture & Design

• Contribute to system architecture and technical decisions
• Design for scalability, reliability, and security
• Propose and implement improvements to codebase and infrastructure
• Document technical designs and API contracts
• Ensure best practices are followed by the team

Backend Development

• Design and implement REST APIs using FastAPI
• Build scalable, maintainable, and testable services
• Design database schemas and optimize SQL queries (PostgreSQL)
• Integrate with external services (OpenAI, Anthropic)
• Optimize API performance, latency, and throughput

Quality & Testing

• Write comprehensive unit and integration tests
• Participate in code reviews (give and receive feedback)
• Debug and resolve production issues
• Maintain high code quality standards

Collaboration & Mentorship

• Work closely with Tech Lead on architecture and roadmap
• Partner with AI Platform Engineer on AI integrations
• Mentor mid-level engineers and share knowledge

Required Skills & Experience

Must-Have

• 7+ years of backend development experience
• Deep knowledge in Python, FastAPI
• Strong database skills: PostgreSQL, SQL, ORMs (SQLAlchemy)
• Experience designing REST APIs: best practices, versioning, documentation
• Cloud platform experience: GCP preferred (AWS, Azure acceptable)
• Testing mindset: unit tests, integration tests, TDD
• Version control & CI/CD: Git, GitHub Actions, Docker
• Strong problem-solving skills: debugging, performance optimization
• Fluent in English (Spanish is a plus)

Nice-to-Have

• Experience with FastMCP
• Familiarity with LangChain, Pydantic AI or similar frameworks
• Knowledge of async programming (asyncio, async/await)
• Familiarity with AI/ML APIs (OpenAI, HuggingFace, Vertex AI)
• Understanding of infrastructure as code (Terraform)
• Experience with microservices architecture

Tech Stack

Current Stack:

• Backend: Python, FastAPI, SQLAlchemy, Pydantic AI, Alembic
• Databases: PostgreSQL, Redis (caching)
• APIs: REST, WebSockets, SSE
• AI/ML: OpenAI API, Anthropic, Gemini
• Cloud: Google Cloud Platform (Cloud Run, Cloud SQL, GCS, VPCs, Bucket)
• Infrastructure: Terraform, Docker
• CI/CD: GitHub Actions, Terraform
• Testing: pytest, pytest-asyncio, pytest-cov

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.

About AIMATX

AIMATX is a Berkeley-based startup revolutionizing materials science by creating next-generation materials and molecules that power the future economy. Our AI-driven platform explores vast chemical spaces, predicts new materials and their properties, and accelerates discovery through intelligent, targeted experimentation. By reducing years of R&D to weeks, we are shaping the future of materials innovation; come join us!

AIMATX is built and guided by a world-class team at the intersection of science, AI and engineering. Our leadership includes Omar Yaghi (2025 Nobel Prize), Fernando Perez, inventor of Jupyter/IPython, alongside former CEOs of public companies and leading researchers in generative AI and autonomous experimentation. This ecosystem brings unmatched scientific depth, computational expertise, and entrepreneurial excellence to accelerate the future of discovery.

Role Overview

We are seeking a Machine Learning Engineer with deep expertise in large-scale generative models (e.g., LLMs, diffusion models) to join our innovative team. You will design, build, and scale the core AI systems that power our materials discovery engine, enabling rapid experimentation, robust deployment, and continuous improvement. As part of our technical team, you will:

• Design and implement training pipelines for LLMs, diffusion models and related architectures for molecular, materials and experimental design.
• Build robust data pipelines and preprocessing workflows for multimodal scientific data.
• Optimize model training and inference at scale, including distributed training and mixed-precision acceleration.
• Develop evaluation, benchmarking and monitoring frameworks to assess reliability, calibration and performance of generative models.
• Collaborate with scientists and engineers to integrate models into self-driving lab workflows and closed-loop experimentation.
• Work closely with MLOps and platform teams to ensure reproducibility, experiment tracking and scalable deployment.
• Stay current with advances in LLMs, diffusion models, reinforcement learning and agentic AI, and translate promising ideas into production systems.
• Maintain high engineering standards, including testing, documentation and code review.

Required Qualifications

• Degree in Computer Science, Machine Learning, Applied Mathematics, Engineering or a related technical field (or equivalent practical experience).
• Strong software engineering experience building and maintaining ML systems in production.
• Expertise with deep learning frameworks such as PyTorch or JAX.
• Proficiency with Python and experience working in collaborative, large-scale codebases.
• Demonstrated track record of owning and delivering end-to-end ML projects from prototype to production.

Preferred Qualifications

• Experience working with generative models in chemistry or materials science.
• Background or strong interest in scientific domains (chemistry, materials science, physics, biology) or scientific ML.
• Contributions to open-source ML or infrastructure projects, or publications in ML/AI conferences or journals.
• Expertise in training large-scale generative models (e.g., LLMs, diffusion models).

Soft Skills & Cultural Fit

• Excellent written and verbal communication skills.
• Collaborative mindset and ability to work effectively in a multidisciplinary team.
• Proactive and self-motivated, with the ability to take initiative.
• Commitment to scientific rigor, innovation and continuous learning.

Benefits & Perks

We offer a competitive salary with bonus potential and meaningful early equity. Compensation reflects experience, expertise and expected impact.

Additional benefits may include: - Flexible work arrangements and remote options. - Medical, dental, and vision coverage. - 401(k) with company matching. - Generous PTO and parental leave.

How to Apply

Send your CV to theo.jaf@aimatx.ai

Preference for on-site candidates in San Mateo, but remote possible.

BigHat is hiring a Principal ML Scientist. We've got an awesome high-throughput wetlab that pumps proprietary data into custom ETL and ML Ops infra to power our weekly design-build-train loop. Come solve hard-enough-to-be-fun problems in protein engineering in service of helping patients!

New York, NY

Applications are invited for scientists at all levels of seniority at the Center for Computational Mathematics (CCM) in the Flatiron Institute. Positions will be based, and have a principal office or workspace, at the Simons Foundation’s offices in New York City. The anticipated start date is between July and October 2026, but is flexible.

Research Scientists are expected to lead active research programs at the forefront of their fields internationally, including dissemination of results through scientific publications, conferences, and/or software. They also are expected to interact with other researchers in CCM, across the Flatiron Institute, and in the wider scientific community, as well as mentor postdoctoral fellows and student interns. They are encouraged to participate in and shape the vibrant activities of the institute such as workshops and seminars. They receive a research budget, participate in the selection and review of research staff, software engineers, and postdoctoral fellows within CCM, and have access to the institute’s powerful scientific computing resources.

To apply and for more details: https://apply.interfolio.com/173640

AI Scientist - AI Retrieval Systems and Knowledge Graphs

Location: Boston (US) / Barcelona (Spain)

About us:

Axiomatic_AI is dedicated to accelerating R&D by developing Automated Interpretable Reasoning, the next generation of a verifiably truthful AI model built for reasoning in science and engineering, with the goal of empowering engineers specifically in hardware design and Electronic Design Automation (EDA). Our mission is to revolutionize the fields of hardware design and simulation in the photonics and semiconductor industry as a first step towards automated and reliable scientific reasoning. We seek highly motivated professionals to help us bring these innovations to life, driving the evolution from research, development to commercial products.

Position overview:

As an AI Scientist specialized in retrieval systems and knowledge graphs, you will play a key role in developing Axiomatic’s verifiable scientific reasoning. Your responsibilities will include designing, prototyping, developing, testing and iterating on the core architecture. You will also manage data curation, conduct benchmarking to evaluate performance, analyze reasoning flaws and propose solutions. Close collaboration with our focused cross-functional team, consisting of AI Engineers, Software Engineers, Physicists and AI scientists, and regular alignment of the development with the customer and business needs will be essential to the success of the project.

Your mission:

• AI Research and Development: Contribute to the development of validated AI reasoning models and architectures, focusing on automated reasoning techniques and application to scientific fields where rigour and reliability are fundamental
• Data & Benchmarking: Supervise dataset curation, run benchmarks, and analyze performance results to guide improvements.
• Collaboration: Work closely with a cross-functional team of engineers and scientists, collaborating on solving challenging problems at the intersection of AI, physics and engineering.
• Documentation and Reporting: Develop detailed technical documentation and present research findings to internal teams and external stakeholders.
• Research & Publication: Contribute to cutting-edge research and publish results in top AI conferences and journals, helping advance the global AI research community whenever opportunities arise.

Key requirements:

• PhD degree in Data Science, Computer Science, Information Technology, Artificial Intelligence, Physics or related field
• 1–2 years of experience, preferably in a mathematical, engineering, scientific, or technical setting.
• Relevant experience in knowledge graphs and retrieval systems
• Strong communication skills
• Ability to collaborate effectively within a multidisciplinary and multicultural environment
• Curiosity, and a proactive, solution-oriented mindset
• Excitement to work in a dynamic and fast-paced environment, ability to thrive in ambiguity

Technical skills:

• Proficiency in Python
• Understanding of fundamental computer science principles
• Solid understanding of machine learning principles and architectures
• Fundamentals of statistics
• Excellent research and analytical skills
• Experience in ontology engineering and semantic modeling
• Experience in designing and developing RAG systems
• Familiarity with Neo4j
• Contributions to research (publications in top-tier conferences) or open-source projects

Preferred Qualifications (Nice to Have):

• Proven excellence in relevant areas (e.g., awards, competition wins)
• Proven ability to independently solve complex problems or lead challenging projects
• Academic or practical background in physics or other natural sciences / engineering
• Experience with good coding practices and software development standards
• Proficiency in agentic and deep learning frameworks
• Hands-on experience with large language models and/or other state of the art models

Global: United States, Europe and Asia

At Citadel, our mission is to be the most successful investment team in the world. Quantitative Researchers play a key role in this mission by developing next-generation models and trading approaches for a range of investment strategies. You’ll get to challenge the impossible in quantitative research by applying sophisticated and complex statistical techniques to financial markets, some of the most complex data sets in the world. As an intern, you’ll get to challenge the impossible in research through an 11 week program that will allow you to collaborate and connect with senior team members. In addition, you’ll get the opportunity to network and socialize with peers throughout the internship. Our signature internship program takes place June through August. Occasionally, we can be flexible to other times of the year. You will be able to indicate your timing preference in the application.