AI4Science

About the Company We’re supporting a venture-backed AI company building cutting-edge surgical AI systems focused on video understanding. Their initial product transforms surgical videos into structured clinical reports - a focused entry point into a much bigger vision: developing a large-scale surgical foundation model, ultimately enabling advanced perception systems and autonomous surgical robotics.This is a deep-tech team building models from first principles - not treating AI as a black box. They combine ambitious research goals with real-world deployment in high-stakes medical environments. The Role Our client is hiring a Computer Vision Engineer who operates at the intersection of research and production. This is not a pure research role - and not just product engineering. They’re looking for someone who can:Deliver research-grade innovationWrite clean, scalable, production-ready codeMove fluidly between experimentation and deploymentYou will work on state-of-the-art video understanding systems that convert unstructured surgical footage into structured intelligence. This role is central to their long-term roadmap toward advanced autonomy. What You’ll Work OnDesigning and training advanced video understanding modelsExtending image-based CV architectures into temporal domainsWorking with multimodal and potentially 3D data (point clouds beneficial)Building scalable training pipelines, including distributed trainingBridging research prototypes into production systemsContributing to publications at leading AI conferencesCollaborating closely with a highly technical founding teamWhat They’re Looking For Technical BackgroundStrong foundation in Computer Vision on imagesExperience in video understandingExposure to 3D data / point clouds (beneficial)Experience with model training pipelines and optimizationAbility to implement research papers quickly and robustlyStrong software engineering fundamentalsResearch MindsetTrack record (or clear potential) for top conference-level workAbility to derive models from first principlesDeep understanding of modern CV architecturesProblem-SolvingComfortable working in ambiguous environmentsStrong analytical and structured thinking skillsAble to tackle unfamiliar domains effectivelyCollaboration & Product AwarenessUnderstands real-world constraints and client needsComfortable working closely with cross-functional teamsThrives in a collaborative environment (not a solo contributor role_Why Consider This Opportunity?Meaningful Impact - Building AI systems that support safer surgical procedures and improve access to care. Big Technical Vision - Report generation is the entry point. The broader roadmap includes foundational models and advanced autonomy systems. Genuine Deep Tech - This team is building core models and infrastructure from the ground up. Publication & Credibility - Publishing at leading conferences is part of the company’s DNA. Strong Talent Density - You’ll work alongside highly technical peers in an ambitious, research-driven environment.

Machine Learning Research ScientistLocation: Waltham, MA (Hybrid. Open to exceptional candidates outside Boston willing to spend approximately one week per month on site)Our client is an early-stage, venture-backed deep-tech company developing next-generation tools for subsurface characterization to accelerate clean energy deployment. Their work sits at the intersection of numerical physics, geoscience, and advanced machine learning, with a specific focus on reducing the cost and uncertainty of geothermal exploration.Founded by experts in physics and computation, the team is intentionally small, highly technical, and academically rigorous. They value first-principles thinking, intellectual curiosity, and a deep personal commitment to climate and clean energy impact. The company has over two years of runway following a recent pre-seed raise and is preparing for its next funding round.As a Machine Learning Research Scientist, you will help build research-grade machine learning models that tightly integrate physical laws with data. You will work closely with domain experts in physics simulation and software engineering to translate geophysical insight into principled ML architectures that can be trusted in real-world energy decisions.This is a selective, fundamentals-driven research role. Our client is not looking for a tooling-only ML profile, but for someone who thinks in mathematics and physics first.Key ResponsibilitiesDevelop machine learning models grounded in mathematical and physical principles to augment numerical physics simulationsDesign and implement algorithms that explicitly incorporate differential equations and physical constraintsCollaborate closely with physicists and engineers to translate geophysical understanding into ML architecturesInfluence the direction of core ML research within a lean, mission-driven teamBuild reproducible research workflows that feed directly into tools for clean energy deploymentRequired ExperienceMust-HavesPhD or equivalent research experience in Mathematics, Physics, or a closely related quantitative fieldStrong mathematical maturity with regular use of linear algebra, differential equations, and numerical methodsFirst-principles problem-solving approach rather than reliance on high-level ML abstractionsStrong Python skills and experience writing clean, research-grade ML codeGenuine motivation for climate, clean energy, and scientifically meaningful workNice-to-HavesExperience in scientific machine learning, including PINNs, operator learning, or surrogate modelingBackground in numerical simulation or high-performance computingExposure to geophysics, subsurface modeling, or energy-domain problemsWhat Success Looks LikeYou can clearly articulate the why, how, and what of your modeling decisions, particularly where physics and ML intersectYou produce reproducible research that improves the speed and quality of subsurface predictionsYou contribute to both foundational algorithms and practical tools used by scientists and engineersInterview ProcessVideo interview with the founding teamOn-site interview with the technical team over one full day