HEY,
BOSTON

Role: Full Stack Robotics EngineerSalary: upto $250,000Location: San Francisco, CA Opportunity to work on next-generation AI-driven physical systems capable of general-purpose manipulation, experimentation, and manufacturing. I’m looking for several Full-Stack Robotics Engineer to architect, prototype, and harden high-precision electromechanical platforms. You’ll own subsystems end-to-end across motion planning, real-time control, sensing, actuation, mechanical design, and embedded firmware. This role is deeply hands-on and requires first-principles thinking, rapid iteration, and the ability to integrate across disciplines to deliver reliable, high-performance robotic capability. Responsibilities:Build and integrate motion planning, kinematics, control, and perception into robust robotic behaviors.Develop real-time control loops, actuator interfaces, embedded firmware, and system-level safety.Lead mechanical design of end-effectors, precision mechanisms, and structural components.Design electrical systems including sensing, power, actuation electronics, and data pathways.Prototype, validate, and iterate complete robotic stacks from fabrication to deployment.Work cross-functionally to align mechanical, electrical, firmware, and control architecture. Qualifications:Deep expertise in robotics, motion, and real-time control of complex electromechanical systems.Proven experience taking platforms from prototype to reliable operation.Strong mechanical, electrical, and firmware engineering skills.Builder mindset with a drive for quality and novel capability creation.

Role: Senior Digital Twin ML EngineerSalary: upto $250,000Location: San Francisco, CA Work on advanced AI-driven physical systems with broad manipulation and experimental capability. I’m seeking a Senior Digital Twin ML Engineer to build high-fidelity digital twins of robotic, electromechanical, and experimental platforms. You will design model-identification pipelines, calibration routines, dynamic-model learning systems, and multi-scale physics representations that support accurate predictive simulation and closed-loop interaction with RL, planning, and control stacks. This role blends physics intuition, ML modeling, and hands-on experimentation to ensure digital twins remain stable, accurate, and continuously updated as real systems evolve. Responsibilities:Build model-identification and parameter-estimation pipelines with adaptive calibration.Develop ML-based dynamic models, multi-scale physics approximators, and hybrid simulation frameworks.Maintain twin fidelity, stability, and version consistency as data and hardware change.Work closely with simulation, RL, controls, and agent teams to integrate twins into decision-making and learning workflows. Qualifications:Strong experience creating or calibrating digital twins or dynamic, data-driven physics models.Knowledge of system identification, time-series modeling, and physical parameter estimation.Ability to combine physics, ML, and experimental data into robust predictive models.Comfort operating across ML, simulation tooling, and physical hardware interfaces in a fast-paced environment.

Our client is building advanced AI systems with real physical capability. Their work spans experimentation, engineering and automated manufacturing, and they have already delivered large scale projects in the public and private sector. This is a team that invents from first principles and builds end to end systems that push the frontier of physical AI.They are now searching for a Senior ML Infrastructure / MLOps Engineer to design, operate and scale the backbone that powers large model development. Your work will shape the training, fine tuning and deployment infrastructure across LLMs, RL agents and physics-driven surrogate models.The roleYou will own the systems that enable large scale training, RLHF and DPO workflows, dataset governance, experimentation, reproducibility and model deployment. This includes distributed training design, containerized model runners, data and versioning pipelines, and evaluation automation that keeps model development reliable and fast.ResponsibilitiesBuild and maintain scalable infrastructure for training, fine tuning and distributed ML workflows.Develop dataset pipelines, versioning systems, experiment tracking and reproducibility frameworks.Operate containerized training and inference environments, including CI/CD for models and evaluation tooling.Partner closely with researchers, RL teams, data engineering and systems engineers to support rapid iteration and robust deployment.What they’re looking forStrong experience in ML infrastructure, distributed training, experiment management or production ML systems.Comfort with containerization, orchestration, dataset governance and model evaluation pipelines.Ability to design reliable, high throughput training and deployment workflows.Someone who enjoys working across ML, infra and data systems in a fast moving research environment.

Senior RL Research Scientist / Reinforcement Learning ScientistJoin a frontier AI team building systems that can act in the physical world, experimenting, optimizing, and controlling real processes through advanced ML, simulation, and automation. This group is pushing the boundaries of physical intelligence, backed by significant long-term funding and a mandate to invent from first principles. If you want to:Work on problems few teams in the world can touchBuild RL systems that power real tools, workflows, and scientific processesOperate in a fast, high-ownership, deeply technical culture…this is the kind of role that defines a career. The Role You’ll design and deploy reinforcement learning systems that control complex tools, optimize multi-step processes, and operate across high-fidelity simulations and digital twins. Expect hands-on research, real-world experimentation, and tight collaboration with teams across ML, simulation, and systems engineering. What You’ll DoBuild RL environments for tool control, workflow optimization, and long-horizon decision-makingDevelop safe and constrained RL methods, verifier-driven rewards, and offline to online training pipelinesCreate state/action representations and evaluation frameworks for reliable policy behaviorWork with cross-functional researchers and engineers to deploy RL agents into real workflowsWhat You BringStrong background in RL, optimal control, or sequential decision-makingExperience applying RL to complex simulated or physical systemsFamiliarity with safe/constrained RL, verifiers, or advanced evaluation pipelinesAbility to design environments, rewards, and diagnostics at scaleComfort working across ML, simulation, and systems interfaces

Senior LLM Research ScientistA frontier-stage research group is building a new class of AI systems designed to reason, plan, and act across the physical world. Their mission is to create intelligent agents capable of experimenting, engineering, and constructing in ways that dramatically accelerate scientific and industrial progress. This team combines deep technical pedigree with real-world wins at scale, including major government-funded initiatives. They operate where advanced model research meets robotics, simulation, and automated engineering systems, offering the kind of impact only possible when first-principles science meets ambitious execution. Joining means stepping into a high-ownership environment where you shape core capabilities end-to-end, influence the direction of physical-world intelligence, and help build technology the world has never seen before. Why This Role Is CompellingWork on cutting-edge reasoning, planning, and tool-use models that directly control autonomous engineering systems.Push the limits of SFT, RLHF, DPO, verifier-guided RL, and long-horizon planning in a setting where your research immediately translates into real-world capability.Operate in a high-velocity research culture with exceptional peers across agent systems, simulation, data, and complex toolchains.Have outsized ownership in a small team tackling one of the most ambitious technical problems of this decade.Role Overview The team is looking for an LLM Research Scientist to pioneer next-generation reasoning and agent architectures. Your work will span model design, alignment strategies, structured tool orchestration, and experimentation with agents interacting across real engineering workflows. This position blends deep research with hands-on systems integration, offering both autonomy and scope to lead foundational progress. Key ResponsibilitiesDevelop advanced models and prompting systems for planning, multi-step reasoning, and structured tool use.Lead training initiatives across SFT, RLHF/DPO, verifier-guided RL, and modular expert architectures to strengthen robustness and controllability.Define schemas, tool-calling strategies, policy constraints, safety mechanisms, and recovery pathways for agent behavior.Partner closely with engineering, simulation, and data teams to test, train, and evaluate models embedded in real production-like toolchains.QualificationsSignificant experience in LLM research, agent reasoning models, or structured tool-use frameworks.Strong background working with SFT, RLHF, DPO, or reinforcement-learning-from-verification methods.Demonstrated ability to design, analyze, and improve long-horizon behaviors and decomposition strategies.Comfortable working across ML research, systems engineering, and real-world experimentation in a fast-moving environment.A track record of excellence and ownership in technically demanding domains.

Senior Agentic AI EngineerA frontier AI company is building systems that can act in the physical world, experimenting, engineering, and executing multi-step processes with real-world constraints. Backed by major research funding and operating at the edge of physical-AI innovation, they’re creating capabilities that don’t exist anywhere else. Join to work from first principles, own high-impact systems end-to-end, and help define how agentic AI will operate complex workflows in the real world. Why This Role MattersBuild agent systems that plan, execute, and recover across intricate engineering workflowsShape foundational behaviour patterns for next-gen LLM tool-useJoin early enough to influence architecture, culture, and performance standardsWork on problems that sit far beyond typical “LLM app” engineeringWhat You’ll DoDevelop planners, state machines, and tool-calling flows using frameworks like LangGraphCreate schemas, action definitions, and cross-tool interfaces for reliable, traceable executionBuild error-handling, timeouts, retries, rollbacks, and replay mechanismsPartner with ML, infra, and systems teams to integrate agents into real engineering toolchainsWhat You BringStrong experience with agent systems, structured tool calling, or orchestration frameworksDeep intuition for schemas, deterministic execution, and multi-step workflow designAbility to model failure modes, edge cases, and safe interactions in complex systemsComfort working across AI, systems engineering, and specialised domain tools in a high-precision environment

Foundation Model AI Architect (Molecular & Multimodal Systems)Our client is exploring a new generation of AI architectures grounded in principles from computational neuroscience, biological computation, and multimodal modelling. Their aim is to build large foundation models capable of reasoning over molecular, structural, and scientific datasets with explainability and precision.They are hiring a Foundation Model AI Architect to lead the design of advanced neural systems that combine transformer architectures, causal reasoning models, multimodal representations, and agentic behaviours. You will design models that integrate chemical data, molecular structures, spectroscopic signatures, and simulation derived information into unified AI systems for materials discovery.A key component of this role involves scaling models on high performance GPU clusters, optimising training and inference pipelines, and working with advanced frameworks such as JAX. You will also build automated labelling systems, behavioural encoding workflows, and interpretable ML pipelines that support transparency and scientific trustworthiness.This position suits someone who can translate ideas from systems neuroscience and complex biological modelling into practical, engineered AI architectures for real scientific problems.Ideal Profile:PhD in computational neuroscience or computational biology, deep expertise in neural architecture design, strong GPU/HPC programming skills, and experience developing large scale or multimodal foundation models.

Product Leader (Scientific Computing & AI Platform)Our client is building a sophisticated scientific platform that integrates automated quantum simulation, high throughput data workflows, and advanced machine learning for molecular and materials discovery. They are seeking a Product Leader to shape how these systems evolve and to guide the infrastructure that enables scientists and ML researchers to work seamlessly.You will define the long-term product vision, design roadmap milestones, and oversee how simulation tools, data pipelines, and AI models come together into a cohesive ecosystem. This includes responsibility for architecting automated DFT pipelines, real time inference systems, continuous integration frameworks, data streaming layers, and evaluation tooling for large scientific models.You will work across teams of computational chemists, simulation scientists, ML researchers, and software engineers to ensure the platform supports fast experimentation and high reliability. You will help shape neural architectures trained on molecular data, from GNNs to transformer-based models, and guide the integration of physics-based domain expertise into core AI workflows.This role requires strategic thinking, technical fluency, and comfort balancing scientific constraints with product execution. You will play a central role in defining the infrastructure that accelerates materials research across the organisation.Ideal Profile: PhD in a computational field, strong experience leading ML or scientific computing product systems, familiarity with automated quantum simulations, and deep understanding of large-scale AI and data tooling.