HEY,
BOSTON

Senior Data Engineer - HealthTech$150,000 - $200,000  Fully RemoteFull time / Permanent A fast-growing healthcare technology start up is looking for a Senior Data Engineer to join their team. They are actively working with large healthcare organizations across the U.S, helping them get more value from their data through AI-powered clinical tools. This work has a direct impact on how care is delivered and the data teams sit at the centre of that. This is a hands-on role. You will be building and owning data pipelines that feed real AI systems used by clinicians every day. Why JoinSmall, collaborative teams where senior engineers have real ownershipClear progression to director. The company is growing and promotes from withinCompetitive base salary, equity, flexible hours, and strong benefitsWork that has a tangible impact on patient care across the U.S.The Role You will join a small team responsible for integrating data from large healthcare organisations into a modern cloud data platform. Day to day, you will be building pipelines, validating data quality, and making sure the right data reaches the right systems reliably and on time. You will also contribute to shared tools and frameworks that make future integrations faster, work that scales well beyond your individual projects. What We're Looking For4 years of experience building production data pipelinesStrong SQL skills across large, complex datasetsProficiency in Python for data transformationExperience with cloud-based data platforms and distributed processing toolsComfortable working with healthcare data formats and standards, or willing to learn quicklyExperience with Azure cloud servicesUseful but not essential:Background in healthcare data or EHR systemsExperience with modern lakehouse architecturesExposure to real-time data pipelines or message-based data feedsExperience in a SaaS or multi-tenant environmentTech Stack Azure Data Factory - Databricks - Python - SQL - PySpark - CI/CD tooling - Healthcare data standards What Success Looks LikePipelines delivered on time, well tested, and clearly documentedData quality issues caught early — before they reach productionReusable components that speed up future workStrong working relationships with partner technical teamsNoteworthy - This is a full-time / permanent role and cannot be considered for C2C, C2H, or any other temporary contracts.

Senior Data Engineer - HealthTech$150,000 - $200,000  Hybrid - Houston, TX Full time / Permanent A fast-growing healthcare technology start up is looking for a Senior Data Engineer to join their team. They are actively working with large healthcare organizations across the U.S, helping them get more value from their data through AI-powered clinical tools. This work has a direct impact on how care is delivered — and the data teams sit at the centre of that. This is a hands-on role. You will be building and owning data pipelines that feed real AI systems used by clinicians every day. Why JoinSmall, collaborative teams where senior engineers have real ownershipClear progression to director. The company is growing and promotes from withinCompetitive base salary, equity, flexible hours, and strong benefitsWork that has a tangible impact on patient care across the U.S.The Role You will join a small team responsible for integrating data from large healthcare organisations into a modern cloud data platform. Day to day, you will be building pipelines, validating data quality, and making sure the right data reaches the right systems reliably and on time. You will also contribute to shared tools and frameworks that make future integrations faster, work that scales well beyond your individual projects. What We're Looking For4 years of experience building production data pipelinesStrong SQL skills across large, complex datasetsProficiency in Python for data transformationExperience with cloud-based data platforms and distributed processing toolsComfortable working with healthcare data formats and standards, or willing to learn quicklyExperience with Azure cloud servicesUseful but not essential:Background in healthcare data or EHR systemsExperience with modern lakehouse architecturesExposure to real-time data pipelines or message-based data feedsExperience in a SaaS or multi-tenant environmentTech Stack Azure Data Factory - Databricks - Python - SQL - PySpark - CI/CD tooling - Healthcare data standards What Success Looks LikePipelines delivered on time, well tested, and clearly documentedData quality issues caught early — before they reach productionReusable components that speed up future workStrong working relationships with partner technical teamsNoteworthy - This is a full-time / permanent role and cannot be considered for C2C, C2H, or any other temporary contracts.

We are seeking an experimental physicist with strong hands-on experience in atomic, optical, or quantum systems to help build and operate advanced experimental platforms. You will work directly with precision hardware for qubit control, measurement, and system scaling, contributing to the development of next-generation quantum technologies.This is a lab-focused role for someone who enjoys designing experiments, troubleshooting complex setups, and collaborating across disciplines to turn ideas into working systems. Responsibilities Design, build, and characterize optical, vacuum, and/or cryogenic experimental systemsImplement protocols for qubit preparation, control, and readoutIntegrate lasers, RF/microwave systems, control electronics, and data acquisitionAnalyze experimental data and optimize performance and stabilityTroubleshoot hardware and control issues across the full experimental stackCollaborate with engineers and scientists to inform system design and scalingRequirements Ph.D. in Physics, Applied Physics, Electrical Engineering, or related fieldHands-on experience with experimental quantum systems (AMO, solid-state, or superconducting)Familiarity with qubit control, spectroscopy, or precision measurementStrong experimental problem-solving skillsExperience using Python or similar tools for experiment control and analysisA collaborative mindset and clear communication skills

Simulation Engineer Location: Onsite - Bay Area.Company: High-growth AI startup (stealth / early-stage)Focus: Physics-based simulation to ML-driven systemsOverviewOur client is building a new class of AI systems designed to understand and operate within real-world physical environments. The company sits at the intersection of simulation, machine learning, and industrial systems, with a focus on turning high-fidelity simulation data into scalable, production-grade intelligence.They are hiring Simulation Engineers across multiple domains who can bring deep subject-matter expertise and translate complex physical systems into computational models that can be learned, optimised, and deployed. This is not a pure research role. It is for engineers who have built and used simulation systems in real-world environments and understand how those systems behave under production constraints.Key Areas of HiringCandidates should come from one of the following domains:Bioreactors / Bioengineering (top priority)CFD / Fluid Dynamics (medical devices or industrial systems)Aerospace (flight physics, aerodynamics, control systems)Fixed-Wing Drones / UAVsAviation (commercial or defence aircraft systems)Space / Rocket SystemsWhat You’ll DoDevelop and apply high-fidelity simulation models across fluid, structural, thermal, biological, or aerodynamic systemsTranslate simulation outputs into ML-compatible datasets and representationsWork closely with ML and AI teams to enable surrogate modelling, optimisation, and system-level learningImprove simulation performance, scalability, and reliability across large-scale compute environmentsDesign end-to-end pipelines from simulation through to data generation, model training, and deploymentValidate and calibrate models against real-world data where availableWhat They’re Looking ForCore Requirements:Strong background in simulation engineering within a real-world domainExperience with tools such as OpenFOAM, ANSYS Fluent, STAR-CCM , Abaqus, ANSYS Mechanical, COMSOLExperience building or working with custom simulation frameworks (C , Python, MATLAB or similar)Solid understanding of physics-based modelling (fluids, thermodynamics, structural mechanics, control systems, or bio-systems)Experience working with large-scale simulations or HPC environmentsPreferred:Exposure to ML workflows (PyTorch, TensorFlow, surrogate models, optimisation loops)Experience generating or working with synthetic data from simulationsFamiliarity with distributed compute, GPU acceleration, or cloud-based simulation pipelinesBackground in companies such as:Medical Devices: Stryker, Medtronic, Boston Scientific, Zimmer BiometDrones/UAVs: Skydio, DJI, Autel, ParrotAerospace/Aviation: Boeing, Airbus, Joby, defence organisationsSpace: SpaceX, Relativity Space, NASA, Project Kuiper, Muon SpaceWhat Makes This DifferentYou are helping turn simulation into intelligence, not just running modelsDirect exposure to next-generation AI systems grounded in physicsOpportunity to work across multiple industries and problem domainsHigh ownership in shaping how simulation integrates into AI systems for the physical worldIdeal ProfileDomain expert first, not a generalistHas built simulations that informed real-world decisionsComfortable operating in ambiguous, early-stage environmentsInterested in bridging physics and machine learningHiring PriorityBioreactors / Bio-simulation (urgent)CFD / Fluid systemsAerospace / UAVAviationSpace systems

Senior Agentic AI Engineer$300,000 - $400,000Onsite, Palo Alto (Remote for exceptional talent)Full time / PermanentA well-known, frontier GenAI company is undergoing a major product pivot, moving from single-modal generative experiences toward a consumer multi-agent ecosystem designed to feel genuinely autonomous, useful, and alive.They’re building the core infrastructure that will define how millions of users interact with AI agents daily. From planning and execution to memory, creativity, and proactive behaviour. This role sits at the heart of this shift: designing and shipping the systems that make intelligent agents function for 1M users.What You’ll DoDesign and evolve the agent runtime, the core loop handling reasoning, tool use, planning, memory retrieval, and response generationBuild agent capabilities across modalities (e.g. image/video generation, voice interaction, browsing, code execution) and ship themOwn LLM orchestration and model routing across multiple providers, optimising latency, cost, reliability, and qualityImplement memory systems that allow agents to learn from interactions (long-term memory, episodic recall, semantic retrieval)Prototype and productionize autonomous behaviours such as proactive task execution, scheduling, and goal-directed workflowsCreate evaluation frameworks and metrics that measure agent quality, personality consistency, and real user impactWhat “Great” Looks LikeYou’ve personally built and shipped agentic systems, not just prompt wrappers or demosYou’re comfortable owning ambiguous, greenfield problems and turning ideas into working product fastYou think in systems: distributed workflows, multi-step reasoning, orchestration, reliabilityYou code daily and care deeply about performance, UX feel, and real-world usefulness(If you’re looking for a narrowly scoped role, heavy process, or pure research track, then this won’t be the right fit.)Why JoinJoin at a genuine product inflection point, early access launch, new architecture direction, and strong internal momentumWork in a small, elite engineering cohort where each senior hire has outsized ownership and influenceHelp define the company’s next-generation agent platform and model infrastructure from the ground upCollaborate closely with product leadership and shape how consumer AI agents evolve in the real worldClear trajectory toward technical leadership and founding-level impact as the organisation scalesIf you’ve built real agent systems and want to work on problems that don’t have playbooks yet, please apply with your resume!

Senior Machine Learning Infra Engineer | San Francisco | Competitive Salary EquityOur client is an early-stage AI company building foundation models for physics to enable end-to-end industrial automation, from simulation and design through optimization, validation, and production. They are assembling a small, elite, founder-led team focused on shipping real systems into production, backed by world-class investors and technical advisors. They are hiring a Machine Learning Cloud Infrastructure Engineer to own the full ML infrastructure stack behind physics-based foundation models. Working directly with the CEO and founding team, you will build, scale, and operate production-grade ML systems used by real customers. What you will doOwn distributed training and fine-tuning infrastructure across multi-GPU and multi-node clustersDesign and operate low-latency, highly reliable inference and model serving systemsBuild secure fine-tuning pipelines allowing customers to adapt models to their data and workflowsDeliver deployments across cloud and on-prem environments, including enterprise and air-gapped setupsDesign data pipelines for large-scale simulation and CFD datasetsImplement observability, monitoring, and debugging across training, serving, and data pipelinesWork directly with customers on deployment, integration, and scaling challengesMove quickly from prototype to production infrastructure What our client is looking for3 years building and scaling ML infrastructure for training, fine-tuning, serving, or deploymentStrong experience with AWS, GCP, or AzureHands-on expertise with Kubernetes, Docker, and infrastructure-as-codeExperience with distributed training frameworks such as PyTorch Distributed, DeepSpeed, or RayProven experience building production-grade inference systemsStrong Python skills and deep understanding of the end-to-end ML lifecycleHigh execution velocity, strong debugging instincts, and comfort operating in ambiguity Nice to haveBackground in physics, simulation, or computer-aided engineering softwareExperience deploying ML systems into enterprise or regulated environmentsFoundation model fine-tuning infrastructure experienceGPU performance optimization experience (CUDA, Triton, etc.)Large-scale ML data engineering and validation pipelinesExperience at high-growth AI startups or leading AI research labsCustomer-facing or forward-deployed engineering experienceOpen-source contributions to ML infrastructure This role suits someone who earns respect through hands-on technical contribution, thrives in intense, execution-driven environments, values deep focused work, and takes full ownership of outcomes. The company offers ownership of core infrastructure, direct collaboration with the CEO and founding team, work on high-impact AI and physics problems, competitive compensation with meaningful equity, an in-person-first culture five days a week, strong benefits, daily meals, stipends, and immigration support.