HEY,
BOSTON

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.

Senior MLOps / ML Infrastructure Engineer About the Company Our client is a Series B, venture-backed deep-tech company building a Physics AI platform that helps engineering teams bring products to market faster, reduce development risk, and explore better designs with greater confidence. The platform combines large-scale simulation data with modern machine learning to generate high-fidelity predictions of physical behavior in near real time. Customers include leading organizations across aerospace, automotive, and advanced manufacturing, working on some of the most demanding real-world engineering problems. The Role This role focuses on building and operating the infrastructure that powers physics-based AI systems at scale. The position enables ML engineers and scientists to train, track, deploy, and monitor models reliably without managing low-level infrastructure. The work sits at the intersection of ML systems, cloud infrastructure, and large-scale simulation data, with a strong emphasis on performance, reliability, and developer productivity. It is a hands-on engineering role in a fast-moving, in-office environment, working closely with ML researchers, platform engineers, and product teams. What You’ll DoDesign, build, and maintain robust MLOps infrastructure supporting the full ML lifecycle, from experimentation and training through to production deployment and monitoringImplement automated training pipelines, experiment tracking, and model lifecycle management using tools such as Kubeflow, MLflow, and Argo WorkflowsDevelop scalable data pipelines capable of handling large volumes of unstructured data, particularly 3D geometric data and physics simulation outputsDeploy machine learning models into production inference systems with strong standards for performance, reliability, and observabilityManage model registries and integrate them with CI/CD workflows to support consistent and reliable model releasesImplement monitoring systems that continuously track model health and performance in productionCollaborate closely with ML researchers, platform engineers, and product teams to evolve the infrastructure platform for physics-based AI applicationsWrite production-grade code and optimize cloud infrastructure, primarily on Google Cloud Platform, while making thoughtful trade-offs around scalability, cost, and operational simplicity using Docker and KubernetesWhat We’re Looking ForBachelor’s degree or higher in Computer Science, Data Science, Applied Mathematics, or a closely related field5 years of industry experience building MLOps platforms or ML systems in production environmentsStrong proficiency in Python, with working knowledge of BASH and SQLHands-on experience with cloud infrastructure such as GCP, AWS, or AzureExperience with containerization and orchestration tools including Docker and KubernetesFamiliarity with modern MLOps frameworks such as Kubeflow, MLflow, and Argo WorkflowsExperience building and maintaining scalable data pipelines, ideally working with unstructured or high-dimensional dataAbility to independently deploy models and implement monitored inference systems in productionComfortable troubleshooting complex distributed systems and building reliable infrastructure that other teams depend onNice to HaveInterest in physics simulation, scientific computing, or HPC environmentsExperience building production MLOps platforms in deep-tech or simulation-heavy environmentsFamiliarity with additional programming languages such as Go or C Working Style and Culture This role suits someone who enjoys startup environments, learns quickly, and communicates clearly across disciplines. The team works on-site five days a week and values close collaboration, fast feedback loops, and hands-on problem solving. There is a strong belief that great infrastructure should be largely invisible, enabling engineers and scientists to move faster without friction.

Founding Machine Learning Research Engineer (Evaluation & Model Iteration Focus) Location: Bay Area Onsite We’re working with a pioneering stealth-stage company in the Bay Area that is redefining how AI is evaluated in healthcare.   Founded by ex-Stanford AI Lab researchers, ex-AWS, with deep expertise in representation learning and working on LLM interpretability.  We are looking for a Founding ML Engineer to: Lead investigations into model behavior, failure modes, and uncertaintyDeliver decision-grade evidence that informs FDA submissions and hospital adoptionWork directly with medical imaging vendors and hospitalsCombine hands-on ML skills with strong customer-facing judgment  To succeed in this role, we're looking for a genuine interest in rigorous evaluation/testing of ML systems, especially in medical AI.  This is a high-impact, high-ownership role, your work will directly influence real-world outcomes, FDA approvals, and how high-stakes AI is governed.  Compensation includes competitive salary $200k - $250k   meaningful early-stage equity (1–3%).  If this sounds like something you’d be excited about, please apply with your resume and we can set up a quick conversation to share more details.

Speech Algorithm Engineer (Speech LLM / SpeechLLM)$150,000 - $250,000San Francisco, Hybrid 3x per week in officeFull time / PermanentAbout the Role This company is already profitable, growing fast, and used by over 1.5M professionals globally. Revenue is tracking at ~$250M in under three years. The product works and is highly marketable, the next step is making its speech system significantly more accurate across languages, industries, and real-world conversations. We’re hiring a speech algorithm engineer to improve speaker diarization and keyword recognition in productio. This is applied, high-impact work that ships. What You’ll DoImprove speaker diarization and multi-language speech recognition accuracy in real customer conversationsDesign and optimize hotword and terminology recognition systems for industry-specific use casesFine-tune and train large speech models on substantial audio datasetsBuild clear evaluation frameworks to measure keyword accuracy and speaker separation performanceCompare open-source and commercial ASR systems and push performance beyond themWork closely with product and engineering to deploy models into live systems used dailyWhat “Great” Looks LikeYou’ve trained or fine-tuned speech models on large-scale datasets (not small research-only projects)You understand how speech systems behave in noisy, real-world conditionsYou’ve improved measurable production metrics (accuracy, diarization quality, keyword recall)You can read research and turn it into working systemsYou take ownership when performance drops Notable: If your experience is limited to light experimentation or purely academic research without production exposure, this likely won’t be a fit. Why JoinProfitable company at ~$250M run rateHybrid San Francisco team building both hardware and AI systemsReal ownership and visibility, not one engineer in a large orgGlobal product scale and meaningful datasetsClear growth path toward senior technical leadership as the audio function expandsStrong data security and compliance standards, this is enterprise-grade infrastructure

Senior Agentic AI Engineer$250,000 - $350,000San Francisco, Hybrid 3x per weekPermanent / Full-timeOver 1.5 million professionals already rely on this product in their daily work. The company is profitable, scaling fast, and redefining how humans interact with AI through a tightly integrated hardware & software platform. The next phase is all about building real-time AI agents that can listen, reason, coordinate tools, and take action. Reliability and scalability in production. I’m hiring a Senior Agent Engineer in San Francisco to help design and ship the multi-agent systems behind that vision. What You’ll DoDesign and build multi-agent workflows that reason, plan, and use tools in real timeDevelop core runtime systems like memory, context management, orchestration, and tool routingWork closely with product and hardware teams to ship AI-driven features into live devices and applicationsMigrate previous systems into a more scalable, agent-based architectureImprove internal SDKs, developer tooling, and deployment pipelines to accelerate shippingWhat “Great” Looks LikeYou’ve built and operated distributed backend systems that actually run in productionYou’ve worked with LLM frameworks and understand how agents break in the real worldYou can design clean architectures without overcomplicating themYou care about latency, reliability, and cost as well as qualityYou take ownership. You don’t wait to be told what to fix.This role is not a fit if your experience is primarily academic, experimental, or limited to prompt tweaking without owning production systems. Why JoinProfitable company with ~$250M revenue run rate achieved in just three yearsReal scale: millions of users, global distribution, live hardware AI systemsSmall, high-talent engineering team with meaningful ownershipWork that directly shapes the future of human–AI interactionClear path toward senior technical leadership as the agent platform becomes core infrastructure