Sam Warwick

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 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.