Sam Warwick

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.

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.

Cell Product Design EngineerOur client is developing high performance Li ion pouch cells for electric mobility, micromobility, and grid scale energy systems. As part of their continued expansion, they are looking for a Cell Product Design Engineer who can take full ownership of design, validation, and New Product Introduction (NPI) for next generation pouch cells.In this role you will lead the structural, electrochemical, and mechanical design of NCM based pouch cells. You will drive the entire lifecycle from concept design and form factor definition, through prototyping, validation, safety testing, and eventual mass production readiness. You will work across interfacial design, separator and electrolyte considerations, formation strategies, and internal architecture to meet performance, cost, and durability targets.You will collaborate closely with cross functional R&D teams, manufacturing engineers, test engineers, and program leads to ensure proper DFMEA, DVP&R, and quality processes are embedded throughout the design cycle. Your work will support products ranging from EV platforms to e bikes and stationary storage systems, requiring strong judgement, technical rigour, and the ability to manage complex tradeoffs.Ideal Profile: Degree in Materials Science or Chemical Engineering, hands on experience in pouch cell development and NPI, strong command of validation and quality methodologies, and the ability to bridge R&D and manufacturing environments.

Battery Product Development & Client Engagement LeaderOur client is a leader in advanced battery innovation, combining materials science, electrochemistry, and AI guided discovery to support global OEMs across mobility, energy storage, and electrification. As they scale international partnerships, they are seeking a technical leader to guide customer facing product development.This role blends technical ownership with client engagement. You will lead strategic programs with major OEM partners, define requirements for next generation battery technologies, and translate complex R&D findings into products that are manufacturable, scalable, and validated for demanding applications. You will guide the product roadmap, shape technical milestones, and coordinate tightly with R&D, materials engineering, testing, and manufacturing teams.Your work will span concept development, prototype definition, validation planning, DOE execution, safety analysis, failure mode assessment, and oversight of the lab to prototype to customer qualification pipeline. This is an opportunity to influence real world deployment of advanced battery systems and play a central role in aligning future technology with commercial needs.Ideal Profile:PhD in Chemical Engineering or Materials Science, strong background in electrochemistry and battery engineering, leadership experience with OEM programs, and expertise in process optimisation, failure analysis, and product scaling.

AI Product Engineering Scientist(Scientific Computing Platform Architect & AI Scientist) Our client is a rapidly advancing frontier company building a unified platform that merges high performance simulation, molecular modelling, and AI driven materials discovery. Their work sits at the crossroads of computational chemistry, physics-based simulation, and software engineering, supporting the discovery of next generation battery materials with measurable real-world impact. They are hiring a senior AI Product Engineering Scientist to lead the design and development of their scientific computing infrastructure. This position is ideal for someone who thrives across both algorithmic science and engineered systems. You will architect end to end platforms that automate MD and phase field simulations, integrate ML force fields, and unify AI models with large scale HPC environments. You will be responsible for building scalable APIs, developing simulation toolchains, enabling cloud and cluster integration, and ensuring scientists and engineers have a robust and performant environment for rapid iteration. The work is highly cross functional, partnering with simulation teams, ML researchers, and software engineers to turn research concepts into production ready tools that support rapid materials exploration. Ideal Profile:PhD in a quantitative discipline, deep experience in MD and physics-based simulation, strong HPC fluency (C and Python), and proven experience architecting scientific or ML driven platforms.

Distributed Systems EngineerSan Francisco, CA (Onsite)About the CompanyA fast-moving AI research group is building the core video data infrastructure used by leading AI labs and major tech companies. The team is small at around fifteen people, nearly all engineers, and recently pivoted to focus exclusively on high-quality video data at massive scale. The shift has driven significant revenue growth, and they are now planning to expand the team steadily over the next few months.The culture is straightforward: engineering led, product focused, low ego, and built around people who enjoy ownership. They work in person five days a week in their San Francisco office, moving quickly, solving hard problems, and avoiding micromanagement.The RoleThis position focuses on designing and scaling distributed systems that support huge ML and ETL workloads across petabytes of video. You will own core infrastructure: compute scheduling, orchestration, throughput, reliability, cost efficiency, and the internal tooling that keeps the entire engineering group moving at pace.The company is beginning to scale its infrastructure footprint aggressively, and this role will become central to that growth. It is a hands-on IC position suited to someone who has operated critical systems before and wants to shape the foundation of a rapidly expanding platform.What You’ll Work On• Architect and scale distributed systems for large-scale ML and ETL workloads• Build compute orchestration and scheduling across thousands of GPUs• Improve uptime, resilience, and execution speed of high-volume data pipelines• Design pipelines capable of handling petabyte-level video datasets• Lead the development of CI/CD and internal tooling for fast iteration• Partner closely with research engineers delivering new video models and algorithms• Operate in a high-trust environment with strong autonomy and clear ownershipRequirements• 3+ years building foundational distributed systems or data infrastructure• Experience running critical systems at significant scale• Proficient across cloud architectures• Strong coding experience with Go (preferred) and Python• Background building or maintaining large-scale pipelines• Experience with ML-focused CI/CD and automation• Video domain experience is not required• Operates as a strong IC who leads through action• Fully onsite in San Francisco, Monday to FridayCulture Fit• Enjoys ambiguity, problem discovery, and self-direction• Communicates clearly and concisely• Shows strong intellectual curiosity• Low ego, collaborative mindset• Motivated by building core systems in a small, high-caliber teamRed flags include weak communication, low curiosity, or unclear motivation for the domain.Interview ProcessIntro call focused on culture, curiosity, and communicationTechnical discussion on background and complexity of past workProblem-solving session with a research engineerOnsite research problem and collaboration exercise

Senior Computational Materials ScientistAbout the CompanyA global energy-technology organization developing next-generation Li-Metal batteries for electric mobility across automotive, aviation, and advanced energy applications. This team integrates modern machine learning directly into materials R&D, cell design, manufacturing workflows and safety analytics, operating across major hubs in North America and Asia.About the Advanced Computation DivisionThis group serves as the company’s core AI and computational science unit. It brings together computational materials scientists, software engineers and machine learning researchers working hand-in-hand with experimental chemists and product engineers. The team builds intelligent scientific tooling, accelerates materials discovery and supports fast iterative R&D.About the Molecular Discovery PlatformThe company’s flagship platform for AI-accelerated materials discovery analyzes more than 10^8 small molecules across quantum-level, ML-derived and experimentally curated properties. Leveraging GPU-accelerated simulation, large-scale automation and advanced visualization, it enables rapid navigation across vast chemical space.About the RoleThe team is seeking a Senior Computational Materials Scientist to contribute to the development of this platform while advancing simulation capabilities for electrolyte systems, solid electrolyte interphase (SEI) modeling, reaction network methods, force field development and large-scale molecular dynamics acceleration on modern HPC infrastructure.You will collaborate across computation, software, AI and experimental groups to develop tools that connect quantum chemistry, statistical mechanics and machine learning for practical molecular design.Key ResponsibilitiesDesign and execute large-scale quantum chemistry and molecular dynamics simulations using industry-standard tools (e.g., GPU4PySCF, GROMACS, LAMMPS, Gaussian).Develop and refine force fields and interatomic potentials for electrolyte-relevant chemistries.Build and improve simulation workflows for SEI formation, including reaction network analysis and atomistic modeling.Contribute to property-calculation workflows covering key quantum descriptors (HOMO, LUMO, ESP), thermodynamics and kinetics.Automate high-throughput simulation pipelines using Python, HPC schedulers (e.g., SLURM) and distributed compute environments.Integrate new simulation capabilities into the broader molecular discovery platform through APIs or modular Python packages.QualificationsRequiredPhD in Materials Science, Chemistry, Chemical Engineering, Physics or a closely related discipline5+ years of post-PhD experience in computational chemistry or computational materials scienceHands-on experience with major molecular simulation packages (GROMACS, LAMMPS, Gaussian, VASP, Quantum Espresso, ADF, GPU4PySCF or similar)Strong Python skills, including scientific libraries (NumPy, ASE, PySCF etc.) and experience writing reproducible research-grade codeExperience with high-throughput computation and large-scale data workflows on HPC or GPU clustersStrong communication skills and comfort working across experimental, computational and AI teamsPreferredExperience with battery materials, electrolyte systems or solid/liquid interface modelingBackground in force-field development, reactive MD (Polarizable FF, ReaxFF, MLFF) or coarse-grained simulationFamiliarity with cheminformatics concepts (molecular representations, fingerprints, exploration of chemical space)Contributions to open-source simulation frameworks or published methodology papersExperience with unsupervised learning methods (dimensionality reduction, clustering beyond k-means)Exposure to CUDA or GPU-accelerated codingWho Thrives HereYou enjoy working at the intersection of chemistry, physics, ML and large-scale computationYou’re comfortable challenging the limits of standard computational toolsYou have a natural curiosity for molecular behavior, electrolyte chemistry and computational designYou prototype quickly, iterate thoughtfully and value reproducible scientific workflowsYou like building tools that turn raw simulation output into interactive, research-ready platforms