Sam Warwick

BMS & AI Edge Software Engineer Battery Systems | AI for Science | Energy Storage Our client is a publicly listed, AI driven energy technology company operating at the intersection of advanced materials science, battery engineering, and machine learning. Their mission is simple but ambitious: accelerate the global energy transition by using AI to fundamentally change how batteries are designed, validated, and operated. They are pioneers in applying AI directly to battery chemistry, materials discovery, and battery management systems, enabling next generation Li ion and Li metal batteries across transportation, energy storage, robotics, aviation, and defense adjacent applications. The Opportunity Our client’s Energy Storage Systems R&D group is seeking a BMS & AI Edge Software Engineer to design and deploy AI centric State of X (SoX) algorithms that run on edge devices. This role sits squarely between battery physics, embedded software, and applied machine learning. You will own algorithm development from concept through edge deployment, working closely with battery scientists, hardware engineers, and customer facing teams to bring production ready software into real world environments. Key Responsibilities Algorithm R&D for SoXDesign and implement SoX architectures covering charge, health, power, safety, degradation, and related metricsTranslate models and logic into production grade code running on edge devicesCollaborate with battery physicists and engineers on model selection and validationModel Design & OptimizationResearch and evaluate alternative algorithms to improve accuracy, robustness, and performanceOptimize models and software for real world operating constraintsPresent results internally and demonstrate measurable improvementsVerification & DeliveryTest and validate software as a production ready product using defined methodologiesSupport validation at customer sites or manufacturing plants as requiredEngage directly with customers to support deployment and technical approvalRequirements EducationPhD or Master’s in Electrical Engineering, Computer Science, AI, or a closely related fieldEquivalent hands on industry experience will be consideredExperience5 to 9 years of experience in Li ion batteries, BMS, or ESS software engineering (10 years for Senior level)Strong background in BMS sensing and control software including voltage, temperature, current, and diagnosticsSolid understanding of battery chemistries and characteristics such as OCV, C rate behavior, and impedanceExperience developing data driven or AI based algorithms for battery systems, ideally deployed on edge or cloudProven experience coding, integrating, validating, and delivering production softwareExposure to customer facing delivery or deployment projectsPreferred BackgroundBattery characterization methods such as GITT, dQ/dV, or similarPower electronics knowledge including DC/DC or DC/AC conversionFamiliarity with power delivery architectures such as UPS or battery backup systems for data centersWhat’s On OfferHighly competitive base salary and strong benefitsMeaningful equity participation in a publicly listed businessDirect impact on globally relevant energy and sustainability challengesWork alongside leading experts in AI, battery science, and engineeringLong term growth opportunities in a technically serious R&D environment

Senior AI Solutions Engineer – SalesInsurtech | Generative AI | Enterprise InsuranceAbout Our ClientOur client is a Series B–backed AI company transforming the property and casualty insurance industry through applied generative AI and intelligent automation. They work directly with large insurers to modernise core workflows, reduce operational cost, and improve customer experience using production-grade AI agents deployed across claims, policy, and service operations.They are scaling rapidly and are now hiring a senior, customer-facing AI Solutions Engineer to sit at the intersection of sales, product, and deployment.The RoleThis is a high-impact, technical commercial role. You will act as the technical authority throughout the customer lifecycle, from pre-sales through implementation and expansion.You will work closely with sales, customer success, and engineering teams to translate insurer requirements into robust, secure, and scalable AI solutions that deliver measurable business outcomes.This role suits someone comfortable owning conversations with CIO, CTO, and enterprise IT stakeholders while remaining hands-on with architecture, integrations, and demos.What You’ll DoPre-Sales & Technical DiscoveryPartner with sales teams to scope insurer requirements and design AI-driven solutionsPresent system architecture, security posture, and integration approaches to senior technical stakeholdersBuild tailored demos, proofs of concept, and solution prototypes aligned to insurer environmentsSolution Design & ImplementationAssess insurer core platforms including policy administration, claims, and billing systemsDesign and lead integrations across APIs, SSO, and workflow orchestrationSupport and guide deployments to ensure smooth onboarding and production readinessCustomer Advisory & ExpansionAct as a trusted technical advisor for insurer clients adopting AI at scaleIdentify opportunities for expansion across advanced AI agents, workflows, and APIsFeed customer insights back into product and engineering teams to influence roadmap prioritiesThought LeadershipRepresent the business in client workshops, technical sessions, and industry eventsProduce technical documentation, integration guides, and solution playbooks for enterprise customersWhat We’re Looking ForMust Have7–10 years in solutions engineering, pre-sales architecture, or enterprise technical consultingDeep experience within insurance technology ecosystems such as Guidewire, Duck Creek, Majesco, One Inc., Insuresoft, or SapiensStrong hands-on experience with APIs, cloud platforms (AWS, Azure, GCP), and enterprise integrationsProven ability to lead complex, multi-stakeholder enterprise implementationsExcellent communication skills with the ability to explain complex technical concepts to non-technical executivesComfortable operating in a fast-moving, high-ownership startup environmentNice to HaveExposure to AI, ML, automation platforms, or NLP-driven systemsBackground working in high-growth startups or insurtech vendorsFamiliarity with insurance compliance, data security, and regulatory frameworks

Product Developer – GeoAI & Spatial IntelligenceLocation: Europe (hybrid or remote options available)Level: Mid to SeniorAbout our client:Our client is a Swiss-Italian deep-tech company focused on applying machine learning to large-scale spatial data. By combining satellite imagery, mobility data, and crowd-sourced mapping, they deliver actionable intelligence across sectors including urban management, transportation, infrastructure, real estate, insurance, marketing technology, and socio-demographic analysis.They work at the intersection of Earth Observation, AI, and real-world decision-making, helping public and private organisations turn complex geospatial data into practical outcomes.The role:Our client is looking for a Product Developer to help shape the next generation of GeoAI-powered products. This role sits at the boundary between research, engineering, and the market.You will analyse past and ongoing projects, identify where research outputs can be transformed into scalable, market-ready products, and help define the product strategy moving forward. You will also assess market needs across multiple sectors and guide the innovation roadmap accordingly.This is a hands-on, cross-functional role requiring both strategic thinking and practical execution.Key responsibilities:Work closely with data scientists, engineers, researchers, and commercial teams to design and evolve GeoAI products.Translate complex AI, Earth Observation, and urban analytics outputs into clear, user-focused products.Conduct market analysis to identify unmet needs, sector gaps, and high-impact opportunities.Evaluate whether existing capabilities can solve identified problems, and define enhancements where needed.Define new products when market needs are not addressed by current solutions, ensuring feasibility and strategic alignment.Build and maintain a clear product roadmap prioritised by impact, value, and user needs.Gather and interpret feedback from users, clients, and market research to drive continuous improvement.Track product performance post-launch and recommend refinements or new initiatives.Required background:Bachelor’s or Master’s degree in Engineering, Data Science, GIS, Environmental Science, Business, Marketing, Product Management, or a related discipline.5 years of experience in product strategy or product development involving geospatial data.Strong problem-solving ability, with a track record of turning research, technical concepts, and market insight into concrete product plans.Solid understanding of software development lifecycles, data visualisation, and UX/UI principles.Comfortable working in fast-moving, multidisciplinary teams and with external stakeholders.Familiarity with remote sensing or urban analytics is required.Experience with AI, cloud platforms, geospatial tooling, or mapping libraries is a strong advantage.Why this opportunity:Join a fast-growing international deep-tech company operating at the forefront of geospatial AI.Work on real-world problems with tangible impact on cities, infrastructure, and society.Collaborate with leading public and private institutions on globally relevant projects.Flexible working environment with a strong emphasis on learning, creativity, and innovation.

Senior MLOps Engineer – GPU Infrastructure & Inference Our client is building AI-native systems at the intersection of machine learning, scientific computing, and materials innovation, applying large-scale ML to solve complex, real-world problems with global impact. They are seeking a Senior MLOps Engineer to own and operate a production-grade GPU platform supporting large-scale model training and low-latency inference for computational chemistry and LLM workloads serving thousands of users. This role holds end-to-end responsibility for the ML platform, spanning Kubernetes-based GPU orchestration, cloud infrastructure and Infrastructure-as-Code, ML pipelines, CI/CD, observability, reliability, and disaster recovery. You will design and operate hardened, multi-tenant ML systems on AWS, build and optimize high-performance inference stacks using vLLM and TensorRT-based runtimes, and drive measurable improvements in latency, throughput, and GPU utilization through batching, caching, quantization, and kernel-level optimizations. You will also establish SLO-driven operational standards, robust monitoring and alerting, on-call readiness, and repeatable release and rollback workflows. The position requires deep hands-on experience running GPU workloads on Kubernetes, including scheduling, autoscaling, multi-tenancy, and debugging GPU runtime issues, alongside strong Terraform and cloud-native fundamentals. You will work closely with research scientists and product teams to reliably productionize models, support distributed training and inference across multi-node GPU clusters, and ensure high-throughput data pipelines for large scientific datasets. Ideal candidates bring 5 years of experience in MLOps, platform, or infrastructure engineering, strong proficiency in Python and modern DevOps practices, and a proven track record of operating scalable, high-performance ML systems in production. Experience supporting scientific, computational chemistry, or other physics-based workloads is highly desirable, as is prior exposure to large-scale LLM serving, distributed training frameworks, and regulated production environments.

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.

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.