HEY,
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Applied AI Engineer (End-to-End ML)Location: Palo Alto, CA (Hybrid)Role Type: Full-Time / PermanentOur client, a pioneering HealthTech AI company in Palo Alto, is seeking a high-calibre Applied AI Engineer to bridge the gap between advanced Machine Learning and robust Software Engineering. This is an end-to-end ownership role: you will be responsible for designing the logic, building the architecture, and deploying the final services. Core ResponsibilitiesArchitect AI Workflows: Design and implement sophisticated agentic workflows and automation sequences that power clinical decision-making.System Design & Integration: Build the backend infrastructure, scalable REST APIs, and data services required to support high-concurrency AI applications.Rapid Deployment: Maintain a high-velocity shipping cycle, moving from prototype to production-grade implementation in days.Model Orchestration: Select, fine-tune, and evaluate the performance of various LLMs (including OpenAI, Anthropic, and open-source models) for specific healthcare tasks.Full-Stack ML: Own the pipeline from data ingestion and time-series forecasting to real-time classification and model monitoring.Technical ProfileComputer Science Mastery: Expert knowledge of algorithms, data structures, and distributed systems.Software-Heavy Background: Professional-grade Python skills. You should be comfortable with software design patterns, testing, and CI/CD.Machine Learning Fundamentals: * Deep understanding of Core ML topics: classification, regression, and clustering.Specific experience in Time Series Forecasting and temporal data analysis.Proficiency in Generative AI: RAG architectures, prompt optimization, and agent frameworks.Infrastructure: Experience deploying services to cloud environments (GCP preferred) and a solid grasp of MLOps and pipeline automation.Education: BS in Computer Science or related field 4 years of experience, or an MS 2 years of experience.Cultural FitStartup Agility: You possess the "scrappiness" to solve problems with limited resources but the rigor to ensure those solutions are enterprise-grade.The "Generalist" Mindset: You enjoy working across the entire stack and are not afraid to dive into data engineering or infrastructure when needed.Mission-Oriented: You are motivated by the prospect of using AI to significantly improve healthcareOur client provides a highly competitive package, including a strong base salary, meaningful equity, and comprehensive premium healthcare benefits. You will join a world-class collaborative team in a hybrid environment in Palo Alto.Please apply for more details

Applied AI Engineer (End-to-End ML)Location: Palo Alto, CA (Hybrid)Role Type: Full-Time / PermanentOur client, a pioneering HealthTech AI company in Palo Alto, is seeking a high-calibre Applied AI Engineer to bridge the gap between advanced Machine Learning and robust Software Engineering. This is an end-to-end ownership role: you will be responsible for designing the logic, building the architecture, and deploying the final services. Core ResponsibilitiesArchitect AI Workflows: Design and implement sophisticated agentic workflows and automation sequences that power clinical decision-making.System Design & Integration: Build the backend infrastructure, scalable REST APIs, and data services required to support high-concurrency AI applications.Rapid Deployment: Maintain a high-velocity shipping cycle, moving from prototype to production-grade implementation in days.Model Orchestration: Select, fine-tune, and evaluate the performance of various LLMs (including OpenAI, Anthropic, and open-source models) for specific healthcare tasks.Full-Stack ML: Own the pipeline from data ingestion and time-series forecasting to real-time classification and model monitoring.Technical ProfileComputer Science Mastery: Expert knowledge of algorithms, data structures, and distributed systems.Software-Heavy Background: Professional-grade Python skills. You should be comfortable with software design patterns, testing, and CI/CD.Machine Learning Fundamentals: * Deep understanding of Core ML topics: classification, regression, and clustering.Specific experience in Time Series Forecasting and temporal data analysis.Proficiency in Generative AI: RAG architectures, prompt optimization, and agent frameworks.Infrastructure: Experience deploying services to cloud environments (GCP preferred) and a solid grasp of MLOps and pipeline automation.Education: BS in Computer Science or related field 4 years of experience, or an MS 2 years of experience.Cultural FitStartup Agility: You possess the "scrappiness" to solve problems with limited resources but the rigor to ensure those solutions are enterprise-grade.The "Generalist" Mindset: You enjoy working across the entire stack and are not afraid to dive into data engineering or infrastructure when needed.Mission-Oriented: You are motivated by the prospect of using AI to significantly improve healthcareOur client provides a highly competitive package, including a strong base salary, meaningful equity, and comprehensive premium healthcare benefits. You will join a world-class collaborative team in a hybrid environment in Palo Alto.Please apply for more details

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

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

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

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.

ML Scientist in AI Explainability  Location: Boston Massachusetts Type: Full time Machine Learning Scientist, AI Explainability and Scientific Discovery We are working with a publicly listed deep tech company operating at the intersection of machine learning, material science, and next generation battery technology. The team is applying AI directly to scientific discovery, with real world impact across energy storage, transportation, robotics, and aerospace. This role sits within an advanced AI research group focused on Large Language Models, AI agents, and explainability in scientific problem solving. Your work will directly influence how new battery materials are discovered and validated using AI. The position can be fully remote. What you will work on You will lead research into machine learning methods for scientific discovery, with a strong focus on multimodal Large Language Models and agent based systems.You will study how LLMs reason, plan, and generate solutions when applied to core scientific and engineering questions, particularly in battery and material design.You will design and optimize training pipelines for large models, tackling challenges around data quality, architecture, scalability, and compute efficiency.You will integrate domain specific data sources such as scientific literature and internal research documents into model training and inference.Your research will be deployed into a production multi agent AI system used for real battery technology discovery.You will collaborate closely with researchers, engineers, and external academic labs, and contribute to publications and conference presentations. What we are looking for An MSc or PhD in Computer Science, Statistics, Computational Neuroscience, Cognitive Science, or a related field, or equivalent industry experience.Strong grounding in machine learning, deep learning, and Large Language Models, with hands on research experience.Solid Python skills and experience with frameworks such as PyTorch or TensorFlow.Experience working with causal graphs and explainability focused AI methods.A proven research track record, ideally including peer reviewed publications.The ability to explain complex technical ideas clearly to both technical and non technical stakeholders.Nice to have Exposure to AI applied to material science, chemistry, or battery systems.Familiarity with recent research methods in LLM optimization and reinforcement learning approaches such as GRPO. What is on offerA highly competitive salary and benefits package, including equity in a publicly listed company.The chance to work on AI for science problems with visible global impact.A collaborative research environment alongside experienced ML scientists, engineers, and domain experts.Strong support for professional development, publishing, and long term career growth.

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.