Senior Data Engineer – HealthTech AI $150,000–$200,000 Equity | Fully Remote (U.S.) | Full-Time DeepRec.ai is partnering with a fast-growing Series B HealthTech AI company to hire multiple Senior Data Engineers. You’ll join a 35 person Data Engineering team building AI-powered clinical products used by major U.S. healthcare organisations. This is a hands-on role focused on building scalable data pipelines that directly support real-world patient care.What You’ll DoBuild and maintain production data pipelinesDevelop data workflows primarily in PythonEnsure high data quality and reliabilityWork with large-scale cloud data platformsContribute to reusable tooling and frameworksRequirements4 years in Data EngineeringStrong Python skills (core requirement)Strong SQL experienceExperience with Databricks, Snowflake, or Microsoft FabricAzure cloud experienceExperience with distributed data processingNice to HaveHealthcare or EHR data experienceLakehouse or real-time pipeline exposureSaaS / multi-tenant platform experienceTech Stack: Python • SQL • Databricks • Snowflake • Fabric • PySpark • Azure Data Factory • CI/CDWhy Join?High-growth HealthTech AI company with strong fundingReal ownership in small, collaborative teamsClear progression to Staff / Director levelCompetitive salary, equity, and strong benefitsMeaningful work with real healthcare impact Please note: This is a permanent opportunity only — no C2C or contract engagements.

Our client is building the world’s first foundation model for physical infrastructure: electricity, gas, heat, and water. The networks that power modern civilization have run for decades on 1970s control logic, under-observed, under-optimized, and increasingly unequal to the demands of the energy transition. Our client was founded to change that. They have already built a composite, physics-grounded causal world model: six co-trained inference engines spanning physics-informed GNNs, causal time-series, topology discovery, federated training, edge inference, and techno-economic optimization. It is in production at 30 distribution system operators across 14 countries, covering 22 million connection points. Several of the world’s largest industrial automation and grid-technology vendors integrate it into their platforms, and it runs on the live grids of multiple tier-1 European utilities today. They are now building the next layer: a large-scale pre-trained foundation model for flow networks, trained on tens of billions of physics-consistent network states and governed by hard conservation-law constraints that no language model will ever learn from tokens. This is not applied AI. It is a new model class. DeepRec.ai is partnering with the company to assemble a small, exceptional team to build it. The data is real, exclusive, and unglamorous. The physics is non-negotiable. The impact is continental.   The problem you will work on Distribution grids are among the most complex dynamical systems on Earth: millions of nodes, time-varying topology, hard physical constraints, and almost no labeled ground truth. The state of the art is classical SCADA with a thin ML veneer. Our client is replacing it with a large-scale pre-trained foundation model, trained on synthetic and real network states, governed by Kirchhoff constraints as a hard loss term, and fine-tuned on operator-specific topologies via federated learning. Stage 1 pre-training target: 10¹? Newton-Raphson power-flow solutions across 50,000 distribution topologies. Stage 2: cross-network generalization to gas, heat, and water flow networks. Same architecture, different conservation laws. What you will doOwn end-to-end pre-training of the physics-informed GNN foundation model: data pipeline design, masked pre-training objective, distributed training infrastructure, and evaluation harness.Characterize scaling laws for physics-informed pre-training: data efficiency vs. compute trade-offs, emergence of physical consistency, and OOD generalization across unseen topologies.Design the pre-training corpus: synthetic topology generation, power-flow simulation at scale, and augmentation strategies that preserve physical validity.Lead the foundation-model preprint: own the architecture and pre-training sections, targeting a top-tier venue (NeurIPS, ICLR, ICML) or arXiv first.Interface with the causal world-model team on physics-informed loss formulation, and with the federated training team on privacy-preserving pre-training across operator estates.Represent our client externally at frontier AI venues. We expect this person to be a recognizable scientific voice for the model class being defined.Required profilePhD in machine learning, computer science, or computational physics from a leading research institution (e.g. ETH Zurich, Cambridge, Oxford, TU Munich, EPFL, UCL, ENS, or equivalent).3 to 6 years of post-PhD experience at a frontier AI lab or leading academic group (e.g. DeepMind, Meta FAIR, Mistral, EleutherAI, Stability AI, Kyutai, Aleph Alpha, Max Planck MIS, IDSIA, ELLIS-network member labs, or equivalent).First-author publications at NeurIPS, ICLR, or ICML on large-scale pre-training, masked modeling, GNN expressivity or scaling, or physics-informed deep learning.Hands-on experience training models at >1B parameter scale with distributed GPU/TPU infrastructure (PyTorch DDP/FSDP, JAX, or equivalent).Desirable: prior work at the intersection of graph neural networks and physical simulations, including molecular dynamics, fluid dynamics, power systems, or any PDE-governed network system.Desirable: experience with physics-informed neural networks (PINNs), neural operators (FNO, DeepONet), or Hamiltonian / Lagrangian networks.What our client offersA genuinely unsolved research problem at the intersection of physics, ML, and critical infrastructure, with exclusive access to real production data from 30 grid operators.First-principles technical latitude: you define the pre-training objective, the architecture choices, and the evaluation methodology, subject to hard physical constraints, not product-manager preference.A small, senior team. You will work directly with world-leading researchers in physics-informed ML and graph-based power systems AI.Competitive compensation benchmarked to tier-1 European AI labs, with meaningful equity in a company with €4M committed capital and growing ARR.Publication and conference travel fully supported.

Senior / Principal Research Scientist – Core AI Algorithms (Autonomous Systems)Location: Germany (Remote-first within Germany, on-site in Frankfurt every 2–4 weeks)About the RoleWe are partnering with a global automotive OEM building a core AI research and algorithm team responsible for the foundational intelligence behind next-generation automated driving systems.This role is research-driven and sits upstream of product teams. The focus is on inventing, validating, and transitioning new perception and world-modeling algorithms from research into production-ready systems. The team operates similarly to a big-tech research lab, but with a clear path to real-world deployment.Research Focus AreasDepending on background and interest, you may work on topics such as:3D scene understanding and world modelingOccupancy, motion forecasting, and dynamic scene reconstructionMulti-sensor perception (camera, LiDAR, radar)Representation learning for autonomous systems (BEV, implicit / generative 3D, Gaussian models, foundation models)Robustness, generalization, and long-tail perceptionLearning under weak, sparse, or noisy supervisionBridging offline training with real-world deployment constraintsKey ResponsibilitiesConduct original research in perception and autonomous systems with clear technical ownershipDesign and prototype novel algorithms and learning frameworksPublish at or contribute toward top-tier conferences and journals (e.g., CVPR, ICCV, ECCV, NeurIPS, ICRA, IROS)Translate research ideas into scalable, production-oriented implementationsCollaborate with applied ML, systems, and hardware teams to ensure feasibilityShape the long-term technical roadmap of the core AI organizationMentor junior researchers and engineers where appropriateRequired BackgroundPhD (or equivalent research experience) in Computer Vision, Machine Learning, Robotics, or a related fieldStrong publication record at top-tier conferences or journalsExperience conducting research within an industrial or applied settingExcellent understanding of modern deep learning methods and 3D perceptionStrong programming skills in Python and/or C Ability to work across the full spectrum from theory to implementationStrongly PreferredResearch experience in autonomous driving, robotics, or embodied AIWork on 3D perception, tracking, SLAM, or world modelsExperience at big-tech research labs, industrial AI labs, or advanced OEM R&DFamiliarity with real-world constraints such as runtime, memory, and system integrationPrior collaboration with product or engineering teamsWhat’s on OfferA research-first role with real influence on production systemsThe opportunity to define core algorithms, not just incremental improvementsA team culture that values publications, patents, and long-term thinkingRemote-first working model within Germany, with regular in-person collaboration in FrankfurtCompetitive compensation aligned with senior / principal research profilesWho This Role Is ForResearchers who want their work to ship into real vehiclesIndustry researchers seeking greater technical ownershipPhD-level candidates who enjoy both publishing and buildingProfiles combining academic depth with practical engineering maturityLooking forward to seeing your profile!

Job Title: Data Science Consultant (GenAI & AI Consulting) Location: London (Hybrid)Roles: MultipleSeniority: Junor to Experienced  The Opportunity Work at the intersection of Data Science and GenAI — not just using AI tools, but shaping how they are evaluated, trusted, and delivered to enterprise clients. This is a consulting role for Data Scientists who can go beyond models and clearly explain why they work, how they’re validated, and the business impact they drive. The RoleDeliver end-to-end Data Science and GenAI solutionsBuild and evaluate ML and LLM-based modelsDefine metrics, validation approaches, and evaluation frameworksTranslate complex AI outputs into clear business insightsWork on PoCs, prototypes, and client-facing innovation projects(Senior/Managing) Lead stakeholders and mentor junior consultantsWhat You’ll BringStrong Python and Machine Learning foundationsExperience across the full Data Science lifecycleSolid understanding of model evaluation, validation, and accuracy metricsExposure to GenAI (LLMs, agentic AI, or similar)Ability to confidently explain technical concepts to clientsConsulting or client-facing experience preferredWhat Makes You Stand OutYou understand why models perform the way they doYou can challenge outputs, not just generate themYou can link AI solutions to real business value and ROIBenefitsBonus schemeUp to 10% pensionPrivate medical25 days holiday option to buy moreLife assurance & income protectionFunded certifications

Senior Machine Learning Engineer Fully Remote (United States)   |   up to $200k base equity   The role Our client is hiring a Senior Machine Learning Engineer to own the end-to-end development and deployment of large language and machine learning models, with a primary focus on data preprocessing, model training, and fine-tuning across large-scale healthcare datasets. This is a hands-on, builder-focused role. You will be designing and training models that solve real clinical and operational problems, integrating structured and unstructured data, and shaping the long-term ML roadmap as the company scales its US product. What you will be doing Data preprocessingClean, transform, and prepare large, complex healthcare datasets for ML model development.Handle missing values, outlier detection, feature engineering, and normalization at scale.Identify, collect, and curate relevant industry-specific datasets for retraining and fine-tuning.Format data appropriately for the chosen LLM and training pipeline.Model training and fine-tuningDesign, train, and fine-tune LLMs on extensive healthcare data to solve specific clinical or operational problems.Set up and manage the training environment, including GPU instances and supporting tooling.Fine-tune pre-trained LLMs on custom datasets to hit specific objectives.Run hyperparameter experiments (learning rate, batch size, training epochs) to optimize performance.Integrate structured and unstructured data into multimodal and multi-input models.Evaluation, optimization, and pipelinesEvaluate model performance using appropriate metrics, identify gaps, and implement targeted optimizations.Build and maintain robust, scalable data and ML pipelines spanning training, inference, and deployment.Collaborate closely with data scientists, clinicians, and software engineers to integrate models into production.Maintain clear documentation of models, pipelines, and experimental results.What we are looking for Essential5 years of experience in Machine Learning Engineering or a comparable role.Proven experience with large-scale data preprocessing, LLM and model training, and fine-tuning.Distributed training experience with PyTorch Distributed, DeepSpeed, Ray, or Hugging Face Accelerate.GPU/TPU optimization and memory management for large language models.Strong Python and core ML stack: PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy.Solid grasp of ML algorithms, large language models, and deep learning architectures.Nice to haveHands-on healthcare data experience.Experience with cloud platforms (GCP strongly preferred; AWS considered) and distributed compute frameworks like Spark.Familiarity with MLOps practices and tooling.Bachelor's or Master's in Computer Science, Machine Learning, AI, or a related quantitative field.Work authorization Open to US Citizens, Green Card holders, and candidates already in the US on a valid H-1B (transfers considered). About the company Our client is an AI-first healthtech company on a mission to detect cancer earlier and prevent it where possible. Their platform has already assessed over 700,000 patients and identified more than 75,000 cancers, and they are now expanding their US footprint with a greenfield product build off the back of a fresh Series A round, backed by one of the most respected VCs in the world. Most of the cancer industry focuses on treatment. This team is focused on detection and prevention, where the impact on survival rates is greatest. The founders are practising doctors who have lived in the problem space first-hand, and the company is tech-first, with the majority of headcount sitting in engineering, data, and ML. Why joinReal-world impact: AI that directly contributes to earlier cancer detection and improved patient outcomes.Greenfield US build at a critical inflection point, with high ownership from day one.Series A backing from a top-tier global VC.Builder culture: production-grade work, not research or prototypes.Direct exposure to the CTO and senior AI leadership in a flat, fast-moving environment.Continuous learning, with access to the latest tools and methods in AI and healthcare.BenefitsCompetitive base salary plus meaningful equity.Fully remote across the United States.Flexible working arrangements.Continuous learning opportunities and access to leading AI tooling.The chance to do work that genuinely matters: building AI that helps save lives.How to apply This search is being run on a confidential basis by Sam Warwick at DeepRec.ai. To apply or learn more about the company before going forward, please get in touch directly and full details will be shared once an initial conversation has taken place.

Senior MLOps Engineer Fully Remote (United States)   |   up to $200k base equity   The role Our client is hiring a Senior MLOps Engineer to build and operate the production platform powering their ML and LLM-driven healthcare workflows. You will design reliable, secure, and compliant systems for model development, evaluation, deployment, monitoring, and continuous improvement, working closely with ML, data, security, and product teams. This is the right seat for someone who has shipped ML systems in production and is excited about LLM orchestration, RAG, evaluations, guardrails, and observability inside a regulated healthcare environment. What you will be doing MLOps and ML platformDesign and operate ML platforms supporting end-to-end workflows: data ingestion, feature engineering, training, evaluation, deployment, and monitoring.Build and maintain CI/CD for ML, including testing, packaging, versioning, reproducibility, automated rollbacks, and approvals.Implement MLOps best practices: model registry, experiment tracking, lineage, governance, and reproducible training environments.Develop scalable training infrastructure: distributed training, GPU scheduling, cost controls, and auto-scaling.Build and maintain feature pipelines and feature stores, ensuring consistency between training and inference.Establish model monitoring and observability: performance, drift, fairness signals where relevant, latency, throughput, and data quality.Own end-to-end LLM delivery pipelines: prompt versioning, retrieval, orchestration, evaluation, deployment, monitoring, and iterative improvement.Build LLM evaluation harnesses, both offline and online: golden datasets, automated regression testing, human-in-the-loop review, and risk scoring.Implement cost controls: token and cost budgeting, caching, autoscaling, and performance tuning.Deployment, reliability, and operationsProductionize ML models on GCP using containers and orchestration (GKE, Cloud Run).Build CI/CD for ML and LLM systems with automated tests and safe rollouts.Implement observability: tracing, metrics, logs, dashboards, and alerting for model and system health, including hallucination indicators and retrieval quality.Data, governance, and healthcare complianceDesign systems with security and privacy by default: IAM, least privilege, secrets management, audit logs, encryption, retention, and PHI/PII handling.Implement governance: model and prompt lineage, dataset provenance, evaluation traceability, and approval workflows aligned with healthcare compliance expectations.Integrate guardrails: content filters, policy checks, prompt injection defenses, structured output validation, and fallback strategies.What we are looking for Essential6 years in software or platform engineering, including 4 years operating ML systems in production.Strong ML engineering background: training pipelines, evaluation, deployment patterns, monitoring, and iteration loops.Demonstrated hands-on experience with LLM systems in production.Strong Python plus production-grade experience building APIs and services.Strong experience with GCP services and cloud-native patterns.Production experience with Vertex AI (pipelines, endpoints, feature store, model registry, evaluation) and/or managed vector search on GCP.Containerization and orchestration with Docker, Kubernetes/GKE, and/or Cloud Run.Work authorization Open to US Citizens, Green Card holders, and candidates already in the US on a valid H-1B (transfers considered). About the company Our client is an AI-first healthtech company on a mission to detect cancer earlier and prevent it where possible. Their platform has already assessed over 700,000 patients and identified more than 75,000 cancers, and they are now expanding their US footprint with a greenfield product build off the back of a fresh Series A round, backed by one of the most respected VCs in the world. Most of the cancer industry focuses on treatment. This team is focused on detection and prevention, where the impact on survival rates is greatest. The founders are practising doctors who have lived in the problem space first-hand, and the company is tech-first, with the majority of headcount sitting in engineering, data, and ML. Why joinReal-world impact: AI that directly contributes to earlier cancer detection and improved patient outcomes.Greenfield US build at a critical inflection point, with high ownership from day one.Series A backing from a top-tier global VC.Builder culture: production-grade work, not research or prototypes.Direct exposure to the CTO and senior AI leadership in a flat, fast-moving environment.Continuous learning, with access to the latest tools and methods in AI and healthcare.BenefitsCompetitive base salary plus meaningful equity.Fully remote across the United States.Flexible working arrangements.

Founding Member of Technical Staff (Research / Post-Training) Applied AI / RL | San Francisco (onsite) | $200k–$275k 0.25–0.50% equityDeepRec is partnered with a YC-backed (S25), seed-stage applied AI and data company working at the cutting edge of reinforcement learning and agentic systems. They collaborate closely with leading AI labs to train models capable of executing complex, real-world workflows across financial services. Their core platform focuses on building high-quality RL environments that simulate tasks across investment banking, private equity, and hedge funds (e.g. financial modelling, presentations, etc.). Following a recent seed raise, they’re now building out their founding research and engineering team. The Opportunity This is a Founding Member of Technical Staff hire focused on research and post-training. You’ll take ownership of training and evaluating frontier models, shaping external benchmarks, and contributing to the company’s research presence. What You’ll Be DoingTraining open-source / frontier models on proprietary RL environments to validate performance and generate insightsLeading public-facing benchmarks and leaderboard initiatives for frontier modelsPublishing research (blogs, papers) to engage both industry and academic communitiesContributing to core platform work where needed (AI tooling, data pipelines, environment/reward systems)Helping establish engineering and research culture from day oneWhat They’re Looking ForExperience in model post-training (fine-tuning, RLHF, or similar)Track record of publishing research or contributing to open research communitiesFamiliarity with RL, evaluations, or benchmarking for AI agentsStrong startup mindset — high velocity, high ownershipProduct awareness and ability to prioritise across a broad roadmapComfortable engaging with users, customers, and subject matter expertsNice to HavePrevious startup or founding experienceCompensation & Benefits$200k–$275k base 0.25–0.50% equityFully covered healthcare (including dependents)Relocation support401(k)Meals, gym, and transport fully coveredVisa sponsorship availableLocation San Francisco — full-time, onsite

Staff / Principal AI Researcher About the company A deep-tech startup building ultra energy-efficient computing infrastructure for new-generation AI inference. Our brain-inspired chip architecture is purpose-built for dynamically sparse and event-driven algorithms, delivering an order of magnitude better energy efficiency than today's GPU-based systems. Our hardware is already deployed at leading research institutions across Europe and the US, and we are scaling rapidly as the industry wakes up to the energy bottleneck that GenAI is creating. We are looking for talented and passionate people with a real appetite for problem solving to help shape the future of AI hardware. About the role As a Staff/Principal AI Researcher, you will lead the design and development of advanced AI algorithms tailored for our sparse, brain-inspired computing systems. This is a senior, high-autonomy role sitting at the intersection of frontier AI research and novel hardware, with direct influence over both the technical roadmap and how our algorithms reach customers in production. You will lead technically, mentor other researchers, and work closely with our compiler, hardware, and systems teams to make sure our models actually ship and scale on real silicon. What you'll be doingLeading the design, development, and optimization of advanced AI algorithms tailored for sparse hardware and brain-inspired computing systemsArchitecting and implementing efficient machine learning and deep learning models, with a focus on scalability, performance, and hardware-awarenessDriving innovation in algorithmic approaches for sparse and event-driven computing paradigms, especially but not limited to Transformer-based architecturesMentoring and managing a team of AI engineers and researchers, fostering technical excellence and professional growthDeveloping robust, scalable, and maintainable algorithmic frameworks that integrate cleanly with the wider software and hardware ecosystemDefining and implementing benchmarking methodologies to evaluate model accuracy, efficiency, latency, and energy consumptionCollaborating with compiler, hardware, and systems teams to ensure seamless integration and co-optimization of algorithms and execution pipelinesContributing to technical documentation, research publications, and demonstrators that showcase the team's capabilitiesWhat we're looking forProven experience leading cross-functional technical teams in AI/ML development or applied researchDeep expertise in machine learning and deep learning algorithms, including model design, training, and optimization5 years of relevant industry experience developing production-grade AI solutionsExpert-level proficiency with ML frameworks such as PyTorch or TensorFlow, and familiarity with modern model deployment workflowsHands-on experience optimizing models for hardware acceleration (CPU, GPU, or specialized accelerators)Strong analytical and problem-solving skills with a track record of tackling genuinely hard algorithmic challengesBSc or MSc in Computer Science, Artificial Intelligence, Applied Mathematics, or a related fieldNice to havePhD or Dr.-Ing. in a computationally intensive disciplineHands-on experience with DevOps tools and CI/CD pipelinesFamiliarity with MAMBA or other state-space model architecturesHands-on experience with model compression, quantization, or pruningBackground in computer architectureContributions to open-source projects or publications at leading AI venuesFamiliarity with multi-chip computing concepts and techniquesWhat we offer A highly competitive salary, relocation support, and a flexible, inclusive work environment. We are an equal opportunity employer and welcome people of different backgrounds, nationalities, and experiences.