Top Hybrid Machine Learning Engineer Jobs

Build and ship production machine learning and computer vision systems processing 11 million daily images. Own the full lifecycle from sensor data collection, dataset curation, training, and deployment through monitoring and iteration. Develop detection, segmentation, classification, embedding, and re-identification models for cloud and roadside edge hardware, balancing accuracy, speed, and power efficiency. Apply both classical computer vision and state-of-the-art methods while writing production-quality software.

Build and maintain internal agentic AI platforms and developer-facing tools that integrate LLMs with engineering workflows. Partner with product and infra teams to design standards for agent deployment, reliability, evals, security, onboarding, and adoption measurement to improve developer productivity.

Own and improve Tessellate’s physician-to-organization matching engine by evolving rules-based matching toward semantic matching, embeddings, and vector search. Establish precision, recall, and evaluation practices; resolve healthcare organization identities across millions of records; and maintain a reliable, lightweight data platform. The role requires strong Python and SQL, applied machine learning expertise, production recommendation or ranking experience, and end-to-end ownership in an early-stage startup.

Build, train, deploy, and maintain large-scale machine learning models and AI agents for marketing and customer experiences. Develop conversational AI, generative AI, recommendation, retrieval, ranking, and transformer-based systems. Own ML product lifecycles, including success metrics, A/B testing, production operations, and iterative improvement. Collaborate with Engineering, Product, Design, customers, and stakeholders while leading technical projects and mentoring teammates.

Provide technical leadership for CarGurus’ recommendation platform, defining architecture and building scalable personalized ranking and discovery systems. Partner with Data Science to productionize machine learning models, improve experimentation and recommendation quality, and deliver AI-powered consumer experiences. Lead complex cross-functional initiatives, improve reliability and observability, make organization-wide technical decisions, write code, mentor engineers, and uphold quality, security, and operational standards.

Develop, validate, deploy, and monitor machine-learning and statistical fraud models to reduce losses and false positives. Clean and synthesize large datasets, forecast fraud losses, automate monitoring and reporting, collaborate cross-functionally, and maintain model governance and documentation.

Leads WHOOP’s cloud-based machine learning organization responsible for sleep, recovery, and training algorithms. Manages ML scientists and engineers, establishes technical standards and development practices, guides architecture and deployment, and partners with platform, hardware, sensor, product, research, and engineering teams. The role requires scaling production ML systems, improving model validation, monitoring, and deployment, and translating physiological sensor capabilities into reliable consumer experiences.

Design, research, and benchmark machine learning algorithms for anomaly detection, error outlier analysis, and faulty deployment analysis. Build, deploy, scale, and monitor user-facing ML features, models, services, and infrastructure in production. Analyze high-volume streaming data, communicate complex algorithms to nontechnical audiences, participate in research journal discussions, maintain team-owned systems, and join the on-call rotation while partnering with engineering and product teams.

Own the end-to-end architecture, development, deployment, evaluation, and operations of AI and machine learning systems. Build predictive ordering models, agentic workflow copilots, retrieval and evaluation infrastructure, safety guardrails, and responsible AI practices. Prioritize AI initiatives based on business outcomes, establish model governance and production standards, advise product and engineering leadership, and mentor experienced contributors.

Lead design and productionalize ML algorithms and pipelines to detect account-sharing fraud in real time. Build scalable preprocessing, training, deployment, monitoring, and A/B testing solutions with cross-functional teams to optimize detection while minimizing user disruption.