Top Hybrid Machine Learning Engineer Jobs

Own the full machine learning lifecycle for search ranking and retrieval, including model training, deployment, serving, feature pipelines, evaluation frameworks, hybrid lexical-vector retrieval, large-scale embedding inference, query understanding, and permissions-aware multi-tenant search. Collaborate with infrastructure, AI, and backend teams to improve search relevance across ClickUp’s platform.

Set the technical direction for machine learning across underwriting, fraud, risk, and personalization. Own the ML platform and modeling roadmap, including feature stores, pipelines, serving, monitoring, governance, and experimentation. Lead high-impact credit and cash-advance models through deployment and iteration, establish model-risk standards, partner with business and compliance leaders, and mentor engineers. This is a hands-on staff role with company-wide technical influence.

Own machine learning at Metriport end to end, including problem framing, feature engineering, model training, evaluation, deployment, monitoring, and infrastructure. Build predictive models and document-processing systems from large, messy clinical datasets. Develop training and inference pipelines, experiment tracking, model versioning, and production monitoring. Collaborate with founders, engineers, and customers to identify high-value ML opportunities and deliver reliable healthcare intelligence.

Design, train, and deploy reinforcement learning policies for humanoid manipulation, grasping, dexterous control, and whole-body coordination. Own the MuJoCo-based sim2real pipeline, including rewards, environments, contact modeling, and domain randomization. Test policies on real robot hardware, use telemetry to improve failures, and collaborate with locomotion, controls, and hardware researchers. Open-source robotics and reinforcement learning work where possible.

Develop generative and predictive machine learning models for therapeutic antibody sequence, structure, and property design. Build multi-objective optimization and agentic LLM methods for lab-in-the-loop protein engineering, deploy models with engineering teams, and support drug development programs. Collaborate with drug developers, wet-lab scientists, automation specialists, and data scientists to translate ML methods into validated antibody candidates.

Design, develop, deploy, and maintain production-grade AI and machine learning solutions, including LLM applications, multi-agent systems, agentic workflows, and enterprise automation. Own the AI solution lifecycle in cloud environments, influence architecture and engineering standards, apply MLOps and responsible AI practices, and collaborate with data, analytics, and business stakeholders to deliver enterprise-scale solutions.

Design, develop, deploy, and maintain production-grade AI and machine learning solutions for enterprise corporate functions. Build LLM applications, multi-agent systems, agentic workflows, RAG solutions, and automation using cloud AI platforms. Own the full AI solution lifecycle, influence architecture and engineering standards, apply MLOps and responsible AI practices, and collaborate with data, analytics, and business stakeholders.

Design, develop, deploy, and maintain production-grade AI and machine learning solutions for enterprise corporate functions. Build LLM orchestration, RAG applications, agentic workflows, multi-agent systems, and automation using cloud AI platforms. Own the full AI solution lifecycle, establish architecture and engineering standards, apply MLOps and responsible AI practices, and collaborate with data, analytics, and business stakeholders.

Develop and deploy machine learning planning algorithms for autonomous trucks. Own the full ML lifecycle, including data curation, feature engineering, training, evaluation, deployment, monitoring, and improvement. Design trajectory and behavior-planning models, safety guardrails, validation pipelines, and performance metrics. Investigate failures and edge cases, optimize inference, and collaborate with perception, prediction, mapping, runtime, and systems teams to deliver reliable production systems.

Lead development and deployment of machine learning models, provide technical leadership on AI/ML solutions, integrate models with data pipelines, build RPA automations, support data governance, evaluate analytics products, and present findings through dashboards and reports to stakeholders.