Top Hybrid Machine Learning Engineer Jobs

Design, build, and operate scalable cloud-native backend services and platform infrastructure (Kubernetes-based). Improve reliability, performance, deployment, observability, and developer tooling, and build AI-assisted internal tools and a coding agent to enable end-to-end feature delivery. Collaborate across teams and participate in architecture, debugging, and operational ownership.

Lead the technical direction and evolution of Bumble's large-scale recommendations platform. Design and operate distributed systems for retrieval, ranking, feature serving, experimentation, and serving. Drive architecture, observability, reliability, and AI-assisted tooling, mentor senior engineers, influence cross-team technical strategy, and contribute hands-on to prototypes and production code.

Lead and run robotics data programs: translate ML priorities into collection/annotation/validation plans, own roadmaps and delivery, improve processes, and build tools to ensure high-quality datasets for model training and evaluation.

Embed with strategic healthcare customers to build and deploy production AI applications on Infinitus platforms. Deliver deployment artifacts (MCP servers, sub-agents, workflows), provide white-glove enterprise support, codify repeatable patterns, and guide safe LLM/agent adoption while collaborating with Product and Engineering.

Implement and productionize ML research into performant C++ and Python systems, build ML infrastructure and training pipelines, design LLM/agent applications, and deploy AI workloads across edge, near-edge, and cloud environments while collaborating with data science and engineering teams.

As a Machine Learning Engineer, you will design and deploy ML models, integrating LLMs with cultural intelligence and developing recommendation systems, while collaborating with data engineers and optimizing model performance.

As an ML Research Engineer, you'll design experiments, implement model architectures, and engineer scalable libraries to advance molecular simulations to support drug discovery.

Lead design and delivery of production-ready AI systems within the ION Factory OS, ship AI-driven and core product features, architect scalable cloud-native systems and ML pipelines, implement RAG and MCP integrations, collaborate with product and data teams, ensure reliability and scalability, and mentor junior engineers.

Lead product strategy for AI-powered personalization and recommendation capabilities. Define requirements, partner with Data Science and Engineering to build, test, launch and optimize models, monitor performance, run experiments, prioritize enhancements using analytics, and communicate results to stakeholders while ensuring scalability, compliance and strong customer experiences.

As a Principal AI Engineer, you'll design scalable AI systems, mentor teams, develop large-scale LLMs, and optimize AI-driven cybersecurity solutions.