Top Hybrid Machine Learning Engineer Jobs

Design and own agentic systems that produce client-ready financial deliverables end-to-end. Build retrieval, evaluation, and verification systems; transform proprietary data into evidence-backed insight; and advance orchestration, function-calling, and reliability for LLM-powered pipelines.

Design and productionize algorithms and regression models for real-time bidding and conversion prediction; apply privacy-preserving clustering to conversational data; analyze data to improve ad matching; conduct research, form hypotheses, and communicate findings to technical and non-technical stakeholders.

The role involves conducting research and optimizing machine learning systems while collaborating with technical staff to enhance product performance.

The role involves building and deploying machine learning solutions to achieve business impact, from model development to productionization, while collaborating with stakeholders.

The role involves designing and deploying strategies for congestion revenue rights, applying quantitative methods, and developing machine learning models.

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.

Design and build ranking, retrieval, and recommendation components for large-scale search; implement embeddings/semantic/hybrid/LLM-enabled retrieval; run offline/online evaluation and A/B tests; develop real-time indexing pipelines and observable microservices; own projects end-to-end and participate in on-call rotation.

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.