Top Hybrid Machine Learning Engineer Jobs

Build Afresh's AI platform: design knowledge graphs and retrieval (vector/graph/structured), develop LLM-powered agents and serving infrastructure, own evaluation/observability, and productionize pipelines and data products on Databricks/MLflow to provide reliable, low-latency context for models.

Embed with grocery customers to scope, integrate, and deploy production LLM- and agent-powered systems. Build and harden platform components (knowledge graph/ontology, retrieval, agent frameworks, serving, evals, and observability) so field learnings become reusable tooling. Balance customer-facing implementation with platform engineering to enable scalable, reliable AI in production.

Lead and mentor ML engineers and data scientists, overseeing ML strategy and development of scalable models for healthcare applications.

Lead and grow the Machine Learning Engineering team, set technical direction, define team roadmap, and collaborate across product and engineering teams.

The role involves developing and productionizing security detection models, collaborating with security research, and shaping engineering culture as a founding team member.

Design, build, and maintain agent APIs and production agent applications for document understanding, advanced RAG, and customer support automation. Integrate open-source models, collaborate with backend and infra for deployment and monitoring, and ensure APIs are robust, scalable, and developer-friendly.

The Machine Learning Researcher will develop new architectures for data-efficient model induction, focusing on optimizing generalization performance and runtime inference in networks, while contributing to a performance-driven code base.

The Senior Applied Scientist will develop machine learning solutions for personalization, pricing optimization, and fraud detection, leading model development and experimentation while collaborating with various teams.

Design, train, and deploy machine learning models for RF signal detection, classification, and vessel activity tracking in maritime intelligence.

The Machine Learning Engineer will design and build scalable ML data pipelines, manipulate 3D data, collaborate with customers, and ensure reliable deployment of ML systems.

The Senior Machine Learning Engineer will design, deploy, and mentor on ML models, collaborate with customers, and influence product direction. Responsible for robust ML systems and data pipelines.

Design, build, and maintain LLM integrations and Snap Packs, develop the Agent Framework and MCP server, implement cross-provider LLM features, leverage AI-assisted development, ensure code quality and test coverage, and share technical knowledge across teams.