Top Hybrid Machine Learning Engineer Jobs

Design and deploy ML systems for enterprise search, recommendations, retrieval, and AI agents. Build scalable data/ML pipelines, evaluation frameworks, and metrics. Apply statistics, experimentation, and causal inference to guide product decisions. Collaborate cross-functionally, prototype LLM-based capabilities (embeddings, RAG), and mentor junior team members.

Design, build, and deploy ML models across diverse use cases; work with managed cloud ML platforms and containerized solutions; collaborate with engineers on production integration and MLOps best practices; preprocess and analyze large datasets; mentor teammates and influence technical direction; translate complex ML concepts for technical and non-technical stakeholders.

The Staff Software Engineer will develop and deploy high-performance machine learning systems, optimizing large language models for production environments. Responsibilities include ensuring high availability and low latency for AI applications and working collaboratively across teams.

Own the Contract Understanding Platform to deliver accurate extraction and connected contract representations. Set roadmap and OKRs, define APIs and schemas, improve extraction quality, build evaluation and feedback loops, work cross-functionally with engineering, applied science, design, data, legal, security, and GTM, and translate customer workflows into platform capabilities.

Design and build scalable search and retrieval infrastructure: indexing pipelines, backend services for semantic and hybrid retrieval, knowledge graph construction, retrieval orchestration, APIs, and observability to optimize latency, throughput, cost, reliability, and security for large-scale LLM-powered systems.

Build core, shared ML capabilities for a production platform: foundation models for documents, extraction agents, model routing, unified evaluation, and continuous improvement pipelines. Own end-to-end capability development, production deployment, and cross-team support to improve accuracy, latency, cost, and reliability across institutional workflows.

As a Machine Learning Engineer, you'll build and enhance features for a search stack, design ML/NLP solutions, and implement an experimentation framework while ensuring code quality.

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.