Staff Machine Learning Engineer

Staff Applied Machine Learning Engineer

Remote (must be based in USA)

Work Authorization: ship or required due to government contract requirements

$230-280,000 base + Equity + Benefits

About the Opportunity

Our client is a rapidly growing, venture-backed AI company building secure, enterprise-grade AI solutions for highly regulated industries. Their platform helps organizations unlock the value of complex data by embedding AI into mission-critical workflows, improving decision-making, operational efficiency, and knowledge discovery.

As the company continues to scale, they are investing heavily in next-generation AI capabilities, including search, knowledge exploration, semantic reasoning, and agentic AI. This is an opportunity to join a high-caliber engineering team tackling technically challenging problems at production scale while helping shape the future direction of the platform.

The Role

As a Staff Applied Machine Learning Engineer, you will design, build, and deploy advanced machine learning solutions that power intelligent search, knowledge exploration, and AI-driven workflows.

You'll work closely with product, platform, and engineering teams to develop production-grade ML systems, combining strong software engineering fundamentals with expertise in modern AI techniques. This is a highly technical individual contributor role with significant ownership and influence over architectural decisions.

Responsibilities

Lead the design, development, and deployment of machine learning models for large-scale production systems.

Design and build intelligent search and knowledge exploration capabilities using modern ML techniques.

Develop systems involving knowledge graphs, semantic representations, and advanced entity understanding.

Collaborate with engineering and product teams to deliver end-to-end machine learning solutions.

Evaluate model performance, runtime efficiency, and scalability in production environments.

Communicate technical decisions, trade-offs, and recommendations to both technical and non-technical stakeholders.

Build high-quality enterprise software while maintaining a fast pace of delivery.

Take ownership of projects from design through implementation, deployment, and ongoing support.

Troubleshoot and support distributed production systems.

Stay current with advances in AI and machine learning and apply emerging techniques where appropriate.

Required Qualifications

ship or required.

Bachelor's or Master's degree in Computer Science, Machine Learning, NLP, or a related field. A PhD is strongly preferred.

10+ years of experience building and deploying production machine learning systems.

Strong background in Natural Language Processing (NLP), information retrieval, or semantic search.

Experience designing and supporting knowledge graph or semantic representation systems.

Proven experience building and deploying Agentic AI systems, including orchestration, tool use, reasoning workflows, and context management.

Strong software engineering skills with experience building scalable distributed systems.

Experience working with production ML systems throughout their lifecycle.

Excellent communication and cross-functional collaboration skills.

Experience leveraging modern AI tools to improve engineering productivity.

Demonstrated curiosity, adaptability, and a continuous learning mindset.

Preferred Qualifications

PhD in Computer Science, Machine Learning, Artificial Intelligence, NLP, or a related discipline.

Experience working in startup or high-growth environments, particularly building 0?1 products.

Familiarity with Kubernetes and cloud-native infrastructure.

Experience integrating machine learning models into large-scale enterprise platforms.

Why Join?

Join a fast-growing, well-funded AI company solving complex real-world problems.

Work on cutting-edge technologies including Agentic AI, NLP, search, knowledge graphs, and semantic reasoning.

Collaborate with an experienced engineering team that values technical excellence, ownership, and innovation.

Competitive compensation and meaningful pre-IPO equity.

Fully remote position within the United States.