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.