Lead AI/ML Engineer

Role overview Own substantial parts of the design, implementation, and operation of machine learning and agent-based AI features in a production software product. This broad, hands-on technical leadership role spans product integration, evaluation, data pipelines, and production tooling, with influence over an evolving AI/ML architecture. Success means delivering useful capabilities while treating reliability, safety, latency, and cost as core engineering concerns.

Responsibilities - Build and productionize machine learning models and agentic AI functionality, partnering with product engineering teams to integrate features into the product. - Help shape AI/ML architecture and contribute to data transformation and pipeline work when required by the systems being built. - Define project evaluations and ground truth with product managers and stakeholders; curate human-generated evaluation sets and support automated evaluation. - Develop and maintain production tooling, with attention to latency, operating cost, error handling, and dependable releases. - Establish appropriate safety and responsible-AI safeguards, identify risks, and help define readiness standards for AI features. - Advise engineers on AI/ML practices, communicate with technical and nontechnical partners, and sometimes explain functionality to customers or customer-facing teams.

Requirements - Six or more years in data science, machine learning engineering, or a related quantitative or engineering field, including hands-on production delivery. - Strong software engineering ability, especially in Python, and direct experience shipping ML models or AI features. - Experience creating or curating evaluation datasets or ground truth for machine learning or LLM systems. - Familiarity with MLOps practices, cloud platforms (preferably AWS), and data warehousing. - Understanding of machine learning concepts and their practical production use, along with version control and CI/CD. - Ability to lead technical work independently, influence direction without formal authority, and communicate clearly.

Nice to have - Experience embedding LLM or agent-based features in a production product, using dbt, or working with Docker and Kubernetes. - Mentoring experience, knowledge of procurement or supply-chain data, or experience in enterprise software and startups.

Benefits and work setup Remote-first work with regular in-person gatherings for team activities and customer visits. The package includes startup equity, medical, dental, and vision premiums covered up to 100% for employee-only coverage, discretionary retirement matching, unlimited paid time off, and a $1,000 annual learning and development budget. The stated salary range is $160,000–$200,000.