Partner with product managers and business stakeholders to define technical goals and outcomes for the ML team
Coordinate cross-functional collaboration and delivery for ML subrogation initiatives
Lead the team to deliver cutting-edge ML features using best-in-class software engineering practices
Establish industry-leading standards for ML development and deployment

Requirements

Deep understanding of ML lifecycle: development, evaluation, deployment, monitoring, and iteration
Expertise with ML system design, model deployment, and production ML systems
Experience in engineering production-grade GenAI features with robust validation, sanity checks, and structured outputs to mitigate hallucination and business risk
Proficiency with AWS cloud and MLOps (Sagemaker, Lambda, Stepfunctions, IAM)
Strong software engineering fundamentals and best practices (CICD, Test-Driven Development, Infrastructure as Code)
Proven ability to build, mentor, and scale high-performing technical teams
Experience translating business objectives into technical roadmaps and deliverables
Ability to balance technical excellence with business priorities and timelines
Excellent communication skills; ability to explain complex technical concepts to non-technical stakeholders including articulation of AI risks and measures taken to mitigate it
Strong stakeholder management and cross-functional collaboration skills
8+ years in ML/Data Science, with 3+ years managing ML/DS teams

Benefits

401K Match
Paid time off
Annual Incentive Plan
Performance Bonus
Comprehensive health insurance
Adoption Assistance
Tuition Reimbursement
Wellness Programs
Stock Purchase Plan options
Employee Resource Groups

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

ML lifecycleML system designmodel deploymentproduction ML systemsGenAI featuresvalidationsanity checksCICDTest-Driven DevelopmentInfrastructure as Code

Soft Skills

mentoringstakeholder managementcross-functional collaborationcommunicationbalancing technical excellence with business prioritiestranslating business objectives into technical roadmapsteam buildingscaling teamsexplaining complex conceptsarticulating AI risks