A hands-on engineering leader to guide the Human Agent Tooling team within Trust & Safety, leading a squad of 3–5 engineers, setting the hiring bar, coaching and developing talent, and owning execution and delivery.
Compensation
$199,000—$275,000 USD
Responsibilities
Manage and develop a squad of 4-5 engineers: Hiring, 1:1s, career coaching, managing for performance
Own delivery for your squad end to end, from sprint planning and delivery commitments through service SLAs and operational health
Write and ship code alongside your team, authoring pull requests that land in production
Be accountable for the squad's technical direction, architecture, and quality bar
Use AI coding agents and tooling to raise both your own leverage and your squad's, and establish the practices that make that durable for the team
A senior hands-on applied science and engineering role focused on shaping the next generation of deep learning systems for personalization, including recommendation, ranking, retrieval, traveler understanding, sequential modeling, and foundation-model-based personalization.
Locations (2)
San Jose, California, United States
Seattle, Washington, United States
Compensation
The total cash range for this position in San Jose is $187,000.00 to $261,500.00. Employees in this role have the potential to increase their pay up to $299,000.00, which is the top of the range, based on ongoing, demonstrated and sustained sustained performance. The total cash range for this position in Seattle is $173,000.00 to $242,500.00. Employees in this role have the potential to increase their pay up to $277,000.00, which is the top of the range, based on ongoing, demonstrated and sustained performance.
Responsibilities
Design, develop, and apply machine learning solutions to real-world personalization, product, and business problems
Drive end-to-end scientific work across problem formulation, data exploration, feature engineering, model development, evaluation, and iteration
Partner closely with engineers, product, and business stakeholders to integrate machine learning solutions into services and workflows
Use strong technical judgment to select appropriate methods, validate outcomes, and improve model performance, reliability, and operational quality
Safely integrate and operate AI/ML-enabled solutions that improve outcomes
Requirements
Bachelor's degree in Computer Science or a related technical field
Nice to have: Advanced degree in Machine Learning, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field
