Staff Machine Learning Engineer, Merchants

What you’ll do

• Own end-to-end technical delivery for cross-team initiatives—from problem framing and technical strategy through architecture, implementation, rollout, monitoring, and iteration.
• Set technical direction and execution plans in partnership with a Director and cross-functional leads, including defining milestones, sequencing, and quality bars for the domain.
• Build and evolve ML and GenAI systems that improve merchant quality and understanding (e.g., merchant content enrichment, attribute extraction/normalization, entity resolution, merchant/brand quality signals, and policy-aware transformations), with clear downstream impact on retrieval, ranking, and shopping surfaces.
• Establish robust evaluation and measurement practices across ML + LLM-assisted systems, including golden datasets, human-in-the-loop review loops, automated regression testing, offline/online metric alignment, and clear go/no-go launch criteria for quality, safety, and performance.
• Design systems with strong attention to quality, cost, latency, reliability, and safety, including guardrails, fallbacks, caching, and observability to support scaled production operations.
• Establish the ML engineering operating model for the org (where applicable): evaluation standards, launch readiness reviews, monitoring/alerting, and sustainable ownership practices to keep quality high as the roadmap scales.
• Partner with cross-functional stakeholders across Product, Engineering, Data Science, Design, Trust/Policy/Legal, and ML platform teams to align on goals, constraints, and rollout plans—and to turn ambiguous needs into concrete ML deliverables.
• Drive experimentation and iteration (A/B tests, holdouts), lead error analysis, and translate learnings into measurable improvements to user trust and shopping outcomes.
• Mentor and raise the bar for technical design, evaluation rigor, and production readiness across the team—enabling faster, safer iteration with AI/ML tooling and best practices.
• Help scale the domain by supporting hiring and onboarding over time (e.g., interview loops, onboarding plans, technical mentorship), as we build out ML engineering capacity.

What we’re looking for

• 8+ years of industry experience in ML engineering / applied ML / software engineering, including meaningful time operating as a Staff-level (or equivalent) IC delivering complex production systems.
• Demonstrated ability to lead 0→1 ML/LLM efforts: taking ambiguous problem spaces, defining the approach, and delivering a production system with measurable impact.
• Strong track record shipping ML-powered systems in domains such as recommendation, ranking, retrieval, content understanding, ads relevance, commerce, or adjacent areas with clear product impact.
• Hands-on experience building LLM-powered applications in production (or adjacent GenAI systems), with strong judgment on reliability, failure modes, rollout safety, and practical tradeoffs.
• Deep experience with evaluation and measurement: dataset strategy, labeling/review operations, metric design, regression testing, and connecting offline improvements to online outcomes.
• Strong systems design skills building data- and ML-intensive systems, with the ability to navigate tradeoffs in performance, reliability, scalability, and cost.
• Strong communication skills and the ability to influence technical direction across teams without directly owning every implementation detail.
• Demonstrated experience building and enhancing cross-functional partnerships with other teams and organizations.
• Bachelor’s degree in Computer Science, Engineering, or a related technical field—or equivalent practical experience.