Senior Machine Learning Engineer
Technical lead for ML team building personalization, ranking, and search systems at scale. Own architecture, model training, deployment, and serve as hands-on mentor driving production ML systems.
About the job
Role Responsibilities
- Serve as the technical lead for a single ML-focused team, setting direction and raising the bar on engineering quality and system design
- Design, build, and scale ML systems supporting personalization, ranking, search, or ad-related use cases
- Own end-to-end architecture for your team’s services, including model training, evaluation, deployment, and serving
- Drive clarity in ambiguous problem spaces, translating product needs into scalable technical solutions
- Lead design reviews and ensure thoughtful tradeoffs around latency, reliability, experimentation, and maintainability
- Partner closely with product, data, and engineering stakeholders to deliver measurable business impact
- Mentor engineers through hands-on technical guidance, feedback, and example
- Use AI tools to accelerate development and improve system design, including prototyping with LLM tools, leveraging AI for code iteration, and exploring LLM-powered features
Minimum Requirements
- 5+ years of industry experience in machine learning or software engineering, with demonstrated ownership of production ML systems operating at scale
- Proven experience building and scaling ML systems in personalization, relevance, search, or ad tech domains
- Strong hands-on expertise in distributed systems, data pipelines, and ML infrastructure
- Experience deploying ML models into production and operating them at consumer scale
- Demonstrated ownership of complex technical initiatives within a team
- Strong systems design skills with the ability to clearly articulate tradeoffs and implementation decisions
- Experience mentoring engineers and influencing technical standards within a team
- Ability to operate effectively in ambiguous environments and drive projects to completion
Preferred Requirements
- Familiarity with LLMs and their application in personalization, feature generation, or search
- Experience with real-time or streaming ML systems
- Exposure to experimentation frameworks (A/B testing) and model performance measurement
- Experience bridging model development with real-time serving systems
Benefits
- Equity in Fetch for full-time employees
- 401k match up to 4%
- Comprehensive medical, dental, and vision plans (including pets)
- $10,000 per year in education reimbursement
- Employee Resource Groups and Inclusion Council participation
- Flexible PTO, 9 paid holidays, and year-end week-long break
- 20 weeks paid parental leave for primary caregivers, 14 weeks for secondary caregivers
- $2,000 Calvin Care Cash for new family members
- Fully remote work option with hardware/software provided
Skills
Machine Learning, Distributed Systems, Data Pipelines, ML Infrastructure, Model Deployment, Personalization, Ranking, Search, Ad Tech, LLMs, A/B Testing, Real-Time Systems
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