Principal Machine Learning Engineer
Design and scale ML infrastructure and real-time learning systems powering personalization, search, ranking, and ad tech for millions of consumers. The role requires deep distributed-systems and data-pipeline expertise, strong architecture leadership, and experience delivering zero-to-one ML systems.
About the job
Responsibilities
- Design and evolve ML infrastructure supporting personalization, search, ranking, and ad tech.
- Build zero-to-one real-time learning systems and data pipelines.
- Define architectural patterns for feature infrastructure, model serving, and low-latency, high-throughput decision-making at consumer scale.
- Advance data infrastructure, distributed systems, and large-scale data pipelines for personalization and ranking.
- Lead technical design, architecture, and cross-team alignment for major ML initiatives.
- Improve streaming and real-time learning infrastructure for ranking, personalization, and search systems.
- Use AI tools for feature design, code generation, prototyping, architecture diagramming, design validation, personalization, conversational search, and feature creation.
- Mentor senior engineers and technical leads.
- Operate effectively with ambiguity and drive zero-to-one system design and delivery.
Requirements
- Proven experience building and scaling ML infrastructure for personalization, relevance, search, or ad tech systems.
- Deep hands-on expertise in data infrastructure, distributed systems, and large-scale ML data pipelines.
- Experience at a consumer product company with ML models operating at scale.
- Experience contributing to ranking, personalization, or ad tech systems with measurable business impact.
- Strong systems design skills and experience leading architecture and communicating tradeoffs.
- Experience mentoring and elevating engineers.
- Experience leading zero-to-one technical initiatives and delivering infrastructure or ML systems from scratch.
- Ability to operate with minimal direction, prioritize effectively, and drive impact.
Nice-to-haves
- Familiarity with LLMs and applications in personalization, feature creation, and conversational search.
- Experience with streaming or real-time learning systems.
- Exposure to conversational search or large-scale information retrieval.
- Experience bridging model development with real-time serving systems.
Compensation and Benefits
- Competitive compensation package including base pay, equity, and benefits.
- Equity for full-time employees.
- Dollar-for-dollar 401(k) match up to 4%.
- Medical, dental, and vision plans, including pet coverage.
- Up to $10,000 per year in education reimbursement.
- Employee resource groups.
Skills
Machine Learning, ML Infrastructure, Personalization, Search Ranking, Distributed Systems, Data Pipelines, Data Infrastructure, Model Serving, Real-Time Learning, Streaming Systems, Large-Scale Information Retrieval, LLMs, Conversational Search, System Design, AI Tools
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