Principal Software Engineer - Machine Learning
Meet Fetch AI & Data AI & Data at Fetch sit at the center of how we understand our business, make decisions, and build intelligent products. The organization operates as an integrated AI & data ecosystem, spanning multiple disciplines, including data engineering, analytics engineering, machine learning, experimentation, and data platforms, all working together to turn data into durable business and customer impact. Teams operate in complex problem spaces where requirements evolve, tradeoffs are constant, and the right answer is rarely obvious. Success depends on strong technical judgment, comfort with ambiguity, and the ability to gather context and make informed decisions while balancing quality, performance, scalability, and responsible use. Practitioners across this org contribute hands-on to production systems, analytical foundations, and intelligent features. You will collaborate closely with product, platform, and engineering partners, help shape standards and best practices, and ensure our AI and data capabilities scale reliably as Fetch grows. About the Role: Fetch is building the future of personalized consumer experiences. We’re looking for a Principal Machine Learning Engineer to design and scale systems that power personalization, relevance, and ranking across our platform. This is a high-impact role where you’ll drive new initiatives, mentor other engineers, and shape the technical direction of ML at Fetch. This is a full-time role that can be held from one of our US offices or remotely in the United States. Role Responsibilities:
- Build and scale ML infrastructure for personalization, search, ranking, and ad tech at consumer scale.
- Design and implement zero-to-one systems, including real-time learning and data pipelines.
- Lead technical design, architecture, and cross-team alignment for major ML initiatives.
- Mentor engineers and help raise the bar on technical execution and design quality.
- Partner with product and engineering teams to create dynamic systems that adapt to evolving user preferences.
- Designing features and validating ideas with ChatGPT & Claude sandboxes.
- Leveraging AI for code generation and technical prototyping.
- Using AI assistants for systems architecture diagramming and design validation.
- Exploring LLMs to enhance personalization, conversational search, and feature creation.
Minimum Requirements:
- 15 years of experience building and scaling ML infrastructure in support of personalization, relevance, search, or ad tech systems.
- Deep hands-on expertise in data infrastructure, distributed systems, and large-scale data pipelines.
- Experience working at a consumer product company with ML models operating at scale.
- Prior contributions to ranking, personalization, or ad tech systems with measurable business impact.
- Strong systems design skills, with a track record of leading architecture and communicating design tradeoffs.
- Experience mentoring and elevating other engineers.
- Success leading zero-to-one technical initiatives and delivering new infrastructure or ML systems from scratch.
- Ability to operate in high levels of ambiguity with minimal direction, prioritizing effectively and driving impact.
- Bachelor’s degree in Computer Science, Engineering, or a related technical field.
Preferred Requirements:
- Strong expertise with LLMs and their application in personalization, feature creation, and conversational search.
- Experience with streaming/real-time learning systems.
- Exposure to conversational search or large-scale information retrieval.
- Previous work bridging model development with real-time serving systems.