Staff Machine Learning Engineer

Description

As a Staff Machine Learning Engineer youre a player-coach: a strong hands-on contributor who also sets standards reviews others work authors the designs others build against and mentors the team. You design build test and deploy production AI products and the platform beneath them and you shape how we build and integrate AI systems working with engineers scientists product managers and domain experts to turn ambiguous problems into reliable scalable deliverables.
The core of this role is engineering: agentic AI products and their platform including stateful transactional services where reliability and data-modeling rigor matter. Classical ML statistics and experimentation are valued and let us take on predictive and inference work when the roadmap calls for it but you may spend long stretches purely on AI-product and platform engineering so were looking for someone energized by that work.
Responsibilities
Design build test and deploy production agentic / LLM-powered products end-to-end on Google Cloud along with the shared platform tooling and harnesses beneath them.
Bring strong software-engineering rigor to our services including stateful and transactional (OLTP) systems across data modeling concurrency idempotency reliability observability and testing.
Apply classical ML statistics and experimentation where the problem calls for it (predictive modeling statistical inference hypothesis testing causal / incrementality measurement).
Build scalable tools that drive automation across domains such as recommendation and personalization conversational and customer experiences and measurement and incrementality.
Author design documents uphold standards for clean code and documentation and review others designs and deliverables.
Mentor and grow engineers and scientists and support our early-career and student mentorship efforts.
Partner with engineering product and domain-expert teams on major cross-functional automation measurement and modeling efforts.
At least 80% technical contributor up to 20% leadership / management.
Actively participate in ELCs diversity and inclusion agenda.

Qualifications

BS/BA in a quantitative or technical field (e.g. Computer Science Statistics Mathematics Physics Engineering Operations Research Economics) or equivalent practical experience; a graduate degree is a plus.
5 years of experience (3 with a graduate degree) across Software Engineering Machine Learning and/or Data Science.
Strong general software engineering in Python clean API and service design data-structure and algorithm proficiency and real testing discipline with experience building and operating production-grade services including stateful / transactional systems and databases (SQL and/or NoSQL).
Experience building and deploying LLM-powered or agentic systems in production (e.g. retrieval-augmented generation agents and tool use structured outputs evaluation safety) or a strong production-ML background paired with a clear appetite to work in this space.
Experience deploying and operating systems on a major cloud platform (Google Cloud a plus) with sound data-processing practices at scale.
Uses agentic / AI-assisted development tools or is eager to adopt them in earnest with judgment about where they help and how to keep quality high.
A track record of mentorship a continuous-learning mindset a bias for simplicity and a collaborative shared-ownership working style.
Preferred / a strong plus
Genuine classical ML / data science / statistics depth statistical modeling hypothesis testing and experimentation causal inference and incrementality and predictive modeling. We value this as a way to de-risk future modeling work; it is hard to acquire on the job so we welcome it even when the immediate work is engineering-focused.
Hands-on experience in NLP agent frameworks or advanced statistical methods.
Familiarity with our stack: Google Cloud (Vertex AI Cloud Run BigQuery Firestore Pub/Sub) async Python (FastAPI) container-based CI/CD and the Model Context Protocol (MCP).
Experience designing a technical roadmap and leading execution in a business environment.