Staff Machine Learning Engineer

The Staff Machine Learning Engineer (player-coach) will design, build, test, and deploy production AI products and the underlying platform that enables agentic, LLM-powered capabilities on Google Cloud. The role combines applied machine learning with software engineering rigor, including support for stateful and transactional systems, and includes mentoring responsibilities.

Key Responsibilities

• Design, build, test, and deploy production agentic and LLM-powered products end-to-end on Google Cloud, including the shared platform, tooling, and harnesses beneath them.
• Apply strong software-engineering rigor to services that involve stateful and transactional (OLTP) systems, with focus on data modeling, concurrency, idempotency, reliability, observability, and testing.
• Use classical ML, statistics, and experimentation when appropriate, including predictive modeling, statistical inference, hypothesis testing, and causal or incrementality measurement.
• Build scalable automation tools for domains such as recommendation and personalization, conversational and customer experiences, and measurement and incrementality.
• Produce design documents, maintain standards for clean code and documentation, and review designs and deliverables.
• Mentor and develop engineers and scientists, including participation in early-career and student mentorship efforts.
• Collaborate with engineering, product, and domain-expert teams on cross-functional automation, measurement, and modeling initiatives.
• Participate in ELC’s diversity and inclusion agenda.

Required Qualifications

• BS/BA in a quantitative or technical field (for example, Computer Science, Statistics, Mathematics, Physics, Engineering, Operations Research, Economics) or equivalent practical experience; a graduate degree is a plus.
• 5+ years of experience (or 3+ years with a graduate degree) across Software Engineering, Machine Learning, and/or Data Science.
• Strong general software engineering in Python, including clean API and service design, data-structures and algorithms, and a disciplined approach to testing. Experience building and operating production-grade services, including stateful/transactional systems and databases using SQL and/or NoSQL.
• Production experience deploying and operating LLM-powered or agentic systems (examples: retrieval-augmented generation, agents and tool use, structured outputs, evaluation, safety) or a strong production ML background with a clear interest in transitioning into this space.
• Experience deploying and operating systems on a major cloud platform, with Google Cloud as a plus, and sound data-processing practices at scale.
• Experience using agentic/AI-assisted development tools, or a strong willingness to adopt them with judgment to protect quality.
• A track record of mentorship, continuous learning mindset, bias for simplicity, and collaborative shared-ownership working style.

Technologies

• Python
• Google Cloud
• SQL, NoSQL
• Retrieval-augmented generation
• LLM-powered systems
• Agentic systems
• Vertex AI
• Cloud Run
• BigQuery
• Firestore
• Pub/Sub
• FastAPI
• Container-based CI/CD
• Model Context Protocol (MCP)

Benefits

• Health insurance coverage (medical, dental, and vision insurance)
• Wellness and family support programs
• Life and disability insurance
• Retirement savings plans
• Paid leave programs
• Education-related programs
• Paid holidays and vacation time
• Bonus program (highly competitive), with the possibility for overachievement
• Participation in the share incentive plan

Preferred / Strong Plus

• Depth in classical ML, data science, and statistics, including statistical modeling, hypothesis testing and experimentation, causal inference and incrementality, and predictive modeling.
• Hands-on experience in NLP, agent frameworks, or advanced statistical methods.
• Familiarity with the stack, including 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.

Compensation and Pay Range

• The anticipated base salary range is $119,300.00 to $196,600.00 per year.
• Exact compensation depends on factors such as experience, skills, education, and budget.
• Salary range may vary based on geographic location.
• In addition to base salary, the position is eligible for participation in a highly competitive bonus program and the share incentive plan.

Location

New York, NY (onsite)