Staff Machine Learning Engineer

Responsibilities

• Set technical direction for model serving, deployment, monitoring, architecture, engineering patterns, and production tradeoffs.
• Build and operate high-throughput, low-latency, observable production ML services.
• Establish engineering standards for testing, CI/CD, observability, alerting, rollback, and on-call practices.
• Own MLOps capabilities including model and feature monitoring, drift and data-quality detection, retraining, promotion workflows, versioning, safe rollout, and incident response.
• Lead multi-quarter projects and influence engineering decisions across teams and stakeholders.
• Scope and prioritize ambiguous cross-team problems with Product Managers and other stakeholders.
• Develop capabilities for AI agents, recommendations, predictive models, multimodal restaurant-content understanding, and partner business insights.

Requirements

• 7+ years of professional software engineering experience, including substantial experience building and operating machine learning systems in production.
• Experience working across more than one organization and applying industry-standard ML system architectures and operating practices.
• Hands-on experience with AWS, GCP, or Azure as a primary model-serving environment, including managed deployment, scaling, and observability services.
• Strong fundamentals in distributed systems, service and API design, concurrency, latency and throughput tradeoffs, testing, and production ownership with on-call responsibility.
• Strong command of Python and proficiency in at least one strongly typed language, preferably Java.
• Demonstrated experience training, serving, and deploying ML models at production scale.
• Experience with production MLOps, including monitoring, data-quality detection, retraining, promotion, versioning, rollout, rollback, and incident response.
• Track record of technical leadership across multi-quarter projects and cross-functional stakeholders.
• Preferred experience serving LLMs in production, including inference infrastructure, GPU utilization, batching, caching, quantization, and latency-cost optimization.
• Preferred applied ML experience in ranking, recommendations, classification, NLP, RAG, or agentic systems.
• Preferred Kubernetes experience at meaningful production scale.
• Preferred experience developing ETL jobs, especially with Spark, or data warehouse infrastructure.
• Preferred familiarity with A/B testing design and analysis and with introducing tooling or platform capabilities and driving adoption.

Benefits

• Hybrid workplace with an expectation of working in the office two days per week.
• Work from almost anywhere for up to 20 days per year.
• Company-paid therapy sessions through SpringHealth and a company-paid Headspace subscription.
• Annual company-wide week off.
• Paid parental leave, generous paid vacation, birthday time off, 20 days of paid time off, and paid volunteer time.
• Development Dollars, leadership development, and access to thousands of on-demand e-learning courses.
• Travel discounts, Employee Resource Groups, private health and dental insurance, life and disability insurance.
• Health benefits, flexible spending account, retirement benefits, paid sick, medical, bereavement, floating and company holidays, and potential annual bonus and equity eligibility.