Staff Machine Learning Engineer

OpenTable as a Staff Machine Learning Engineer, where you will set the technical direction for building, serving, and operating machine learning systems in production. You will work closely with Machine Learning Scientists and engineers across the company to take models from experimentation to reliable, monitored production services. This role is engineering-focused, requiring hands-on experience with major cloud platforms, production MLOps ownership, and a strong command of Python. Enjoy generous benefits, including paid vacation, mental health support, and the flexibility to work from almost anywhere.

Definir la dirección técnica para la construcción, implementación y operación de sistemas de aprendizaje automático en producción.

Responsibilities

• Colaborar con científicos e ingenieros de aprendizaje automático para llevar modelos desde la experimentación hasta servicios de producción confiables y monitoreados.
• Establecer estándares y patrones de ingeniería para sistemas de aprendizaje automático, incluyendo pruebas, CI/CD, observabilidad, alertas, retrocesos y prácticas de guardia.
• Demonstrated experience training, serving, and deploying ML models at production scale
• A track record of technical leadership: leading multi-quarter projects, influencing engineering decisions beyond your immediate team, and coordinating with Product Managers and other stakeholders
• Hands-on experience with a major cloud platform (AWS, GCP, or Azure) as a primary model-serving environment, including its managed services for deployment, scaling, and observability
• Production MLOps ownership: model and feature monitoring, drift and data-quality detection, retraining and promotion workflows, versioning, safe rollout and rollback, and incident response when a model misbehaves
• Breadth of industry perspective. You have seen how ML systems are built and operated at more than one organization, and can speak to industry-standard practices, common reference architectures, and where the real tradeoffs lie
• Deployment: Docker, Kubernetes, Helm, Prometheus, Graphite / Grafana

Requirements

• Strong command of Python and proficiency in at least one strongly typed language (Java preferred)
• Deep engineering fundamentals: distributed systems, service and API design, concurrency, latency and throughput tradeoffs, testing discipline, and genuine production ownership including on-call
• 7+ years of professional software engineering experience, with a substantial portion spent building and operating machine learning systems in production
• ML: PyTorch, XGBoost / CatBoost, LLMs, LangChain, LangSmith
• Kubernetes in production at meaningful scale

Preferred

• Pipelines: Spark, Airflow, EMR, SageMaker, Snowflake, S3, Delta Lake
• Applied ML depth in ranking, recommendations, classification, NLP, RAG, and/or agentic systems
• Experience developing ETL jobs (especially Spark) or data warehouse infrastructure
• Experience introducing new tooling or platform capability to a team and driving adoption
• Familiarity with A/B testing design and analysis best practices

Build: Poetry, FastAPI, Flask, Gunicorn / Uvicorn, Spring, Maven, TeamCity

Infrastructure: Kafka, ElasticSearch, Postgres, MongoDB, Redis, Qdrant

• We do not expect experience with everything on this list - it is here so you know what you would be working with:
• Serving LLMs in production: inference infrastructure, GPU utilization, batching and caching strategies, quantization, and managing the latency/cost frontier (vLLM, TGI, TensorRT-LLM, or similar)