ML Ops Lead

Join our team as an ML Ops Lead, where you will have the opportunity to assess and improve our data and ML infrastructure. This role requires a senior-level candidate with experience in MLOps, data engineering, and ML platform/infrastructure. You will be responsible for evaluating our current pipelines and data architecture, designing data and ML systems, and implementing the necessary changes. Additionally, you will raise the bar on observability, reliability, and data quality. This position can be a six-month contract or a full-time hire, depending on fit.

• Évaluer l'état actuel des pipelines, de l'architecture des données et des flux de travail ML, et produire un plan priorisé et argumenté pour les changements nécessaires.
• Concevoir des systèmes de données et d'apprentissage automatique ancrés dans les besoins des clients, avec des compromis clairement documentés.
• Rebâtir et renforcer les pipelines, mettre à niveau l'architecture des données et expédier les corrections vous-même.
• Important note: Data shows that men typically apply when meeting 3/10 requirements, while women often wait until it's 10/10. We encourage you to apply if you see a strong (not necessarily perfect) fit
• A track record of walking into complex, fast-grown systems, diagnosing the real problems, and materially improving them
• Staff or principal-level experience in MLOps, data engineering, or ML platform/infrastructure
• Deep experience building and operating production data pipelines and ML workflows at scale
• Strong systems design ability: you can translate customer and product needs into durable, scalable architecture and communicatewrite it down clearly
• Genuinely hands-on: you are as comfortable in the codebase implementing the fix as you are in the design doc
• Fluency with the modern data and ML stack and the cloud infrastructure it runs on
• Experience standing up MLOps practice (CI/CD for models, experiment tracking, feature stores, monitoring) from an early stage
• Background in mission-driven, workforce, or government-adjacent data environments
• Publications, presentations, blog posts, or other public artifacts showcasing your expertise and knowledge of best practices in MLOps
• Comfort mentoring and leveling up a small data and engineering team while you build
• Your alma mater isn't our focus. Your grit, hunger, and drive are. If you learn continuously, tackle challenges head-on, and know your strengths and gaps intimately, you're our person
• Visualization and reporting: Looker
• Machine learning and experimentation: AWS SageMaker
• Languages: SQL, Python
• Data storage and warehousing: PostgreSQL, Redshift, MongoDB (for unstructured data)
• Infrastructure: AWS ecosystem (S3, Lambda, Glue, Redshift)
• Data orchestration and transformation: Airflow, dbt
• Our Tech Stack for Data