ML Ops Lead, FutureFit

• 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
• Staff or principal-level experience in MLOps, data engineering, or ML platform/infrastructure
• A track record of walking into complex, fast-grown systems, diagnosing the real problems, and materially improving them
• 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
• Deep experience building and operating production data pipelines and ML workflows at scale
• Fluency with the modern data and ML stack and the cloud infrastructure it runs on
• 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
• Our Tech Stack for Data
• Languages: SQL, Python
• Data orchestration and transformation: Airflow, dbt
• Data storage and warehousing: PostgreSQL, Redshift, MongoDB (for unstructured data)
• Machine learning and experimentation: AWS SageMaker
• Visualization and reporting: Looker
• Infrastructure: AWS ecosystem (S3, Lambda, Glue, Redshift)

Desirable

• Contract (6 months, potential to convert) or Full-Time
• We're seeking a Staff / Principal MLOps Engineer to join our team
• Our data and ML infrastructure has grown fast alongside the business, and it now needs senior ownership to bring it to where it should be
• You will come in, assess the state of our pipelines and data architecture with clear eyes, decide what to fix and in what order, and then go fix it yourself
• This is a role for someone senior enough to write systems designs grounded in what our customers actually need, and hands-on enough to be in the codebase implementing them
• We are open to running this as a six-month contract or as a full-time hire, depending on fit and what you are looking for
• Assessment and plan: Evaluate our current pipelines, data architecture, and ML workflows, and produce a prioritized, opinionated plan for what needs to change and why
• Systems design: Design data and ML systems that are anchored in customer needs and built to last, with clear tradeoffs documented so the team can build on them
• Hands-on implementation: Do the work: rebuild and harden pipelines, upgrade the data architecture, and ship the fixes yourself rather than handing off a deck
• Reliability and standards: Raise the bar on observability, reliability, and data quality, establishing the patterns and practices the rest of the team can run with

• At FutureFit AI, our hiring process is designed to help you assess whether this role and our culture are the right fit based on your unique skills, mindset, and experiences. We move fast and work with intensity, so we want you to get a real sense of that from the start
• Each journey includes a mix of interviews and a performance challenge. For this role, that might look like:
• Online Application
• Initial Screen with Director of People & Culture
• Interview with Hiring Manager
• Performance Challenge
• Final 1:1 Interviews
• Final Decision
• Generally, this entire process takes around 6 weeks, although the timing can vary due to specific candidate circumstances