futurefitai - Staff ML Platform Engineer (MLOps)

New York City, New York, USA - Hybrid$195k - $220k4mo ago

Remote Staff NA Cloud Computing Artificial Intelligence Platform Engineer Principal Data Scientist Team Lead MLOps Documentation Reporting MongoDB dbt

Requirements

• Staff-level, hands-on experience in MLOps, ML platform, or ML infrastructure (we're also open to Data Platform Engineer, ML Infrastructure Engineer, or Data Scientist backgrounds with strong platform ownership: the title on your last resume matters less than what you actually built) • Experience standing up MLOps practice end to end: CI/CD for models, experiment tracking, model registries, deployment workflows, and monitoring • Production experience with LLM-based systems: serving, prompt and response evaluation, routing across models and providers, and managing cost and latency tradeoffs. If you've done this with traditional ML systems and can show you pick up LLM tooling fast, that counts too • Experience operating models in both batch and real-time serving contexts • Hands-on with compute provisioning and environment management: containers, reproducible training and serving environments, and keeping frameworks and packages current on a fast-moving stack without breaking production • Experience running controlled model experiments in production: A/B tests, shadow or canary deploys, holdouts, and the judgment to set success criteria before ramping • Depth in observability and traceability for production ML: drift and regression detection, alerting that catches a recommender going down or going off the rails, lineage, and the ability to trace a prediction back to the inputs that produced it and reproduce it after the fact • Hands-on operational experience: you have carried the pager or its equivalent, debugged production ML incidents under time pressure, and fixed systems you did not originally build • Comfort doing the data engineering the platform needs: pipelines, feature computation and storage, and keeping training and serving features consistent • 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 product needs into durable architecture and stay close enough to the code to build it yourself • Interest in growing into model development yourself. This role starts on the platform side, but the line between platform and modeling is thin here, and we would rather hire someone who wants to cross it • Feature store experience. We are early here, so you would be shaping it rather than inheriting it • Experience evaluating AI/ML observability or LLM evaluation vendors, with judgment on when to buy versus build • Background in mission-driven, workforce, or government-adjacent data environments • Comfort mentoring a small data and engineering team while you build • Languages: SQL, Python • Data orchestration and transformation: Airflow, dbt • Data storage and warehousing: PostgreSQL, Redshift, MongoDB • Machine learning and model serving: AWS SageMaker (PyTorch models, artifact upload to S3, model registration), serving real-time and batch inference • Visualization and reporting: Looker, Quicksight • Infrastructure: AWS (S3, Redshift), GitHub Actions for CI/CD • 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. • Remote (CA/US). Toronto-based candidates are welcome to work from our office at 325 Front St West if they prefer, but it's optional, not a hybrid requirement. • Travel Expectations • Approximately 2-3 trips per year, including our company off-site in August.

Responsibilities

• 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. • WHAT YOU'LL OWN • 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. • What You Bring - Experiences, Skills, Education • The Hiring Journey • 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: • 1. Online Application • 2. Initial Screen with Director of People & Culture • 3. Interview with Hiring Manager • 4. Performance Challenge • 5. Final 1:1 Interviews • 6. Final Decision • Generally, this entire process takes around 6 weeks, although the timing can vary due to specific candidate circumstances. • READY TO SHAPE THE FUTURE OF WORK? • At FutureFit AI, we're not just building a company—we're transforming how talent and opportunity connect. Join our driven team united by a commitment to job seekers and the workforce ecosystems we serve. • COMPANY SNAPSHOT: • Team: 30-50 across US and Canada (hubs in NYC and Toronto) • Customers: Workforce development agencies and intermediaries, government agencies, employers • Industry: SaaS/AI technology • Funding: Bootstrapped 0-1, then raised funding led by JP Morgan • Structure: Growth, Customer Success, Product, Engineering, Data, People & Culture, Finance & Operations • OUR CORE PRINCIPLES • Drive to Outcomes • Raise the Bar • Speed Matters • USE OF AI IN HIRING • At FutureFit, we use artificial intelligence (AI) tools to make our hiring process more efficient, consistent, and equitable—never to replace human judgment. We use AI in the following ways: • Screening support: AI may help us compare applications against the skills and experience required for a specific role. These skills are defined by the hiring team for each position. A human reviews each application, with the AI assessment as just one input. • Interview support: In some interviews, we may use an AI notetaker to summarize the discussion so interviewers can focus on being present in the conversation. • Insights, not decisions: AI provides data points to support our team’s evaluation but does not make or recommend final hiring decisions. Every hiring decision is made by people. • We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, perform essential job functions, and receive other benefits and privileges of employment. Please contact us to request an accommodation.

Benefits

• As a remote-first company, we benchmark to the national market for comparable roles at institutionally-funded startups, targeting the middle of market. Bands reflect applied experience, with room to grow. • If establishing the standards a growing ML org runs on, rather than inheriting a finished one, is the kind of problem you want, let's talk. • 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 Talent Acquisition • Interview with Hiring Manager • Performance Challenge • Final 1:1 Interviews • Generally, this entire process takes around 6 weeks, although the timing can vary due to specific candidate circumstances. • Ready to shape the future of work? • At FutureFit AI, we're not just building a company—we're transforming how talent and opportunity connect. Join our driven team united by a commitment to job seekers and the workforce ecosystems we serve. • Company Snapshot: • Team: 30-50 across US and Canada (hubs in NYC and Toronto) • Customers: Workforce development agencies and intermediaries, government agencies, employers • Industry: SaaS/AI technology • Funding: Bootstrapped 0-1, then raised funding led by JP Morgan • Structure: Growth, Customer Success, Product, Engineering, Data, People & Culture, Finance & Operations • Our Core Principles • Drive to Outcomes • Use of AI in Hiring • At FutureFit, we use artificial intelligence (AI) tools to make our hiring process more efficient, consistent, and equitable—never to replace human judgment. We use AI in the following ways: • Screening support: AI may help us compare applications against the skills and experience required for a specific role. These skills are defined by the hiring team for each position. A human reviews each application, with the AI assessment as just one input. • Interview support: In some interviews, we may use an AI notetaker to summarize the discussion so interviewers can focus on being present in the conversation. • Insights, not decisions: AI provides data points to support our team’s evaluation but does not make or recommend final hiring decisions. Every hiring decision is made by people. • We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, perform essential job functions, and receive other benefits and privileges of employment. Please contact us to request an accommodation.