Staff / Principal MLOps Engineer

Contract (6 months, potential to convert) or Full-Time | USD $195,000 - $220,000 (NYC) | Remote (US or Canada)

The Opportunity
Your Role

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
Required Experience

• 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

Bonus Points

• 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

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)

Your Education

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.

The Logistics - Location, Compensation
Location

[CA/US Remote] We are open to candidates living anywhere in Canada or the US. For candidates living in Toronto, our office is conveniently located at 325 Front St West (a short walk from Union Station). For candidates living in New York City, our office is at 18 W 18th Street. You are welcome to come in on a hybrid schedule.
Travel Expectations

Although this role is remote, you may be expected to travel up to once per quarter for offsites and team gatherings.
Compensation

We are open to engaging this role as a six-month contract with potential to convert, or as a full-time hire. For the full-time path, the base salary range is USD $195,000 to $245,000 for candidates based in New York and CAD $175,000 to $220,000 for candidates based in Toronto, benchmarked to the middle of the market for comparable venture-backed companies. For the contract path, the rate range is USD $120 to $170 per hour, commensurate with level. The final figure reflects the varying levels of expertise and responsibilities that will be determined through the interview process, based on applied experience and other criteria established by the hiring committee.

The Hiring Journey
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

• Be Curious
• Drive to Outcomes
• Raise the Bar
• Speed Matters
• Own It
• We Over Me

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.