Scan your resume against ATS criteria for this Staff ML Platform Engineer (MLOps) role at Futurefitai.
If that resonates deeply with you, this could be your next career move. We're seeking someone who leads with humility, pursues audacious goals, and is motivated by meaningful impact on people and the world.
At FutureFit AI, our core mission is to help more people get to better jobs faster and cheaper, with a specific focus on those facing barriers to opportunity. Our work helps resolve the growing issue of economic inequality, ensuring that no is left behind in the future of work. Our AI-powered platform brings efficiency and insight to workforce development, replacing outdated systems and unlocking human potential at scale.
Ready to make an impact? Apply today.
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.
We're seeking a
to build the platform our ML and LLM-powered products run on. Our ML footprint has grown fast, but the layer underneath it has not kept pace. You'll own that layer end to end: how models get built, deployed, evaluated, and served; how compute and environments get provisioned and managed; how our LLM calls get routed and optimized for cost; and how we know quickly when a recommender goes down or goes off the rails.
This is a build role and an operate role: when a model regresses or a recommendation looks wrong, you can trace it back to the inputs that produced it and help fix it.
Our ML footprint has grown quickly: batch models, real-time recommendation models, LLM-powered features, and daily pipelines processing every available job across the US and Canada. What we haven't built is the platform underneath it: consistent compute and environments, a disciplined path from experiment to production, cost-aware routing across LLMs, and the monitoring that tells us fast when something breaks.
You'll assess our current pipelines and ML workflows with clear eyes, decide what to build and in what order, then build it. This is greenfield platform work with direct influence on production models and how our ML team operates, and it comes with real operational ownership: you will be close enough to the running systems to debug them, not step removed.
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
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.
Approximately 2-3 trips per year, including our company off-site in August.
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, 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
Final Decision
Generally, this entire process takes around 6 weeks, although the timing can vary due to specific candidate circumstances.
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.
Be Curious
Drive to Outcomes
Raise the Bar
Speed Matters
Own It
We Over Me
At FutureFit, we use artificial intelligence (AI) tools to make our hiring process more efficient, consistent, and equitable—never to replace human judgment
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.
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FutureFit AI All rights reserved, we are proud to be an equal opportunity workplace. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, religion, color, gender identity, sexual orientation, age, disability, veteran status, or other applicable legally protected characteristics. We encourage people of different backgrounds, experiences, abilities, and perspectives to apply._
visioned and managed; how our LLM calls get routed and optimized for cost; and how we know quickly when a recommender goes down or goes off the rails.
How to apply for Staff ML Platform Engineer (MLOps) at Futurefitai?
Click the "Apply on Company Website" button on this page to submit your application directly on the employer's official portal.
What is the salary for this role?
The salary for this role is $172,000-$215,000 per annum.
What experience is required?
This position is open to freshers and experienced candidates.
Is this position still open?
Yes, currently active and accepting applications.
Staff ML Platform Engineer (MLOps)
Futurefitai · Remote (US)
