Note: The job is a remote job and is open to candidates in USA. FutureFit AI is an AI-powered workforce technology company focused on helping people find better jobs and improving workforce development. The company is seeking a Staff ML Platform Engineer to build and operate the platform supporting its machine learning and LLM-powered products, including model deployment, compute, experimentation, routing, monitoring, and traceability. The role will establish scalable MLOps standards while maintaining hands-on operational ownership of production systems.
Responsibilities
- Assessment and plan: Evaluate our current pipelines, data architecture, and ML workflows, and produce a prioritized, opinionated plan for what needs to change
- Platform foundations and optimization: Own compute provisioning and environment management, keep training and serving environments reproducible, keep frameworks and packages current across services and model images, and tune latency, throughput, and spend, all without destabilizing production
- LLM infrastructure and smart routing: Build the layer our LLM features run on, including smart routing that sends each request to the cheapest model that can handle it well, plus the prompt and response evaluation needed to prove quality holds when we route down
- Experimentation and safe rollout: Give us a real discipline for A/B testing models before they are fully ramped: shadow deploys, canaries, holdouts, and success criteria agreed in advance, so a model earns its way into production instead of being switched on
- Observability and traceability: Know within minutes when a recommender goes down or starts drifting, and be able to explain why: model and data monitoring, alerting, regression detection, lineage, and enough traceability to reproduce a questionable recommendation on demand or trace a prediction back to the inputs that produced it (we currently use Braintrust; comparable tooling counts too)
- Hands-on operations: Stay close enough to the running systems to operate them. You will work with the team to keep models online, but when something breaks in production you can dive in and help fix it, including models other people built
- Data and feature infrastructure: Own how features are computed, stored, and served consistently between training and inference, and take on the data engineering the team needs along the way
- Standards, not sole ownership: Establish the deployment and monitoring standards the rest of the team can run with. You are building shared ownership, not becoming the only person who keeps things online
Skills
- 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
- Remote (CA/US)
- Approximately 2-3 trips per year, including our company off-site in August
- 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
- If you learn continuously, tackle challenges head-on, and know your strengths and gaps intimately, you're our person
- 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
Benefits
- Remote-first company; this role is remote.
- 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.
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