We're seeking a Senior AI Engineer to join our team.

You will build the AI products that sit directly between a job seeker and their next, better job. That means LLM-based systems people actually rely on: chat-based coaching that meets someone where they are, agentic workflows that carry out real steps on a person's behalf (completing an eligibility form, assembling an application), and orchestration that pulls the right context from case management systems and other data sources so those interactions are accurate and grounded.

The centerpiece of this role is our coaching products. We want to tell a job seeker not just what jobs exist, but which specific next step is most likely to move their life forward, grounded in evidence we can defend: transitions we have observed in labor market data, and interventions we have measured as causally lifting wages and lifetime earnings. You will own both halves: establishing what the evidence supports, and the engineering that turns it into a product a person can use.

This is a hands-on, high-ownership role on a small team. You will partner closely with Engineering, Product, and the VP of Data & AI, and you will have wide latitude to decide how these systems get built.

What You'll Own

• LLM product development: Design, build, and ship LLM-based product experiences end to end: conversational coaching, agentic workflows that complete multi-step tasks for a user, and the tool use and orchestration layers underneath them.
• Coaching and pathways, grounded in evidence: Build the products that guide a job seeker to their next best step, and do the analysis that earns those recommendations: observed career transitions, causal impact on wages and lifetime earnings, and an honest assessment of what our data can and cannot claim.
• Evaluation and quality: Own how we know our AI is any good. Build the eval harnesses, offline and online quality measurement, regression tracking, and human-in-the-loop review that let us ship fast without shipping something harmful or wrong.
• Applied ML beyond LLMs: Build predictive, classification, and ranking models when that is the right tool for the product problem, and have the judgment to know when it is.
• Data engineering for AI: Prepare, label, govern, and maintain the pipelines and knowledge sources our AI systems depend on, so retrieval and reasoning are grounded in data we trust.
• Integration engineering: Connect our AI systems safely to internal tools, databases, SaaS products, case management systems, and enterprise workflows, with the auth, guardrails, and failure handling our customers' environments demand.

Where This Role Can Go

This role carries real influence over how we build. You will help shape the patterns our AI work runs on: how we prompt, how we evaluate, and how we decide what an agent is allowed to do on someone's behalf. You will do it while building brand new products, with room to show technical leadership across the team.

From there, the path is yours to steer: deeper technical leadership over our AI platform, or broader ownership of the coaching and pathways products themselves. What matters most to us is a willingness to learn, adapt as the product changes, and stay open to unfamiliar work, and we'll build the path with you.

Required Experience

• Strong applied ML/AI engineering experience (roughly 5+ years), with a track record of shipping systems into real products used by real people
• Demonstrated LLM product experience: you have built and shipped LLM-based features to production (not prototypes), including prompt engineering, agentic workflows, tool use and function calling, retrieval, and the orchestration that holds it together
• Real evaluation discipline: you know how to measure whether an LLM system is working. You have built evals, defined quality metrics for open-ended output, caught regressions before users did, and can speak concretely about where your systems failed and how you found out
• Classical ML depth: solid grounding in machine learning fundamentals, with hands-on experience building and rigorously evaluating predictive or classification models. This is a firm criterion: we need someone who reaches for an LLM because it is right, not because it is the only tool they have
• Fluency in Python and SQL, with real comfort in production codebases
• Experience building the data and integration foundation your AI depends on: ingestion and transformation pipelines, labeling, knowledge sources, and governance, plus connecting to external and enterprise systems (APIs, databases, SaaS platforms, third-party data providers) despite unreliable delivery, inconsistent quality, and auth complexity
• Sound instincts about safety and guardrails in a consequential domain. Our users are navigating benefits, eligibility, and career decisions, and a confidently wrong answer has a real cost
• Clear communication: you can explain a model, an agent's behavior, or an evaluation result and its tradeoffs to a non-technical audience

• Experience with AWS and SageMaker, or building and deploying AI systems on AWS more broadly
• Experience with jobs-and-skills, HR, or labor market data, or with skills/occupation frameworks such as O*NET or ESCO
• Experience integrating with case management systems, government systems, or other public-sector data environments
• Experience with recommender systems, ranking, or search at scale
• Experience with retrieval-augmented generation at production scale, including retrieval quality evaluation
• Experience with labor economics, program evaluation, or measuring the impact of workforce interventions
• Publications, talks, blog posts, or open source work showing your depth in AI or ML engineering

Our Tech Stack for Data & AI

• Languages: Python, SQL
• AI and LLM: modern LLM and agent tooling, including agent orchestration frameworks (for example, LangChain or LangGraph), vector search, and RAG tooling
• Machine learning and NLP: scikit-learn, modern NLP and embedding tooling, AWS SageMaker
• Orchestration and transformation: Airflow, dbt
• Storage and warehousing: PostgreSQL, Redshift, MongoDB
• Cloud: AWS
• Visualization and reporting: Looker, Quicksight

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.

Location

Remote across the US and Canada. We are open to candidates living anywhere in either country, with at least 5 to 6 hours of daily overlap with our EST-based team required. For candidates living in Toronto, our office is conveniently located at 325 Front St West (a short walk from Union Station), and you are welcome to drop by, though it is entirely optional and not a hybrid requirement.

Travel Expectations

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

Compensation

The base salary range for this role is USD $150,000 to $185,000 for candidates based in the United States and CAD $130,000 to $165,000 for candidates based in Canada, benchmarked to the middle of the market for comparable venture-backed companies. Where you land in the range reflects your applied experience and other criteria established by the hiring committee.

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:

• Online Application
• Initial Screen with our Recruiter
• 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.

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.

• 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.

© 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.