Senior AI Engineer, FutureFit

• 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
• 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

Desirable

• 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
• 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
• 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

• 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