Remote Senior ML Engineer

About the RoleWe're hiring a Senior ML Engineer to be the data team's owner of production ML and AI agent systems. You'll take models from prototype to production, build and maintain the low-latency ML API that powers our Next Best Action (NBA) engine, and partner with our HAL team to ship LLM agents that turn NBA recommendations into real conversations with credit union members and partners. This is a builder's role at a builder's moment: NBA is going live, the agent infrastructure is being shaped now, and you'll define how Clutch does production AI for years to come.About the TeamThe Data team today is five people: one data scientist, two data engineers, one data analyst, and one product manager. We're small, ambitious, and shipping fast — two ML models heading to production, an ML API being built, and two AI agents (one customer-facing, one partner-facing) in active development. You'll be the senior technical voice for ML and AI engineering inside the team, and the bridge to HAL, the platform team that builds Clutch's agent runtime. Expect tight feedback loops, real autonomy, and a team that values pragmatism over purity.What You’ll DoWithin 3 months, you will:* Take ownership of the ML API that serves NBA recommendations, partnering with the data engineer who's been building it, and harden it for low-latency production traffic\n* Ship your first agent tool contract end-to-end: schema design, handler implementation, structured-error contract, unit tests, deployed via HAL's runtime\n* Set up the eval foundation for our agents: golden transcripts, rubric-based judges, regression suites that run on every prompt or model change\n* Build a working relationship with HAL and become the data team's go-to on agent infrastructure decisions\n\nWithin 6 months, you will:* Be the primary owner (with data engineer support) of the ML API and the agent tool layer that wraps NBA and our ML models\n* Have shipped at least one production-grade agent (customer-facing or partner-facing) with prompt versioning, evals, observability, and multi-tenant gating in place\n* Define the data team's playbook for shipping a new ML model as an LLM-callable tool, end-to-end\n* Mentor the data engineers on ML/AI patterns so they can confidently support and extend the systems you own\n\nWithin 9 months, you will:* Operate as the technical lead within the data team for NBA production AI at Clutch — the person other teams come to when they want to understand how NBA ships ML and agents responsibly\n* Have measurably improved agent cost and latency (target: 30%+ reduction on P95 latency or per-conversation cost on at least one agent)\n* Be shaping the data team's roadmap for the next generation of ML and AI products, in partnership with the PM and data scientist\n* Help us decide what to hire next as the team scales\n\nWhat You’ll BringRequired* 7+ years of engineering experience, with a proven track record of building and shipping production ML systems — you've taken models from prototype to production and own what happens after deploy\n* Strong Python — most of the work (ML training, evaluation, the ML API, data pipelines) is in Python, and you're comfortable in production codebases, not just notebooks. Some TypeScript is involved for tool contracts and integration with our agent runtime — you don't need to be an expert, comfort with a second language is enough\n* Tool-design discipline for LLM consumption. Can take an ML model or data source and shape it into an LLM-callable tool with narrow input/output schemas, identity-required and scope-gated dispatch, and structured-error contracts (RATE_LIMITED, UPSTREAM_ERROR, NOT_FOUND) that the agent runtime converts to graceful tool-results instead of crashing\n* Eval discipline for non-deterministic systems. You treat evals as the unit-test equivalent for agents: golden transcripts, rubric-based judges, regression suites that run on every prompt or model change. You understand the difference between offline metrics and online evals, and use both\n* Prompt-shape literacy. You read a system prompt the way another engineer reads code: audience, register, compliance guardrails, template-var allow-list, allowed-tools section. You debug "why did the agent do that?" by reading the prompt and tool descriptions before reaching for model swaps. You've shipped at least one agent where the prompt was version-controlled and reviewed as code\n* Tool implementation rigor. You build handlers behind tool contracts with identity fields read from request context (never from LLM-supplied args), output re-parsed through the tool's schema before return, structured-error throws on every failure path, and unit tests covering both happy path and each named error. You have a story about a tool you shipped, a bug production traffic surfaced, and how you hardened it\n* Experience building and maintaining low-latency production APIs (FastAPI, BentoML, or equivalent), with opinions on serving, batching, and caching\n* Comfortable in AWS (Lambda especially), Docker, and GitHub-based workflows\n* You use AI tooling actively in your engineering workflow — not as a novelty, but as a default. You'll be expected to demonstrate this during the technical evaluation\n\nDesired* Production agent observability: reading audit rows, distributed traces, per-tool latency and error metrics\n* Cost and latency tradeoff intuition in agent loops — has measurably reduced per-conversation cost or P95 latency on a live agent\n* Familiarity with an agent runtime framework (Vercel AI SDK, LangChain, LlamaIndex, or equivalent)\n* Multi-tenant agent gating experience\n* Prior SaaS and/or FinTech experience\n* Nice to have but not required: Databricks, PySpark, Terraform\n\nPlease note that this role may evolve as our business needs change, so we appreciate your flexibility and adaptability.What’s In It For You?* Remote Flexibility: Enjoy the freedom of remote work from anywhere, balancing life and career seamlessly.\n* Unforgettable Off-Sites: Twice a year, bond with colleagues in exciting destinations, fostering teamwork and fresh ideas.\n* Paid Time Off and National Holidays: Enjoy 20 PTO days yearly and the National Holidays for relaxation and rejuvenation.\n* Stock Options: Joining us means having a stake in our success, so you'll receive stock options as part of your compensation package.\n* Home Office Setup: Create your ideal workspace with a dedicated budget for home office essentials.\n* Work Trip Budget: Grow personally and professionally with a budget for work-related trips and co-working.\n\nAbout UsClutch is a revolutionary vertical SaaS company, proudly backed by Andreessen Horowitz (A16z), aimed at revolutionizing the way Credit Unions engage and change the lives of their members. As a champion of financial well-being, we address the urgent need for affordable lending solutions in an era where the average American grapples with over $155,000 in household debt.  Unlike traditional financial institutions, Clutch develops software to turn Credit Unions into FinTech lenders and leverage their balance sheets to responsibly lend to over 130M Americans. Our mission extends beyond mere financial transactions; we strive to fundamentally enhance the way credit unions interact with their members. By integrating cutting-edge technologies and user-centric designs, we help credit unions provide seamless digital experiences that are on par with leading tech companies. This approach not only preserves but revitalizes the longstanding tradition of community and member-focused service inherent to credit unions.Please note: This position is offered on a contractor basis. Applicants must have the necessary documentation and authorization to work in the country where the job is located. Clutch cannot provide sponsorship or assist with obtaining work permits for this role.
\n\n#Benefits\n 🏖 Paid time off\n\n