Senior ML Engineer

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Clutch

WebsiteLinkedInAll Job Openings

51 - 200 employees

☁️ SaaS

🏢 Enterprise

Consulting • SaaS • Enterprise

Clutch is a results-driven consulting firm that redefines traditional consulting methods through a human-centered approach. By focusing on data-driven solutions, Clutch addresses gaps in business processes for companies of all sizes, including government entities, small businesses, and financial institutions. It specializes in marketing, communications, and event management, and utilizes proprietary tools and resources to drive transformational outcomes. Notably, Clutch applies a Human Transformation Approach to re-integrate human elements often overlooked by technology-led solutions. It offers AI-driven insights to predict and quantify human emotions, enhancing understanding and support for entrepreneurs and the startup marketplace.

📋 Description

• 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. • Ship your first agent tool contract end-to-end: schema design, handler implementation, structured-error contract, unit tests, deployed via HAL's runtime. • Set up the eval foundation for our agents: golden transcripts, rubric-based judges, regression suites that run on every prompt or model change. • Build a working relationship with HAL and become the data team's go-to on agent infrastructure decisions. • Be the primary owner (with data engineer support) of the ML API and the agent tool layer that wraps NBA and our ML models. • Have shipped at least one production-grade agent (customer-facing or partner-facing) with prompt versioning, evals, observability, and multi-tenant gating in place. • Define the data team's playbook for shipping a new ML model as an LLM-callable tool, end-to-end. • Mentor the data engineers on ML/AI patterns so they can confidently support and extend the systems you own. • 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. • Have measurably improved agent cost and latency (target: 30%+ reduction on P95 latency or per-conversation cost on at least one agent). • Be shaping the data team's roadmap for the next generation of ML and AI products, in partnership with the PM and data scientist. • Help us decide what to hire next as the team scales.

🎯 Requirements

• 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. • 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. • 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. • 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. • 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. • 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. • Experience building and maintaining low-latency production APIs (FastAPI, BentoML, or equivalent), with opinions on serving, batching, and caching. • Comfortable in AWS (Lambda especially), Docker, and GitHub-based workflows. • 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.

🏖️ Benefits

• Remote Flexibility: Enjoy the freedom of remote work from anywhere, balancing life and career seamlessly. • Unforgettable Off-Sites: Twice a year, bond with colleagues in exciting destinations, fostering teamwork and fresh ideas. • Paid Time Off and National Holidays: Enjoy 20 PTO days yearly and the National Holidays for relaxation and rejuvenation. • Stock Options: Joining us means having a stake in our success, so you'll receive stock options as part of your compensation package. • Home Office Setup: Create your ideal workspace with a dedicated budget for home office essentials. • Work Trip Budget: Grow personally and professionally with a budget for work-related trips and co-working.