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Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

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About the job

Plug [https://cdn.prod.website-files.com/69fe0c3f4c779ad4c5453b7d/6a049dcd5f255ce73c6b48ec_LogoBlk.svg]

Job Title: Sr. Applied Machine Learning Engineer Team: Engineering Location: Onsite - Sawtelle (Between Santa Monica & West LA) Employment Type: Full-time, Salary, Exempt Reports To: Head of Engineering

About Plug Plug is the marketplace for used electric vehicles. Consumers get an instant cash offer backed by battery-health data, and dealers across the US bid in real time on that inventory. Former Tesla executives founded Plug in 2023. We’ve facilitated over $150M in EV sales and raised a $20M Series A led by Lightspeed Venture Partners.

Why Plug

- You’ll own a piece of the biggest infrastructure opportunity in automotive.

- You’ll work on a platform built exclusively for EVs, with nothing adapted from legacy automotive.

- You’ll be part of an AI-native company. We’re rethinking how work gets done at a fundamental level.

- You’ll work directly with operators who have scaled multi-billion dollar businesses in automotive, EV, and marketplaces.

- You’ll have real ownership, high autonomy, and a direct line to company outcomes.

The Opportunity Every car on Plug starts with a number: what is this EV worth today, and what will a dealer pay for it tomorrow? Those numbers come from our models. They draw on auction results, market data, vehicle telematics, and battery health. As volume grows, our models have to get more accurate, update faster, and be easier to trust and explain.

You’ll join our Intelligence team and work alongside our ML engineer and data team, who built the models behind pricing and battery health. Together, you’ll take those models further: sharper price and volume forecasts, battery state-of-health models built from telematics, and LLM pipelines that pull structured data from vehicle, title, and inspection documents.

What You’ll Do

- Build and ship agents and multi-agent workflows for operations, dealers, and sellers: document extraction, inspection and title review, dealer and seller communication, and internal tooling.

- Trace every agent so you can see each step, tool call, and decision. Use those traces to debug failures and turn them into new eval cases.

- Write evals before and alongside each agent: build datasets from real cases, measure accuracy and failure modes, and gate changes to prompts, models, and tools on the results.

- Make model and prompt decisions with data: compare options on accuracy, cost, and latency, and run upgrades when a model is deprecated or a cheaper one holds up.

- Integrate agents with the services the team already owns, through clean APIs and tools. Work within the existing architecture and raise design concerns with the owning engineer instead of routing around them.

- Ship full-stack when the job calls for it: the TypeScript and React screens where people review and correct agent output, and the backend endpoints behind them.

- Help the rest of engineering adopt the same patterns. Build reusable tools, examples, and short write-ups that make the next agent faster to build.

What You’ll Bring

- 5+ years of full-time industry software engineering, with a track record of shipping features to production and supporting them after launch.

- Hands-on experience shipping LLM features to production: tool use, retrieval, and structured output.

- Experience building evals and tracing for LLM systems: datasets drawn from real cases, automated scoring, and production traces you used to find and fix failures. We care less about which agent framework you used (e.g., Claude Agent SDK, LangChain Deep Agents) than how you knew it worked.

- Strong TypeScript or Python, and comfort working across the stack: APIs, databases, and frontend when needed.

- You work agent-first. You’ve built your own tooling around coding agents, such as skills, MCP servers, custom workflows, or evals.

- You enjoy being part of a small team: pairing, reviewing each other’s work, sharing credit, and building inside systems other people designed. You can disagree, make your case, and then ship the agreed plan.

- A habit of writing things down: short design notes, eval results, and runbooks that others can pick up.

- High agency in an ambiguous, fast-moving startup. You find the most valuable workflow to automate and ship against it without waiting for a perfect spec.

- BS in computer science, math, statistics, or a related quantitative field, or equivalent experience.

Not sure you should apply? Do it. Research consistently shows that people from under-represented groups are more likely to talk themselves out of applying if they don’t meet 100% of the listed requirements. You don’t need to check every box. If this role excites you and you bring most of what’s listed under “What You’ll Bring,” we want to hear from you.

Compensation and Benefits Annual salary: $164,000 to $200,000

Salary ranges come from independent, third-party benchmarking against national compensation data. Where an offer falls within the range depends on relevant experience, specialized skills, training and qualifications, current business needs, and market conditions. We review ranges periodically and may adjust them as the market changes.

Benefits & Perks

- Health, Vision, and Dental insurance

- 401(k) plan

- Equity

- Unlimited PTO

- Daily lunch and dinner stipend, plus a fully stocked kitchen

- Dog-friendly office and on-site EV charging

- 30-day remote work from anywhere policy

- Ongoing opportunities for growth and development

This full-time position is in-person, based in Sawtelle (between Santa Monica and West Los Angeles). We will not provide relocation assistance to successful candidates. We do not offer sponsorship at this time.

Acknowledgments Plug is an equal opportunity employer. We don’t discriminate on the basis of race, color, religion, creed, national origin, ancestry, sex, gender, gender identity or expression, sexual orientation, pregnancy, age, marital status, military or veteran status, physical or mental disability, medical condition, genetic information, or any other characteristic protected by law. Plug will consider qualified applicants with arrest and conviction records in accordance with the Los Angeles Fair Chance Initiative for Hiring and the California Fair Chance Act.