Sr. Applied Machine Learning Engineer

Plug Technologies•Los Angeles, CA

18h•Onsite

About The Position

Plug is hiring a Senior Applied Machine Learning Engineer for its Intelligence team. Plug is a marketplace for used electric vehicles where consumers receive instant cash offers supported by battery-health data and dealers bid on inventory in real time. The role will help advance the models and systems that power EV pricing, volume forecasts, battery state-of-health analysis, and document data extraction. The engineer will work with the existing ML engineer and data team to build production-ready AI workflows and integrate them into Plug’s services and products. The position focuses on building and shipping agents and multi-agent workflows for operations, dealers, and sellers, including document extraction, inspection and title review, communications, and internal tools. The engineer will establish tracing and evaluation practices, use real cases to measure system performance, and make model and prompt decisions based on accuracy, cost, and latency. They will integrate agents through clean APIs, work within the existing architecture, develop TypeScript and React interfaces and backend endpoints when needed, and help other engineers adopt reusable patterns. This is a full-time, salaried, exempt, in-person role based in Sawtelle, Los Angeles. The role reports to the Head of Engineering. The annual salary range is $164,000 to $200,000, with offer placement depending on relevant experience, skills, training, qualifications, business needs, and market conditions. Plug does not provide relocation assistance or visa sponsorship for this role.

Requirements

At least 5 years of full-time industry software engineering experience, with a track record of shipping production features and supporting them after launch.
Hands-on experience shipping LLM features to production, including tool use, retrieval, and structured output.
Experience building evaluations and tracing for LLM systems, including datasets based on real cases, automated scoring, and use of production traces to identify and fix failures.
Strong TypeScript or Python skills and comfort working across APIs, databases, and frontend development when needed.
Experience building personal tooling around coding agents, such as skills, MCP servers, custom workflows, or evaluations; the role requires an agent-first approach.
Ability to collaborate in a small team through pairing, code review, sharing credit, and working within systems designed by others.
Ability to communicate disagreements, make a case, and then execute the agreed plan.
Habit of documenting work through short design notes, evaluation results, and runbooks.
High agency in an ambiguous, fast-moving startup environment, including identifying valuable workflows to automate and shipping without waiting for a perfect specification.
BS in computer science, mathematics, statistics, or a related quantitative field, or equivalent experience.

Responsibilities

Build and ship agents and multi-agent workflows for operations, dealers, and sellers, including document extraction, inspection and title review, communications, and internal tooling.
Trace agent workflows, including their steps, tool calls, and decisions; use traces to debug failures and create new evaluation cases.
Build evaluations using datasets from real cases, measure accuracy and failure modes, and use results to gate changes to prompts, models, and tools.
Compare model and prompt options based on accuracy, cost, and latency, and upgrade models when they are deprecated or alternatives perform adequately at lower cost.
Integrate agents with existing team-owned services through clean APIs and tools.
Work within the existing architecture and raise design concerns with the responsible engineer rather than bypassing the architecture.
Build TypeScript and React interfaces for reviewing and correcting agent output, along with supporting backend endpoints when needed.
Create reusable tools, examples, and short write-ups to help the engineering team build agents more efficiently.
Collaborate with the Intelligence team to advance pricing and volume forecasts, battery state-of-health models, and LLM pipelines for extracting structured data from vehicle, title, and inspection documents.