> Markdown version of [/jobs/ext/4150974-founding-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/4150974-founding-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Founding Machine Learning Engineer - **Company:** The Arcade - **Location:** San Francisco, CA, United States - **Experience:** Experienced - **Salary:** $230,000.0 - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Python (Programming Language), Machine Learning, Raw Data, Software Engineering, TypeScript, Privacy Controls, Graphics Processing Unit (GPU), Chatbots, ONNX (Open Neural Network Exchange) Format, Free and Open-Source Software, Machine Learning Operations, TensorRT, Claude, Google Gemini, Model Context Protocol, VLLM, llama.cpp, GPT, Data Pipelines - **Published:** October 4, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=29b61d8537f7f947 ## About the Role * 7+ years of software engineering experience, with 4+ years training and shipping production ML systems. Formal title matters less than the work. * You've trained or fine-tuned models that went to production and moved a metric customers cared about, not just a leaderboard number. * Expertise in production agent systems: harnesses, memory, skills, tool use, and sub-agents. You know the nuances well enough to help decide what we build, and have intuition about what will actually work before we build it. * Know how and when fine-tuning works and how to apply it. What data is needed, and how to derive it from what raw data telemetry gives you. * Evals you'd defend in a review. You have the statistics fluency to say whether a small delta is real or noise. * You've deployed models under real constraints like tight latency budgets, limited GPUs, or someone else's infrastructure (vLLM, ONNX, TensorRT, llama.cpp, or equivalent). * Strong Python for training and ML work, plus TypeScript or Go for the production services that serve your models. * You pick the stack, write the doc, and can still defend the decision a year later. * A do-er, not a researcher-in-residence. You'd rather ship a working v0.5 next week than a polished v2.0 next quarter. * Comfort with ambiguity: early team, a charter that will expand, decisions made with incomplete data. * An insatiable desire to ship. ## Description * Own the training pipeline: Run Arcade's ML pipeline end to end, from data and training through evaluation and release. Make it reliable enough that shipping a new model is routine, not an event. * Build the models: Fine-tune and train models for tool selection, routing, retrieval, and agent memory. Use whatever gets the job done distillation, embeddings, rerankers and more. * Expand the use cases: Take our model into new territory, starting with search and recommendation over tools and agent context. * Measure what matters: Build the eval system. That means offline evals against real agent traces, online measurement in production, and head-to-head comparisons with Claude, GPT, and Gemini, so every model decision has data behind it. * Ship on-prem: Quantize, optimize, and package models for customer VPCs and air-gapped environments, and work with the Runtime team on how they're served. * Turn telemetry into training data: Build the loop from production agent traces and tool-call data to better models, within the data boundaries our enterprise customers require. * Set the direction: Choose the ML stack, write the strategy, and help decide who we hire next into ML. * Decide what to build: Use your depth in modern agent systems (harnesses, memory, skills) to pick where models make agents meaningfully better, and to call early whether an approach is going to work. * Use AI to compound your own output: Projects that take a week today should take a day next time., * You've shipped ML into enterprise on-prem or regulated environments (financial services, healthcare, government). * Tool-use benchmark or eval work: BFCL, τ-bench, ToolBench, MCP evals, or equivalent. * Familiarity with the MCP (Model Context Protocol) ecosystem. Extra bonus if you've filed an issue against the spec. * You've built training-data pipelines from production traces with privacy controls in place. * You've been the first ML hire somewhere before, and you'd do some things differently this time. * Open-source contributions or published work that survived contact with other engineers. * Experience at an early-stage startup, and you loved it. Join The Movement We're not just building a product - we're leading a movement to transform AI from just chatbots to agents that can take actions against real systems. This is your chance to be at the forefront of that revolution.