Machine Learning Engineer

About the company

We find the manual work slowing teams down, rebuild it as automations inside tools clients already own, and run them. Our platform has three parts: a client workspace where operations are mapped and priced; a delivery system that runs projects and the builder network; and an intelligence layer across both. We have delivered 311 projects for more than 100 companies.

The role

You make the AI inside our intelligence layer and AI interviewer measurably reliable. The interviewer talks with client teams by chat, voice and screen share, then turns what it captures into process maps and draft SOPs. You own the quality of that pipeline: extraction, retrieval, evaluation, and the signals that decide when a workflow can run with less human review. This is applied work on foundation models from Anthropic, OpenAI and Google, not model research.

What you will do

• Own the pipeline from interview transcript, screen recording and uploaded file to structured process map and draft SOP.
• Build the evaluation harness: golden sets, regression tests, and scoring for extraction accuracy, SOP quality and assistant answers.
• Design retrieval over each client's approved workflows so the assistant answers consistently in Claude, ChatGPT, Gemini and Slack.
• Build the scoring behind the autonomy ladder: the run-quality signals that move a workflow from full review to spot-checks to auto-publish.
• Detect when a run deviates from its SOP, classify the deviation, and route retries and human review.
• Improve the time and money estimate attached to each workflow, and keep estimates labelled as estimates.
• Choose models per task on cost, latency and quality, and keep the system portable across providers.
• Hold tenant isolation and privacy in every pipeline. Client interviews and SOPs are private by default.
• Ship end to end with the founding engineers and own what you ship in production.

What you bring

• 5+ years building ML or LLM systems that run in production with real users.
• Strong Python and sound engineering habits: testing, versioning, observability.
• Hands-on LLM experience: context design, structured output, tool use, retrieval, agents.
• A record of building the evaluation before trusting the model change.
• Experience with unstructured input: transcripts, audio, documents, screenshots.
• Working statistics. You can design a measurement, size a sample, and say what a result does not show.
• Comfort with ambiguity and with direct contact with customers and founders.

Nice to have

• Speech and multimodal pipelines for voice interviews and screen capture.
• Process mining, workflow modelling or operations research.
• Assistant integrations for Claude, ChatGPT, Gemini or Slack.
• Classical ML for anomaly detection, classification or forecasting.
• Time in a services or forward-deployed team.