Machine Learning Engineer

ESB Technologies builds automation systems that transform manual work into workflows within tools clients already use. The Machine Learning Engineer will develop and operate reliable AI pipelines that convert interviews and uploaded materials into process maps and SOPs, while building evaluation, retrieval, autonomy, and production-quality systems.

Responsibilities:

• 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

Requirements:

• 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
• 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