Founding Machine Learning Engineer

You will own the end-to-end machine learning pipeline, from data ingestion and training through evaluation, release, and production monitoring. You will build and fine-tune models for tool selection, routing, retrieval, and agent memory; create evaluation systems; and deploy optimized models to customer VPCs and air-gapped environments. You will turn telemetry into training data, choose the ML stack, make build-versus-buy decisions, and shape the ML strategy and future hiring.

Responsibilities

• Own the end-to-end ML training pipeline from data through evaluation and release
• Train and fine-tune models for tool selection, routing, retrieval, and agent memory
• Expand model use cases for tool and agent-context search and recommendation
• Build offline and online evaluation systems and compare models with Claude, GPT, and Gemini
• Quantize, optimize, package, and serve models for customer VPCs and air-gapped environments
• Turn production agent traces and tool-call telemetry into training data within enterprise data boundaries
• Choose the ML stack, write the strategy, and inform ML hiring
• Use expertise in agent systems to decide where models improve agents
• Use AI tools to increase delivery speed

Requirements

• 7+ years of software engineering experience
• 4+ years training and shipping production ML systems
• Experience training or fine-tuning models that reached production and improved customer-relevant metrics
• Expertise in production agent systems, including harnesses, memory, skills, tool use, and sub-agents
• Knowledge of fine-tuning and deriving training data from raw telemetry
• Statistics fluency for evaluating whether metric changes are meaningful
• Experience deploying models under latency, GPU, or infrastructure constraints
• Python for training and ML work
• TypeScript or Go for production model-serving services
• Ability to make and defend technical stack decisions

Benefits

• Equity
• Benefits