Founding Machine Learning Engineer

Arcade.dev builds an MCP runtime and tools platform that enables AI agents to securely take actions within enterprise systems. The Founding Machine Learning Engineer will own the end-to-end ML pipeline, build and evaluate models for agent capabilities, productionize existing systems, and deploy efficient models in customer VPCs and air-gapped environments.

Responsibilities:

• 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