Founding Machine Learning Engineer

📋 Job Overview

Arcade is hiring the principal machine learning engineer that wants to build the future of agent capabilities. We're the MCP runtime that gives agents the power to do both seamlessly. We connect agents to the systems they act in, then give each one a permission slip and a paper trail - proof of what it's allowed to do, and a record of what it did. That's what makes AI safe to turn loose: real actions, on real systems, already shipping inside Fortune 100 companies.

🏢 About Arcade

Everyone's building AI agents, but almost nobody gets them to production. Building an impressive demo is easy. Building an AI agent that can securely take action inside enterprise systems is hard. The moment an agent accesses customer data, executes a workflow, or makes changes on behalf of a user, authorization, governance, and trust become the real engineering challenge. Arcade is the MCP runtime that gives agents the power to do both seamlessly.

🎯 The Role

Arcade is hiring the principal machine learning engineer that wants to build the future of agent capabilities. These core problems define the role: What’s old is new: How can a classic two-tower e-commerce recommendation system be used to make agents smarter? How can a classifier be used to make agents more efficient? The ability to take what’s been done and apply it to a new and ever-changing field is paramount for this role. We’ve launched Tool Recommendations as our first foray into agent–led recommendations, and there is a long list of features we want to create on these (& similar) building blocks.

✅ Key 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

📌 Required Qualifications

• Own the model pipeline from data ingestion to model publishing lifecycle - including models across embedding, reranking, classification, recognition, and other tasks.
• Be the leader of a group making agent capabilities, not just API calls, but intelligent and efficient abilities that outpace the competition.
• Provide insight and experience bringing ideas and practices that should be implemented in a stable but young model pipeline.
• Be willing to stand by your models because you’ve been provided able data to cover every possible outcome you can.
• Everything you build has to run where our customers run: their cloud, their hardware, and sometimes an air-gapped network. Quantization, export, serving, and packaging are part of the model design from day one, not a deployment step at the end.

⭐ Desirable Experience

• Our largest customers run Arcade inside their own walls. A Fortune 100 bank doesn't send its agents' tool calls to someone else's API, and its agents can't wait on a frontier model to pick the right tool out of thousands. So the models behind Arcade's agentic features have to be small, fast, accurate, and shippable into a customer's VPC.
• This is just one example of why custom, small models are the unlock to many of Arcade’s future products.
• You'll report directly to the Head of Engineering and own the models that sit in the runtime path of every agent call we serve.
• Our first model for agent recommendation is already built (& patent pending), along with its training pipeline, but it needs to be productionized. You'll own it, decide what the ML stack at Arcade looks like, and set the patterns going forward.
• You’ll own the build-buy decisions for our stack going forward and have a healthy budget to spend.

🎁 Benefits

Competitive

🛂 Visa & Eligibility