Engineering Leader - Machine Learning

Basis AI • New York, NY

84d • $100,000 - $300,000

About The Position

As an Engineering Leader on the ML Systems team, you’ll be responsible for achieving company-level outcomes through the people and systems you build. Your job is to make the team successful—to deliver ambitious technical goals while fostering an environment where exceptional engineers can do their best work. You’ll operate across research and production, experiment and impact—bridging strategy and execution. You’ll make hard trade-offs explicit, design frameworks for fast iteration, and ensure that the entire team learns faster than the problems evolve. This is a hands-on leadership role: you’ll drive technical direction, architect systems, and review critical code—but your real leverage will come from clarity, conviction, and how effectively you grow others.

Requirements

• Think in systems—models, people, organizations—and can operate across all three.
• Care about clarity and iteration more than flash; you ship, learn, and refine relentlessly.
• Have conviction in your decisions but stay open to being wrong.
• Are driven by both technical excellence and the growth of those around you.
• See ambiguity as an invitation to lead.

Responsibilities

• Build and lead the applied-ML organization; hire and grow a world-class team of ML and systems engineers; set crisp goals and coach continuous development.
• Foster a culture of rigor, iteration, and shared learning—where people move fast and stay grounded in reality.
• Establish clear processes for experimentation, evaluation, and delivery; make success criteria objective and comparable.
• Be a source of clarity and calm when things are ambiguous or hard.
• Define and evolve our multi-agent architecture: autonomy boundaries, orchestration logic, context management, and safety layers.
• Own evaluation infrastructure—offline, online, and hybrid—that lets us ship models with confidence and traceability.
• Integrate retrieval, memory, and context management into production-grade agent loops; ensure stability under real workloads.
• Align closely with Research, Product, and Platform to translate insights into production systems with measurable impact.
• Insist on clean abstractions, legible systems, and deep observability; make complexity visible and manageable.
• Set and uphold high standards for experimentation, documentation, and decision quality.
• Continuously improve team processes—reviews, onboarding, retros, performance cycles—to compound speed and quality.
• Coach engineers not just to build better models, but to think better about systems.

Benefits

• Eligible to participate in our equity plan and benefits program.
• Meritocratic and competitive compensation.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Number of Employees: 1-10 employees