Engineering Leader - Machine Learning

About Basis

Basis equips accountants with a team of AI agents to take on real workflows.

We have hit product-market fit, have more demand than we can meet, and just raised $34m to scale at a speed that meets this moment.

Built in New York City. Read more about Basis here.

About The Team

We build the agentic ML systems that power Basis’s AI Accountant—so it can read documents, reason over context, and complete real accounting workflows safely and accurately.

We’re practitioners of the new AI paradigm: rather than just tuning a model, we optimize the system around it—tools, memory, retrieval, orchestration, and evaluation. We push model providers to their limits when necessary (custom runtimes, bigger containers, nonstandard packages) and run the experiments required to learn quickly.

We work from first principles with tight loops alongside Research, Product, Platform, and Accounting SMEs. We think in systems and care deeply about observability, clear abstractions, and code that’s easy to reason about in production.

About The Role

• 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.

What You’Ll Be Doing:

1. 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.

2. Drive ML systems strategy and execution

• 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.
• 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.

3. Elevate the craft

• 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.
• 📍 Location: NYC, Flatiron office. In-person team.

What We’D Love To See

• 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.
• Process-oriented: Skilled at breaking down complex problems into clear, repeatable steps and managing execution.
• Strong communicator: Clear in explaining concepts and comfortable collaborating across all levels of seniority.
• First-principles reasoner: Question assumptions and apply lessons creatively to new situations.
• 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.
• Process-oriented: Skilled at breaking down complex problems into clear, repeatable steps and managing execution.
• Strong communicator: Clear in explaining concepts and comfortable collaborating across all levels of seniority.
• First-principles reasoner: Question assumptions and apply lessons creatively to new situations.

What Success Looks Like In This Role

• Company-builder: Eager to lay groundwork both technically and culturally as we rapidly scale.
• Office lover: Prefers face-to-face interactions in our NYC office.
• All-in: Driven to seize a massive opportunity, accelerate growth, and commit deeply to Basis’s success.
• Company-builder: Eager to lay groundwork both technically and culturally as we rapidly scale.
• Office lover: Prefers face-to-face interactions in our NYC office.
• All-in: Driven to seize a massive opportunity, accelerate growth, and commit deeply to Basis’s success.

In accordance with New York State regulations, the salary range for this position is $100,000 –$300,000. This range represents our broad compensation philosophy and covers various responsibility and experience levels. Additionally, all employees are eligible to participate in our equity plan and benefits program. We are committed to meritocratic and competitive compensation.