Engineering Leader - Applied ML

Basis AI•New York, NY

2d•Onsite

About The Position

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 ML systems that power Basis's AI Accountant. Our systems read documents, reason over context, and complete real accounting workflows safely and accurately. We focus on the whole system, not just the model. We optimize everything around it: tools, memory, retrieval, orchestration, and evaluation. We push model providers to their limits when needed (custom runtimes, unusual packages, unconventional loops) and run experiments to learn quickly. We work in small, focused pods alongside Platform, Product, and Accounting experts. We think in systems, debate trade-offs, and write code that's observable, understandable, and built for continuous learning in production. About the Role As an Engineering Leader on the Applied ML team, your job is to help the team succeed. You'll work toward ambitious technical goals while building both the people and systems that make success sustainable. You'll shape our AI agents, develop great engineers, and make sure what we build today still makes sense a year from now. This is a hands-on leadership role. You'll design systems, review architecture and set technical standards. You'll also coach, develop, and unblock others so the team can do great work independently.

Nice To Haves

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.
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.

Responsibilities

Build and lead the Applied ML organization
Hire and grow a world-class team of ML engineers.
Set clear goals and coach continuous development.
Build a culture of rigor, iteration, and shared learning where people move fast while staying grounded in reality.
Establish clear processes for experimentation, evaluation, and delivery.
Make success criteria objective and comparable.
Be a source of clarity and stability when things are ambiguous or difficult.
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.
Work closely with Research, Product, and Platform to turn insights into production systems with measurable impact.
Elevate the craft
Push for clean abstractions, understandable systems, and deep observability.
Make complexity visible and manageable.
Set and maintain high standards for experimentation, documentation, and decision quality.
Continuously improve team processes (reviews, onboarding, retros, performance cycles) to increase speed and quality.
Coach engineers not just to build better models, but to think better about systems.