Engineering Leader - Applied ML Team

About Basis

We started Basis with the core belief that agents would become an integral part of knowledge work in the near future, and that accounting would be among the first and most important domains where this happened. Accounting is structured, high-stakes, and essential to every business on earth. It's also one of the most underbuilt areas in technology.

Here we are 3 years later, and Basis can now complete a partnership tax workbook end to end. Basis' abilities improve monthly and the rate of improvement itself is increasing. Every day accountants use Basis to create complex journal entries, debug difficult reconciliations, prepare technical accounting memos, and much more. We're deploying real agents to do real work in the real economy.

The world is going to look very different in 2030, and accounting will be at the center of that transformation in ways most people don't yet appreciate. Read more about how we're thinking about the work.

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.

What you'll be doing

Build and lead the Applied ML organization

• Hire and grow a world-class team of ML and AI 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.

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• 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.

7+ years of experience

as a software engineer working with and leading machine learning or applied ai teams

Salary:$200K - $325K

Equity: Highly Competitive

Visa sponsorship available; Can sponsor all types

On-site work policy, in-person with our team in NYC (Flatiron), 5 days/week