Tech Lead - 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 only 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, unusual 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 a Tech Lead on the ML Systems team, you’ll hold the technical vision for a critical area of Basis’s AI platform—such as agent orchestration, evaluations, or context management—and drive it from design to production.

You’ll architect systems, write code, and teach others how to do both with clarity and precision. You’ll review designs, simplify abstractions, and make sure the codebase stays coherent as we scale.

Your job is technical leadership: holding a high bar for design, execution, and reasoning, and helping others reach it.

You’ll operate as both architect and practitioner—writing, teaching, debugging, and designing systems that shape how AI agents reason and learn.

What You’Ll Be Doing:

1. Define and uphold the technical vision

• Own the architecture for a core ML capability (e.g., agent orchestration, eval systems, or context stack).
• Write and review critical code; establish standards for structure, interfaces, and testing.
• Drive design reviews that clarify trade-offs and ensure long-term coherence across teams.
• Create frameworks and abstractions others can build on confidently.

2. Build excellent systems and elevate others

• Partner with engineers across ML, Research, and Platform to implement robust, observable, and maintainable systems.
• Teach others how to think like architects: how to simplify complexity, make trade-offs explicit, and leave systems cleaner than they found them.
• Design processes that help teams reason rigorously—good specs, clear metrics, reproducible experiments.
• Make sure the work product (code, data, evaluations) reflects our values of clarity, precision, and craft.

3. Lead by example in technical execution

• Run high-velocity experiments across models, tools, and architectures—learn fast, share insights, and translate them into production decisions.
• Work closely with product and accounting domain experts to turn ambiguous problems into well-defined systems.
• Contribute across the stack: from prompt orchestration and retrieval to evaluation pipelines and observability tooling.
• Document decisions and teach through clarity—your design docs, code reviews, and explanations set the tone for the org.
• 📍 Location: NYC, Flatiron office. In-person team.

What We’D Love To See

• Experience with retrieval, embeddings, and structured context management.
• Familiarity with eval frameworks, vector stores, and experiment tracking.
• Comfort working with observability stacks (metrics/logs/traces).
• Exposure to multi-model routing, guardrails, and cost/latency optimization.
• Prior startup or high-velocity environment experience.
• Architect: You’ve built a subsystem that others depend on and understand intuitively.
• Mentor: Engineers around you level up in how they reason about systems.
• Unifier: The codebase feels consistent, legible, and coherent across boundaries.
• Experience with retrieval, embeddings, and structured context management.
• Familiarity with eval frameworks, vector stores, and experiment tracking.
• Comfort working with observability stacks (metrics/logs/traces).
• Exposure to multi-model routing, guardrails, and cost/latency optimization.
• Prior startup or high-velocity environment experience.
• Architect: You’ve built a subsystem that others depend on and understand intuitively.
• Mentor: Engineers around you level up in how they reason about systems.
• Unifier: The codebase feels consistent, legible, and coherent across boundaries.

What Success Looks Like In This Role

• Force multiplier: You ship your own work—but your real impact is how much better the team ships.
• Builder: You operate with conviction, curiosity, and calm under pressure.

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.