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 the model—tools, memory, retrieval, orchestration, and evaluation.

We push model providers to their limits when needed (custom runtimes, unusual packages, unconventional loops) and run the experiments required to learn fast.

We work in tight, high-context pods alongside Platform, Product, and Accounting experts. We think in systems, debate trade-offs, and write code that’s observable, legible, and built for production learning.

About the Role:

As an ML Engineer at Basis, you’ll own end-to-end projects that bring intelligence into production. You’ll act as the Responsible Party (RP) for systems that help our agents reason, plan, and evaluate themselves — meaning you’ll scope, build, and deliver from first principles.

You’ll have full autonomy: plan your projects, define success, run experiments, and decide when your system is ready to ship.

You’ll move fast, instrument deeply, and design for clarity — building the scaffolding that lets models act safely and improve continuously.

This is a role for engineers who want to operate like researchers and builders at once: reasoning, experimenting, and shipping systems that get smarter over time.

What you’ll be doing:

1. Build and evolve our agent systems

• Design and iterate multi-agent architectures that automate real accounting workflows.

• Encode autonomy boundaries, tool usage, and fallback behaviors that make agents safe and reliable.

• Manage context and memory for coherence across steps; plan and execute agent loops with measurable success criteria.

• Route, evaluate, and optimize models under real-world constraints (latency, cost, accuracy).

2. Design evaluation and experimentation frameworks

• Build scalable evaluation pipelines (offline + online) that run hundreds of experiments automatically.

• Define golden tasks, labeling strategies, and metrics that make performance measurable and comparable.

• Instrument the stack to detect regressions, track error taxonomies, and drive closed-loop improvement.

• Use data and experiments to drive product and architectural decisions—not just intuition.

3. Engineer for context and retrieval

• Architect prompt stacks and instruction hierarchies that structure model reasoning.

• Build retrieval and indexing pipelines that surface relevant context efficiently.

• Parse messy documents into structured representations that agents can reason about.

• Design guardrails and validation layers to keep behavior safe and deterministic.

4. Operate as an RP — plan, build, deliver

• Scope your projects with clarity; write concise specs and architecture docs that eliminate ambiguity.

• Build, test, and instrument your systems end-to-end.

• Communicate progress clearly: what’s built, what’s learned, what’s next.

• Collaborate tightly within your pod — teaching, unblocking, and sharing learnings as you go.

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

What Success looks like in this role

• Owner: You scope, execute, and deliver your systems from concept to production.

• Engineer–Scientist: You instrument everything, measure outcomes, and learn from data.

• Simplifier: You design clean abstractions for complex ML systems that others can build on.

• Force multiplier: Your work raises the team’s speed and quality through clear interfaces and insight.

• Builder: You move fast, stay curious, and design with conviction and care.

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.