Basis is looking to solve the problem of equipping accountants with a team of AI agents to take on real workflows, and is seeking an ML Engineer to own end-to-end projects that bring intelligence into production.
Requirements
- Experience with multi-agent architectures
- Knowledge of autonomy boundaries, tool usage, and fallback behaviors
- Understanding of context and memory management
- Familiarity with model optimization under real-world constraints
- Experience with scalable evaluation pipelines
- Knowledge of golden tasks, labeling strategies, and metrics
- Experience with instrumentation for regression detection and continuous improvement
Responsibilities
- Design and iterate multi-agent architectures that automate real accounting workflows.
- Build in 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).
- Build scalable evaluation pipelines (offline and 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 patterns, and drive continuous improvement.
Other
- Scope your projects clearly. Write concise specs and architecture docs that eliminate ambiguity.
- Communicate progress clearly: what's built, what's learned, what's next.
- Work closely with your pod, teaching, unblocking, and sharing learnings as you go.
- Move fast, stay curious, and build with conviction and care.
- In-person team in NYC, Flatiron office.
