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 TeamWe 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 RoleAs a Tech Lead on the Applied ML team, you'll own the technical vision for a critical part of our AI platform (like agent orchestration, evaluations, or context management) and see it through from design to production.
You'll architect systems and write code while helping others do the same. You'll review designs, simplify complex ideas, and keep our codebase clean as we grow. Your role is technical leadership: maintaining high standards and helping your team meet them.
You'll be both architect and hands-on engineer. You'll write code, teach others, debug problems, and design the systems that determine how our AI agents think and learn.
What you’ll be doing:Define and uphold the technical vision
Own the architecture for a core ML capability (agent orchestration, eval systems, context management, etc.).
Write and review critical code. Set standards for how we structure code and test it.
Lead design reviews that make trade-offs clear and keep our systems coherent.
Create frameworks and patterns that others can build on with confidence.
Build excellent systems and elevate others
Work with engineers across ML, Research, and Platform to build robust, observable systems.
Teach others to think architecturally: simplify complexity, make trade-offs explicit, improve everything you touch.
Create processes that help teams work rigorously with clear specs, metrics, and reproducible experiments.
Ensure all our work (code, data, evaluations) meets high standards of clarity and quality.
Lead by example in technical execution
Run experiments quickly across models, tools, and architectures. Share what you learn and turn insights into production decisions.
Work with product and accounting experts to turn vague problems into clear technical solutions.
Contribute everywhere: prompt orchestration, retrieval, evaluation pipelines, observability tools.
Document your decisions clearly. Your design docs, code reviews, and explanations set the standard for everyone.
📍 Location: NYC, Flatiron office. In-person team.
What We’d Love to SeeExperience with retrieval, embeddings, and structured context management.
Familiarity with eval frameworks, vector stores, and experiment tracking.
Comfort working with observability stacks (metrics/logs/traces).
Experience with multi-model routing, guardrails, and cost/latency optimization.
Background in startups or fast-moving environments.
You've built subsystems that others depend on and find intuitive to use.
Engineers around you get better at thinking about systems.
The codebase stays consistent and understandable across different areas.
You ship your own work, but your bigger impact is making the whole team more effective.
You work with conviction and curiosity while staying calm under pressure.
Top Skills
What We Do
AI Platform for Accounting firms giving accountants a team of AI assistants
