Member of Technical Staff Applied ML

Company: Basis
Location: New York NY (Flatiron office in person 5 days per week)
Compensation: $175000 - $350000 highly competitive equity
Employment Type: Full-time
Visa Sponsorship: Visa transfers; can sponsor all types

Basis started from the belief that AI agents would become integral to knowledge work and that accounting (structured high-stakes and essential to every business) would be among the first domains transformed. Three years in Basis can complete a partnership tax workbook end to end and accountants use it daily to create complex journal entries and debug reconciliations with capabilities improving every month.

Basis has raised a $140M Series B.

As an ML Engineer at Basis you will own end-to-end projects that bring intelligence into production: the systems that help its agents reason plan and evaluate themselves. You will have full autonomy to plan projects define success run experiments and decide when a system is ready to ship. This is an applied role for engineers who want to operate as researchers and builders at once.

• Design and iterate multi-agent architectures that automate real accounting workflows with clear autonomy boundaries tool usage and fallback behavior.
• Manage context and memory across agent steps; route evaluate and optimize models under latency cost and accuracy constraints.
• Build scalable offline and online evaluation pipelines that run hundreds of experiments automatically with golden tasks labeling strategies and metrics.
• Instrument the stack to catch regressions track error taxonomies and drive closed-loop improvement.
• Architect prompt stacks retrieval and indexing pipelines and document parsing into structured representations agents can reason about.
• Scope projects with concise specs build and test end to end and communicate progress clearly within your pod.

• 4-12 years as a machine learning engineer
• Experience at a fast-paced startup (Series A-D) a tier-1 tech company or a hedge fund
• End-to-end LLM agent applications: benchmarking model orchestration and evals for agent behavior and reliability
• Structured ML experimentation: framing hypotheses building evaluation infrastructure iterating on measurable results
• CS physics math or other technical degree from a top school
• Very clear communication
• Located in the US or Canada and able to work in the New York office 5 days a week

• ML products and underlying models at a fast-paced company
• Deep Python and LLM/transformer expertise
• Interest in AIs impact on accounting and finance

Initial screen with leadership meet-the-team screen technical coding interview onsite plus references.

Python Postgres LLMs agent frameworks evaluation pipelines

REVENUE: 21% of first-year salary. Est. fee per hire $37K-$74K; 10 seat(s) up to $551K if all filled.

TARGET COMPANIES (client (DeepMind/Ramp/Databricks named) suggested): Ramp Databricks Google DeepMind Harvey Hebbia Citadel Jane Street.

BEST-FIT CANDIDATE: 4-12 yrs; end-to-end LLM agent systems evals; 0-to-1 ownership at startup/tier-1 tech/hedge fund; quantified production impact; visa: transfers; can sponsor; location: NYC 5 days. Applied not research: generally avoid Masters/PhD-heavy academic profiles and big-company-only backgrounds.