Member of Technical Staff - Applied ML

Job Description

Job Description

Member of Technical Staff - Applied ML

Company: Confidential — venture-backed technology company
Location: New York, NY (Flatiron office, in person 5 days per week)
Compensation: $175,000 - $350,000 + highly competitive equity
Employment Type: Full-time
Visa Sponsorship: Visa transfers; can sponsor all types

About the Company

The client 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, the client 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.

The client has raised a $140M Series B.

The Role

As an ML Engineer at the client, 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.

What You Will Do

• 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.

What You Bring

• 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

Nice to Have

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

Interview Process

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

Tech Stack

Python, Postgres, LLMs, agent frameworks, evaluation pipelines

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