Member of Technical Staff - Applied ML

About the Role

This is a hands-on ML engineering role at a Series B fintech company building agentic AI for accounting professionals. You will own end-to-end projects, from scoping and experimentation through to production, designing the systems that help AI agents reason, plan, and continuously improve. The work is high-autonomy and high-impact, sitting at the intersection of applied research and product engineering.

What You'll Do

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

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

• Manage context and memory to maintain coherence across multi-step agent loops.

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

• Build scalable evaluation pipelines (offline and online) that run structured experiments automatically.

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

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

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

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

• Parse unstructured documents into structured representations that agents can reason over.

• Write concise specs and architecture docs; communicate progress clearly within your team pod.

What We're Looking For

• 2 or more years of substantive ML engineering experience at a fast-paced startup, top-tier tech company, AI-native company, or hedge fund; a junior-only or purely academic background is not a fit.

• Proven track record building end-to-end LLM-based AI agent applications, including model orchestration, benchmarking, and evaluation frameworks.

• Deep expertise in Python and LLM or transformer-based systems.

• Experience running structured ML experiments: framing hypotheses, building evaluation infrastructure, and iterating on measurable results.

• Strong grounding in machine learning fundamentals with a hands-on emphasis on agent systems and evals rather than classical ML alone.

• Technical degree (CS, Physics, Math, or equivalent) from a rigorous program.

• Very clear communicator who can break complex concepts down to their fundamentals.

• Currently based in or willing to relocate to New York; east coast location preferred.

• Background or genuine interest in accounting, finance, or economics is a plus.

Compensation & Benefits

Salary range: $175,000 to $350,000 USD annually. Visa sponsorship is available.

Location

On-site, five days per week in New York, NY, United States.