Member of Technical Staff - Applied ML

Job description

### About the Role This is an applied ML engineering role at a Series B fintech company building agentic AI for accounting professionals. You'll own end-to-end projects — from architecture through production — designing the systems that help intelligent agents reason, plan, and evaluate themselves at scale. The work sits at the intersection of research-minded thinking and pragmatic engineering, and your output will directly shape how accounting workflows become smarter and more autonomous. ### What You'll Do - Design and iterate multi-agent architectures that automate real-world accounting workflows end-to-end. - Encode autonomy boundaries, tool usage, and fallback behaviors to keep agents safe, reliable, and auditable. - Manage context and memory across multi-step agent loops with clearly defined success criteria. - Route, evaluate, and optimize model usage under production constraints including latency, cost, and accuracy. - Build scalable evaluation pipelines — offline and online — capable of running hundreds of experiments automatically. - Define golden tasks, labeling strategies, and metrics that make model and product performance measurable and comparable. - Instrument the stack to detect regressions, track error taxonomies, and drive closed-loop improvement. - Architect prompt stacks and retrieval pipelines; parse messy documents into structured representations for reasoning. - Design guardrails and validation layers to keep agent behavior safe and deterministic. ### What We're Looking For - 3+ years of AI/ML engineering experience building production systems or AI applications. - Deep expertise in Python and LLM/transformer-based systems. - Hands-on experience building end-to-end LLM-based agent applications including model orchestration, benchmarking, and evaluation frameworks. - Experience designing and running structured ML experiments — hypothesis framing, evaluation infrastructure, and iteration on measurable results. - Experience building retrieval and indexing pipelines; familiarity with parsing unstructured documents into structured representations. - Background at a fast-paced startup, top-tier tech or AI-native company, or quantitative finance environment. - Strong CS fundamentals; a degree in CS, Math, Physics, or a related technical discipline. - Clear, concise communicator who can break complex concepts down to first principles. - Interest in AI applications within accounting, finance, or economic systems is a plus. ### Compensation & Benefits Salary range: **$175,000 – $300,000 USD** annually. Visa sponsorship is available. ### Location On-site, five days a week in **Los Angeles, CA**. Candidates currently located in the US or Canada preferred.