Member of Technical Staff – Applied MLLocation - New York, NY — Flatiron On-site role requiring 5 days/week in the NYC office.
Compensation: $175,000 – $350,000 Base + Highly Competitive Equity
Visa: Open to Visa Transfers (OPT, H1B Transfers) + New Visa Sponsorship
Company Stage: Series B – Growth-Stage AI / FinTech / Enterprise Software Company
Industry:
Artificial Intelligence, Machine Learning, FinTech, Accounting, Enterprise Software, AI Agents, Applied ML
About the Company:
Our client is building an AI-native platform transforming accounting and finance through intelligent software agents. The platform uses advanced AI systems to automate complex accounting workflows, combining large language models, agentic architectures, evaluation systems, and data infrastructure to solve traditionally manual financial processes. The company has raised over $100M in its Series B and is growing rapidly with significant runway and a highly accomplished technical team. As a Member of Technical Staff – Applied ML, you'll work at the intersection of machine learning research, AI engineering, experimentation, and production software. You'll own projects end-to-end — from defining the problem and designing experiments through building evaluation infrastructure, developing agent systems, and deploying reliable AI capabilities into production. This is an opportunity for an applied ML engineer who wants to operate like both a researcher and a builder while working on the frontier of AI agents and intelligent enterprise software.
What You'll Do:- Design, build, and iterate multi-agent architectures for complex accounting and finance workflows
- Build end-to-end LLM-based AI agent applications
- Develop systems that enable AI agents to reason, plan, use tools, and execute multi-step workflows
- Define autonomy boundaries, tool usage, fallback behaviors, and reliability mechanisms for AI agents
- Manage model context and memory across multi-step agent interactions
- Design agent loops with measurable success criteria
- Route, evaluate, and optimize models based on latency, cost, accuracy, and reliability
- Build scalable evaluation pipelines for AI agents and LLM-based systems
- Develop offline and online evaluation frameworks
- Define golden datasets, benchmark tasks, labeling strategies, and performance metrics
- Instrument AI systems to identify regressions and track error taxonomies
- Run structured ML experiments and use measurable results to drive product and architecture decisions
- Build experimentation infrastructure capable of running hundreds of experiments automatically
- Design prompt stacks and instruction hierarchies for reliable model behavior
- Build retrieval and indexing pipelines to surface relevant context efficiently
- Develop systems for parsing messy documents into structured representations
- Build guardrails and validation layers that make AI behavior safer and more deterministic
- Develop systems that allow models and agents to continuously improve through feedback loops
- Scope projects from first principles and define clear technical objectives
- Write concise technical specifications and architecture documents
- Build, test, deploy, and instrument ML systems end-to-end
- Own production systems from initial concept through deployment and iteration
- Communicate technical progress, experimental results, and learnings clearly
- Collaborate closely with engineering and product teams to ship high-impact AI capabilities
- Teach, unblock, and share technical learnings with teammates
- Make architecture and implementation decisions independently
- Operate with high autonomy in a fast-moving startup environment
- Balance experimentation and research-oriented thinking with production engineering requirements
- Build AI systems that are reliable enough for real-world accounting and financial workflows
Ideal Candidate:
Background Experience Requirements4–12 years of experience in machine learning engineering or applied MLExperience building and deploying production ML or AI systemsExperience working at fast-paced startups, leading technology companies, or technically demanding environments Experience building end-to-end LLM-based applicationsExperience working with AI agents, agentic systems, or LLM orchestration Experience running structured ML experimentsExperience defining hypotheses, evaluation methodologies, and measurable success criteria Experience building evaluation infrastructure for ML or AI systemsExperience working with production AI systems and reliability challenges Strong ownership mentality with demonstrated ability to take projects from concept to production Experience operating independently in ambiguous environmentsStrong technical communication skillsExperience collaborating closely with engineering and product teams Strong problem-solving ability and analytical thinking Experience working in environments where rapid iteration and experimentation are expected Technical Requirements Strong Python engineering experience Strong understanding of machine learning fundamentals Experience with LLMs and transformer-based systemsExperience building LLM-based AI agent applicationsExperience with model orchestration and routing Experience designing and implementing AI agent architectures Experience building evaluation and benchmarking systemsExperience designing structured ML experimentsExperience with offline and online evaluation methodologies Experience defining metrics for model and agent performance Experience building retrieval and indexing pipelines Experience with prompt engineering and instruction hierarchies Experience managing model context and memory Experience building guardrails and validation systemsExperience working with large datasets Strong data analysis and feature engineering skillsExperience with production ML infrastructure Experience with PostgreSQL or comparable relational databases Strong software engineering fundamentals Ability to build reliable production systems Ability to instrument systems and monitor performance Ability to reason about latency, cost, accuracy, and reliability tradeoffs Strong debugging and troubleshooting ability Ability to rapidly prototype and productionize AI systems Strong understanding of distributed systems and scalable AI infrastructure preferred EducationBachelor's degree in Computer Science, Mathematics, Physics, Engineering, or related technical field preferred Strong technical education from a rigorous university environment preferred Equivalent practical engineering or ML experience accepted Soft Skills Exceptional ownership and execution ability Extremely strong analytical and problem-solving skills Clear and concise technical communication Ability to explain complex ML and AI concepts clearly Highly autonomous working style Strong intellectual curiosity Comfortable operating in ambiguous environmentsStrong experimental mindset Comfortable forming hypotheses and testing them through data Strong bias toward measurable outcomes Strong product and business judgment Comfortable making decisions independently Strong ability to prioritize competing technical problems High execution velocity Comfortable working in fast-paced startup environments Low-ego collaborative mentality Strong ability to communicate experimental findings Comfortable challenging assumptions and proposing better approaches Strong interest in AI agents and modern ML systems Excited about the impact of AI on accounting, finance, and enterprise workflows Comfortable working on-site in NYC 5 days/week
Compensation & Benefits
Base Salary: $175,000 – $350,000 Highly competitive equity package
Opportunity to work on frontier AI agent applications Opportunity to build foundational ML systems for accounting and finance High ownership over AI systems from research through production Exposure to advanced LLM, agentic AI, evaluation, and experimentation systems Opportunity to work with a highly accomplished technical team
