Member of Technical Staff - Applied ML

Member of Technical Staff - Applied ML

Full-time | On-site

Location: New York, NY

We are a rapidly growing AI enterprise focused on equipping professionals with teams of AI agents to automate complex, real-world workflows. We are seeking a highly autonomous Applied ML Engineer to own end-to-end projects that bring intelligence into production. This role is for engineers who want to operate like both researchers and builders, focused on shipping reliable, continuously improving agentic systems.

As a key member of our Machine Learning team, you will act as the Responsible Party (RP) for mission-critical systems. You will have full autonomy to scope, build, experiment, and decide when your solution is ready to ship, focusing on clarity, deep instrumentation, and safe, reliable operation.

What you’ll be doing:

• Build and Evolve Agent Systems: Design and iterate on multi-agent architectures that automate complex, regulated workflows.
• Encode autonomy boundaries, tool usage, and fallback behaviors to ensure agents are safe and reliable.
• Manage context and memory for coherence, planning and executing agent loops with measurable success criteria.
• Route, evaluate, and optimize models under real-world constraints (latency, cost, accuracy).
• Design Evaluation and Experimentation Frameworks: Build scalable evaluation pipelines (offline and online) that run hundreds of experiments automatically.
• Define golden tasks, labeling strategies, and metrics to make performance measurable and comparable.
• Instrument the stack to detect regressions, track error taxonomies, and drive a closed-loop improvement process.
• Engineer for Context and Retrieval (RAG):
• Architect prompt stacks and instruction hierarchies that structure model reasoning.
• Build retrieval and indexing pipelines that surface relevant context efficiently, including parsing messy documents into structured representations.
• Design guardrails and validation layers to ensure safe and deterministic agent behavior.
• Operate as an RP: Scope projects with clarity, write concise specs, build and instrument systems end-to-end, and communicate progress clearly ("what’s built, what’s learned, what’s next").

Requirements

We are looking for builders who excel at turning research insights into robust, production-ready systems and thrive in a fast-paced, high-risk environment.

Experience and Technical Skills:

• 3 – 10 years of experience developing complex data-heavy products and Machine Learning models.
• Expert proficiency in Python for building production-quality ML systems.
• Strong familiarity with relational databases (e.g., Postgres).
• Proven ability to reason, experiment, and ship systems from first principles with full autonomy.
• Experience in designing and running A/B tests or controlled experiments to drive architectural and product decisions.
• Strong analytical skills to define metrics, track error taxonomies, and measure agent performance.

Work Policy:

• This is a full-time, in-person role requiring attendance 5 days a week in our New York City (Flatiron) office. We believe being in-person is essential for operating on the frontier of what is technically possible.
• Visa sponsorship is available (can sponsor all types).

Benefits

We offer a highly competitive compensation package designed to reward engineers who are all-in on building at the cutting edge of AI.

• Salary Range: $175,000 - $300,000 (Base Salary).
• Equity: Highly Competitive Equity offering, targeted to be well above market average, reflecting the high-growth trajectory and risk appetite of the company.
• Healthcare: 100% employer-paid medical, dental, and vision coverage.
• In-Office Perks: Daily in-office lunches and a fully stocked kitchen.
• Ownership: Opportunity to gain significant responsibility and ownership from day one in an extremely fast-growing business.

Due to the high volume of applications we anticipate, we regret that we are unable to provide individual feedback to all candidates. If you do not hear back from us within 4 weeks of your application, please assume that you have not been successful on this occasion. We genuinely appreciate your interest and wish