Member of Technical Staff - Applied ML

Raydar; New York (NY); Sur place; USD 175 000 - 350 000; Plein temps

Avantages offerts par ce poste

Equity

Visa sponsorship

Résumé du poste

Raydar, a Series B AI company in New York, is seeking a Machine Learning Engineer to own end-to-end projects that bring intelligence into production. You will work on systems that help agents reason, plan and evaluate themselves with full autonomy to plan projects, run experiments and decide when to ship.

Ideal candidates have 4–12 years of ML engineering experience, strong Python, and a background in scalable ML experiments; visa sponsorship available; on-site in Flatiron, NYC.

Qualifications

• Four to twelve years of ML engineering experience with end-to-end LLM-based agent apps.
• Experience at fast-paced startups or top technology firms.
• CS/Physics/Math degree from a top school.

Responsabilités

• Design multi-agent architectures with autonomy boundaries.
• Build evaluation pipelines and metrics for hundreds of experiments.
• Instrument the stack to detect regressions and drive improvements.
• Engineer context and retrieval: prompt stacks and parsing of documents into structured representations.
• Scope projects, write concise specs and communicate progress within your pod.

Connaissances

Machine Learning Engineer

LLM-based agents

ML Experiments

Strong communicator

Technical degree in CS/Physics/Math

Formation

CS/Physics/Math degree

Outils

Python

LLM/Transformers

Description du poste

About the Company

Our client is a Series B AI company, valued above $1B, whose agents do real work in a high-stakes, regulated professional domain. Founded in 2023, it has raised more than $100M, has grown from about 60 to roughly 150 employees this year and expects to keep growing quickly. The team is based in New York City.

About the Role

Own end-to-end projects that bring intelligence into production. You will be responsible for systems that help agents reason, plan and evaluate themselves, with full autonomy to plan projects, define success, run experiments and decide when a system is ready to ship. This role suits engineers who want to operate like researchers and builders at once.

What You Will Do

• Design and iterate multi-agent architectures, encoding autonomy boundaries, tool use and fallback behavior that make agents safe and reliable.


• Build scalable offline and online evaluation pipelines that run hundreds of experiments, with golden tasks, labeling strategies and metrics.


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


• Engineer context and retrieval: prompt stacks, indexing pipelines and parsing of messy documents into structured representations.


• Scope projects clearly, write concise specs and communicate progress within your pod.

What We Are Looking For

• Four to twelve years as a machine learning engineer, with end-to-end LLM-based agent applications including benchmarking, orchestration and evaluations.


• Experience at a fast-paced startup (Series A to D), a quant or hedge fund, or a top-tier technology company.


• Experience running structured ML experiments and building evaluation infrastructure.


• A CS, Physics, Math or related technical degree from a top school. The role is applied rather than research-focused.


• A very clear communicator who can break complex ideas down.


• Currently located in the US or Canada, ideally on the East Coast.

Bonus Points

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


• Interest in the impact of AI on professional services and the economy.

Compensation and Benefits

$175,000-$350,000 base salary plus highly competitive equity, depending on experience. Visa transfers and sponsorship are available.

Location and Work Model

Full-time and on-site five days a week in Flatiron, New York City.