Research Scientist - Human-AI Systems

snorkelai; USA; Hybrid; USD 200.000 - 375.000; Vollzeit

Zusammenfassung

snorkelai is seeking a Research Scientist to advance high-quality datasets and agentic environments, spanning applied ML research, data engineering, and production systems. You will design experiments, collaborate with domain experts, and ship research into production workflows.

The role emphasizes developing reusable pipelines, rigorous experimentation, and cross-functional collaboration to close performance gaps for advanced models. Remote-friendly with options to base in major US tech hubs.

Qualifikationen

• PhD in machine learning, NLP, or related field preferred; equivalent industry or research lab experience considered.
• Experience developing and evaluating new ML methods.
• Experience building environments for AI agents and synthetic data generation.
• Strong experimental design with hypothesis definition and ablation studies.
• Solid software engineering with clean code and version control.

Aufgaben

• Design and optimize reusable pipelines combining AI with expert judgment to accelerate data and environment creation.
• Run rigorous experiments and ablations to assess impact on data quality and model performance.
• Collaborate with engineers, data ops, domain experts, and customers to productionize research.
• Work with SMEs to design tests for authoring, reviewing, and refining data and environments.
• Surface findings with product and data teams to inform roadmaps.
• Stay current with synthetic data and agent environments research and apply best practices.
• Represent the team’s research via publications, blogs, talks, and customer engagements.

Kenntnisse

AI research

NLP / LLMs

Experiment design

Collaboration

Software engineering

Ausbildung

PhD in ML / NLP or related field

Tools

Python

Git

Jobbeschreibung

Role overview

A frontier AI research team is hiring a Research Scientist to push forward how high-quality datasets and agentic environments are built. The role sits at the intersection of applied ML research, data engineering, and production systems, with a focus on turning human expertise into scalable, reusable pipelines that close performance gaps for advanced models. Work spans designing experiments, partnering with domain experts, and shipping research into production workflows.

Responsibilities

• Design, implement, and optimize reusable pipelines that combine AI capabilities with expert judgment to accelerate data and agentic environment creation.
• Run rigorous experiments, including ablations, to validate proof‑of‑concept approaches and measure impact on data quality, pipeline efficiency, and model performance.
• Partner with engineering, data operations, domain experts, and customers to convert research prototypes into reliable production workflows.
• Collaborate directly with subject‑matter experts to design and test workflows for authoring, reviewing, and refining data and environments.
• Work cross‑functionally with product, engineering, and data operations to surface findings that inform roadmap decisions.
• Stay current with research on synthetic data, agent environments, and data‑centric AI, and bring best practices back into internal workflows.
• Represent the team's research externally through publications, blog posts, conference talks, and customer engagements.

Requirements

• Strong research background in AI, machine learning, NLP, LLMs, or related fields, with experience developing and evaluating new methods.
• Experience building environments for AI agents in areas such as automated research, computer use, coding, or professional domain workflows.
• Hands‑on work with one or more of synthetic data generation, human‑in‑the‑loop workflows, reinforcement learning, agent environments, or model evaluation.
• Strong experimental design skills, including hypothesis definition and ablation studies.
• Solid software engineering practices, including clean code, modular design, and version control.
• Ability to collaborate with domain experts and translate their knowledge into concrete tasks, evaluation criteria, and repeatable workflows.
• Comfort with rapid iteration, ambiguous research questions, and moving ideas from experiment to production.
• Ph.D. in machine learning, NLP, or a related field preferred; equivalent industry or research lab experience considered.

Benefits and work setup

Remote-friendly with options to be based in San Francisco or New York City. Compensation is determined by skills, qualifications, experience, and location, with a published salary range of $200,000–$375,000 USD.