Staff Research Engineer

turing; United States; Remote; USD 250,000 - 400,000; Full time

Benefits offered by this job

Equity

Conference publication opportunities

High autonomy

Job summary

Turing is seeking a staff-level researcher to lead research engineering on frontier AI systems, shaping datasets, reinforcement learning environments, and benchmarks. You will own technical direction and translate findings into production-grade improvements with autonomy and impact.

Responsibilities include guiding investigations across domains, designing rigorous experiments, and contributing to the research community through publications and open-source work.

Qualifications

• Substantial research background in machine learning, AI systems, or related fields; demonstrated technical leadership.
• Deep proficiency with modern AI and ML tools for training, testing, and evaluating models at scale.
• Track record of designing experiments that yield evidence-based, reproducible conclusions.
• Strong communication skills for researchers and cross-functional partners.
• History of contributing to AI research through publications, open-source work, or industry equivalents.

Responsibilities

• Lead investigations into capabilities and training methods of frontier AI systems across domains.
• Design rigorous experiments, prototypes, tooling, and evaluation frameworks for synthetic data and RL.
• Build and operate RL environments for coding and browser-use agents and function-calling workflows.
• Translate research findings into scalable product and platform improvements with cross-functional teams.
• Contribute to the research community via publications, open-source work, workshops, and mentoring.
• Uphold practices for experimental rigor, data quality, reproducibility, and interpretation.

Skills

Substantial research background in ML/

Deep proficiency with AI/ML tools

Experimental design & reproducible 연구

Job description

Role overview

This staff-level position leads research engineering work on frontier AI systems, designing datasets, reinforcement learning environments, and benchmarks that improve advanced model capabilities across domains. The role spans foundational research through applied development, with ownership over technical direction and the autonomy to translate findings into production-grade improvements.

Responsibilities

• Lead investigations into the capabilities, limitations, and training methods of frontier AI systems across domains such as software engineering, finance, retail, and developer tools.
• Design rigorous experiments, prototypes, tooling, and evaluation frameworks for synthetic data generation, reinforcement learning, post-training, and model understanding.
• Build and operate RL environments for coding agents, computer-use or browser-use agents, and general function-calling workflows.
• Translate research findings into scalable product and platform improvements through close collaboration with research, engineering, product, and operations teams.
• Contribute to the broader research community through publications, open-source work, workshops, conference participation, and mentorship.
• Establish and uphold practices for experimental rigor, data quality, reproducibility, and interpretation.

Requirements

• Substantial research background in machine learning, AI systems, or related fields, with demonstrated technical leadership.
• Deep proficiency with modern AI and ML tools for training, testing, and evaluating models at scale.
• Track record of designing experiments that yield evidence-based, reproducible conclusions.
• Strong communication skills for both specialized researchers and cross-functional partners.
• History of contributing to AI research through technical publications, open-source work, or industry equivalents.

Nice to have

• Experience building RL environments or benchmarks for coding agents, browser-use agents, or enterprise function-calling systems.
• Background in long-range, multi-step workflow design and evaluation.
• Familiarity with agentic AI systems and enterprise deployment contexts.

Benefits and work setup

• Compensation range of $250,000 to $400,000 OTE plus equity.
• Opportunity to set technical direction on post-training, RL environments, and frontier benchmarks.
• Potential to publish at leading AI conferences including ICLR, ICML, and NeurIPS.
• High-autonomy environment with significant commercial impact and rapid iteration.