Senior Research Engineer

turing; United States; Remote; USD 250,000 - 350,000; Full time

Benefits offered by this job

Equity

High autonomy

Job summary

Turing seeks a senior research engineer at the frontier of AI to build large-scale datasets and reinforcement learning environments that improve model capabilities. The role spans synthetic data generation, agentic workflows, post-training, and evaluation across enterprise domains.

You will design experiments, prototype tooling, and train and evaluate models with modern ML tools, translating insights into scalable real-world applications in collaboration with research, engineering, product, and

Qualifications

• Strong research background in ML/AI systems and related fields.
• Proficiency with modern AI/ML tools for training, testing, and evaluating models.
• Ability to design experiments, analyze results, and iterate from hypothesis to insight.
• Strong communication skills for both technical specialists and cross-functional audiences.
• Track record of contributing to AI research through publications, open-source work, or equivalent industry experience.

Responsibilities

• Investigate capabilities, limitations, and training methods of advanced AI systems, formulating research questions that inform technical strategy.
• Design rigorous experiments, prototypes, tooling, and evaluation frameworks for synthetic and agentic data generation, reinforcement learning, and post-training.
• Train, test, and evaluate models using modern machine learning tools, drawing evidence-based conclusions from results.
• Collaborate across research, engineering, product, and operations teams to translate promising ideas into scalable real-world applications.
• Share findings through technical reports, open-source contributions, workshops, or conference participation.
• Establish practices for experimental rigor, reproducibility, and data quality across studies.

Skills

Research background

ML tooling

Experiment design

Communication

Publications/Open‑source

Job description

Role overview

This senior position sits at the intersection of frontier AI research and practical engineering, focused on building large-scale datasets and reinforcement learning environments that improve advanced model capabilities. The work spans synthetic data generation, agentic workflows, post-training, and evaluation across domains like software engineering, finance, retail, and developer tools.

Responsibilities

• Investigate capabilities, limitations, and training methods of advanced AI systems, formulating research questions that inform technical strategy.
• Design rigorous experiments, prototypes, tooling, and evaluation frameworks for synthetic and agentic data generation, reinforcement learning, and post-training.
• Train, test, and evaluate models using modern machine learning tools, drawing evidence-based conclusions from results.
• Collaborate across research, engineering, product, and operations teams to translate promising ideas into scalable real-world applications.
• Share findings through technical reports, open-source contributions, workshops, or conference participation.
• Establish practices for experimental rigor, reproducibility, and data quality across studies.

Requirements

• Strong research background with experience in machine learning, AI systems, or related fields.
• Proficiency with modern AI and ML tools for training, testing, and evaluating models.
• Ability to design experiments, analyze results carefully, and iterate from hypothesis to validated insight.
• Strong communication skills for both technical specialists and cross-functional audiences.
• Track record of contributing to AI research through publications, open-source work, or equivalent industry experience.

Nice to have

• Experience designing RL environments for coding agents, computer-use or browser-use agents, or function-calling agents.
• Background building benchmarks or evaluation frameworks for frontier models across long-range, multi-step workflows.
• Familiarity with agentic AI design across enterprise and consumer applications.

Benefits and work setup

• Compensation range of $250,000 to $350,000 OTE plus equity.
• Opportunity to collaborate with leading AI labs and enterprises on post-training and RL environment design.
• Potential to publish work at top-tier conferences such as ICLR, ICML, and NeurIPS.
• High-autonomy environment emphasizing rapid iteration and meaningful commercial impact.