Senior Frontier AI Research Engineer - Data & RL

Turing; Palo Alto (CA); Presencial; USD 250.000 - 350.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Equity

Competitive salary

Descripción de la vacante

Turing seeks Senior Research Engineers to advance frontier AI systems, developing data, RL environments, and evaluation benchmarks. You will work across research and engineering to translate ideas into scalable applications, collaborating with teams across the company.

Office-based role, five days a week, in San Francisco, Palo Alto, or Seattle. The role emphasizes pushing the boundaries of synthetic data generation, post-training, and model understanding, with opportunities to contribute to

Formación

• PhD or Master’s degree in AI, ML, CS or related field is required or exceptional equivalent experience.
• Strong foundation in ML and practical experience designing experiments, training or evaluating models, and using modern AI tooling.
• Experience with synthetic or agentic data generation, RL or post-training, evaluation, benchmarks.

Responsabilidades

• Conduct research on frontier AI systems to inform products and strategy.
• Build and evaluate research systems, datasets, experiments, prototypes, tooling, and evaluation frameworks.
• Translate research findings into product improvements and scalable applications.
• Contribute to the research community via reports, open-source, workshops, or conferences.

Conocimientos

Research background

Technical depth

Relevant expertise

Research engineering

Scientific judgment

Communication

Educación

PhD or Master’s in AI/ML/CS

Descripción del empleo

Turing seeks Senior Research Engineers to advance frontier AI systems, developing data, RL environments, and evaluation benchmarks. You will work across research and engineering to translate ideas into scalable applications, collaborating with teams across the company.

Office-based role, five days a week, in San Francisco, Palo Alto, or Seattle. The role emphasizes pushing the boundaries of synthetic data generation, post-training, and model understanding, with opportunities to contribute to