Research Scientist, Post-Training

True Talent Solutions Partners; San Francisco (CA); On-site; USD 150,000 - 250,000; Full time

Job summary

True Talent Solutions Partners is seeking an experienced ML researcher to join a post-training R&D team in San Francisco. You will design controlled experiments to quantify how data affects model capabilities, generalization, and alignment, and translate findings into practical recommendations.

You will publish research and build evaluation infrastructure, while collaborating with partner labs to ensure reproducible results across data and model configurations.

Qualifications

• Minimum 3 years of machine learning research with post-training or RL exposure.
• Experience implementing RL or post-training techniques in research or production.
• Strong results in published research or shipped ML systems.
• Deep familiarity with large language models and transformer architectures.
• Proficiency in Python and major DL frameworks; distributed training experience.

Responsibilities

• Design and run supervised fine-tuning and RL experiments to test data impacts.
• Build evaluation infrastructure, benchmarks, and new data types.
• Publish external research, blogs, and technical reports.
• Collaborate with internal teams to improve data quality from experiments.
• Work with partner labs to produce reproducible evidence about data and models.

Skills

ML research

Post-training RL

Python

PyTorch

TensorFlow

Distributed training

Open-source contributions

Education

PhD in ML/CS or equivalent

Tools

PyTorch

TensorFlow

Job description

About the Role

Join a post-training research team at a Series A AI research and training data startup. You will run controlled experiments to establish how training data affects model capabilities, generalization, and alignment, and turn findings into practical recommendations.

What You'll Do

• Design and run supervised fine-tuning and reinforcement learning experiments to measure the impact of datasets on model performance.

• Build shared evaluation infrastructure, benchmarks, public evaluations, and new data types.

• Publish external-facing research, blog posts, and technical reports.

• Work with internal teams to improve data quality based on experimental results.

• Collaborate with partner labs to produce clear, reproducible evidence about data and model outcomes.

What We're Looking For

• At least 3 years of machine learning research experience, including post-training, reinforcement learning, or related model optimization work.

• Experience implementing reinforcement learning algorithms or post-training techniques in research or production, and a track record of research publications or shipped machine learning systems.

• Strong familiarity with large language models, transformer architectures, and rigorous experimental evaluation.

• Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow, plus experience with distributed training or scaling machine learning systems.

• Experience at a frontier AI lab and comfort working across domains such as finance, software engineering, and policy.

• A PhD in machine learning, computer science, or a related field, or equivalent research experience; open-source contributions are also valued.

Compensation & Benefits

Base salary: $150,000 to $250,000 annually. Visa sponsorship is available.

Location

On-site in San Francisco, California, United States.