Backed by Greylock and a16z speedrun.
About the role
We are looking for a research engineer to improve model capabilities through better data, training environments, and reinforcement learning. You will work across data generation, environment design, and post-training, with ownership from an initial hypothesis to a measured improvement in model behavior. The work calls for strong engineering, careful experimentation, and judgment about what is worth pursuing.
What they're looking for
- - Strong Python engineering skills and hands-on experience training or post-training language or multimodal models with PyTorch, JAX, or a comparable framework
- - Technical depth in reinforcement learning, synthetic data, or interactive environments, and the ability to work across the broader training pipeline
- - A practical understanding of policy optimization, reward design, and credit assignment in multi-step tasks
- - Evidence of rigorous experimental judgment: credible baselines, informative ablations, and clear explanations of what a result does and does not establish
- - Experience building dependable data or experiment infrastructure and debugging failures across the learning pipeline
More about this role
Sonder is an applied AI lab building models that learn how people work.
We bring research, engineering, and design together to build useful personal AI, with privacy and efficiency at the core. We are building our early team in New York.
We are looking for a research engineer to improve model capabilities through better data, training environments, and reinforcement learning.
You will work across data generation, environment design, and post-training, with ownership from an initial hypothesis to a measured improvement in model behavior. The work calls for strong engineering, careful experimentation, and judgment about what is worth pursuing.
You will work directly with researchers and engineers to identify capability gaps, design experiments, and bring successful results into our models.
What You'll Do
- Build high-quality data pipelines. Develop systems for generating, curating, and validating training data. Understand how coverage, quality, and data mixtures affect learning.
- Develop training environments. Build reliable environments for multi-step tasks, with reproducible execution and useful feedback for training and evaluation.
- Improve post-training. Establish supervised...
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