Research Engineer - Agency and Reasoning
Zyphra · San Francisco
CHECKED 6H AGO
Frontier labonsite
At a glance
Highlights
- Core contributor to Agency and Reasoning team building next-gen language models.
- Perform novel research in RL, post-training and human preference learning.
- Apply research ideas at scale to advance ambitious next-generation models.
What they offer
- Comprehensive medical, dental, vision, and FSA plans
- 401(k) plan
- Unlimited PTO and company holidays
- In-office snacks and meals provided
- Relocation and immigration support on a case-by-case basis
Ideal candidate
Curious researcher who loves understanding intelligence and thrives in fast-paced collaboration, eager to take bold ideas from conception to large-scale experiment with grounded, methodical rigor.
Requirements
- Strong research taste and intuition with ability to take research from conception to execution to write-up
- Strong implementation and prototyping skillset, highly proficient with PyTorch and Python
- Experience with reinforcement learning for language model reasoning or classical RL tasks
- Experience with supervised fine-tuning and preference learning methods such as DPO and simPO
- Experience with context-length extension methods
- Ability to understand model behaviors and correct them through iterative fine-tuning and data engineering
- Postgraduate degree in Computer Science, EE/EECS, Mathematics, Physics or related scientific field
- Published machine learning research in well-respected venues
- Excellent communication and collaboration skills in a high-paced research setting
- remote policy
- In-person
- visa sponsorship
- Immigration support available on a case-by-case basis
Lab profile
About Zyphra
San Francisco AI lab building open language and audio models and autonomous desktop agents.
What they build
- Desktop and browser-based autonomous agents
- Open language models
- Open-source audio models (ASR, TTS, speech-to-speech)
Known for
- Agentic systems and human-AI interaction
- Reinforcement learning, post-training and preference learning
- Large-scale language model pre-training
- Novel model architectures and training methods
Key facts
- Headquartered in San Francisco, California