# Principal AI Scientist

**Company:** [Astera](https://hotfix.jobs/companies/astera)
**Location:** New York, NY, San Francisco, CA
**Role:** AI Research
**Salary:** $250k – $350k/yr
**Experience:** 7+ years
**Skills:** Reinforcement Learning, preference optimization, diffusion models, Transformers, Model Training, Python, PyTorch, gpu clusters
**Posted:** 2026-07-29

> Principal AI Scientist at Polytope Bio (Astera) to co-direct development of reinforcement learning post-training methods that close the loop between frontier biological AI models and high-throughput experimental data. Requires PhD (or equivalent), hands-on training of large models from scratch, and deep RL expertise; ideal for researchers seeking scientific leadership and potential co-founding role.

## Job Description

## Responsibilities
- Co-direct the research program: Partner directly with the founder to shape the scientific vision, research direction, and technical execution.
- Develop RL feedback loop: Design, implement, and improve model post-training methods that translate high-throughput biological measurements into direct reward signals for biological language models.
- Hands-on engineering: Work directly with the technical founder to architect model training infrastructure and build, run, and debug models, training loops, and evaluation metrics.
- Bridge wet/dry lab: Partner with the experimental team to ensure that what we measure in the lab and what the models learn are designed as a single, cohesive system.

## Qualifications
- PhD in machine learning, computational biology, or a related field with a minimum of 2 to 5 years of industry research experience post-PhD (accomplished researchers without a PhD are also encouraged to apply).
- Trained models from scratch, not just fine-tuned or called APIs. Owned real training runs, know where they break, and know how to debug them.
- Hands-on experience with generative diffusion models and/or transformer architectures.
- Deep familiarity with modern reinforcement learning and preference-optimization methods for deep learning.
- Track record of strong research via publications, open-source work, shipped models, or equivalent evidence that you drive results.
- Highly self-directed but thrive in a tight-knit, collaborative founding partnership.
- Comfort operating with ambiguity and research ownership. Excited to build the infrastructure and grow the team.

## Nice-to-Haves
- Familiarity with biological research (protein modeling, sequence models, structural biology, or adjacent areas).
- Experience building and scaling training infrastructure on large GPU clusters.
- Past team management and technical leadership experience.

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