# Senior/Staff AI Scientist

**Company:** [Astera](https://hotfix.jobs/companies/astera)
**Location:** New York, NY, San Francisco, CA
**Role:** AI Research
**Salary:** $200k – $300k/yr
**Experience:** 7+ years
**Skills:** Reinforcement Learning, generative diffusion models, transformer architectures, Model Training, Python, PyTorch, gpu clusters, protein modeling, structural biology
**Posted:** 2026-07-29

> Senior/Staff AI Research Scientist building post-training RL feedback loops and generative models that align frontier biological AI to high-throughput experimental measurements of folding, binding, and function. Requires PhD (or equivalent), hands-on training of models from scratch, and experience with diffusion/transformers/RL.

## Job Description

## Responsibilities
- Drive core research: Work closely with the technical founder and team to develop and execute the scientific vision, develop cutting-edge modeling approaches, and iterate rapidly on new ideas.
- 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 team 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 1-2 years of post-PhD research or industry experience (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.
- 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 early-stage environment.
- Comfort operating with ambiguity and a desire to build something new. Excited to tackle hard problems and potentially transition into a technical co-founder in the future.

## 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.

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