# Head of Bio AI - Radial

**Company:** [Astera](https://hotfix.jobs/companies/astera)
**Location:** Emeryville, CA
**Role:** ML Engineering
**Salary:** $400k – $600k/yr
**Skills:** Machine Learning, Diffusion Models, Transformers, Foundation Models, Representation Learning, Large-Scale Training, Data Pipelines, Distributed Computing, Generative Modeling, Inverse Problems, Physics-Based Modeling, Ml Pipelines
**Posted:** 2026-03-10

> Leads technical direction for bio AI research programs, architects AI/computational infrastructure, prototypes modeling systems, and builds high-caliber engineering team. Requires deep ML expertise in generative models, large-scale training, and bio applications with hands-on leadership.

## Job Description

## Key Outcomes (12–24 Months)

### Technical Architecture & Program Leadership
- Establish AI and computational architecture across current and future programs, beginning with DiffUSE and its expansion across multiple structural biology data modalities.
- Shape which foundational bottlenecks Radial addresses and how they become durable technical systems.
- Help make build-vs-fund-vs-partner decisions across Radial’s program portfolio.

### Hands-On Building
- Conceive, architect, and directly prototype early modeling systems and core infrastructure.
- Design scalable training workflows, data pipelines, validation frameworks, and evaluation benchmarks.
- Evolve capabilities from early prototyping to production-grade, open platforms the scientific community can build on.

### Team & Culture
- Build a high-caliber AI and engineering team, starting small and scaling deliberately.
- Set the technical bar through hands-on contribution and architectural authorship.
- Establish a culture of technical rigor, intellectual honesty, and disciplined experimentation.

### Ecosystem & Impact
- Engage leading scientific collaborators and build technically rigorous partnerships.
- Represent Radial’s technical vision within the AI and life sciences communities.
- Ensure outputs are structured and shared as durable public goods.

## Competencies

### Functional Expertise
- Deep expertise in modern ML: large-scale training, representation learning, generative modeling (**diffusion**, **transformers**, **foundation models**).
- Track record of translating ideas into working, scalable systems.
- Understanding of the interplay between machine learning and physics-based modeling.
- Direct experience formulating and solving inverse problems, including familiarity with ill-posedness, regularization strategies, and the trade-offs between learned and model-based reconstruction approaches.
- Experience building and iterating on ML pipelines for large-scale, data-intensive problems, including efficient data ingestion, preprocessing of high-dimensional inputs, and training workflows that scale across distributed compute resources.
- Systems-level thinking across data generation, modeling, infrastructure, and experimentation.
- Experience designing technical platforms intended to endure and compound over time.
- Scientific range beyond a single modality can engage across problem domains.

### Leadership Attributes
- Has managed small technical teams; knows how to attract and retain exceptional talent.
- Hands-on builder who sets the standard through their own work.
- Deep familiarity with the frontier AI x Bio landscape.
- Operates effectively in ambiguity; creates clarity through disciplined technical judgment.

### Cultural Alignment
- Motivated by the chance to build something that doesn’t exist yet.
- Committed to open science and durable public infrastructure.
- Thrives in early-stage, open-ended environments blending AI and biology.

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