AI Scientist
Skills
- Strong background in developing and implementing deep learning methods, preferably with demonstrated innovation
- Hands-on experience with PyTorch and large-model training and inference on GPUs
- Experience with distributed training frameworks and scaling ML workloads
- Demonstrated ability to communicate research to peers and general audiences
- Familiarity with applying ML to biological problems (protein/antibody design, genomics, or related areas)
Responsibilities
- Research, design, and implement novel deep learning architectures and algorithms for protein and antibody design and biological modeling
- Lead and execute projects that push state-of-the-art AI methods applied to drug discovery and clinical development
- Train and fine-tune large models on distributed GPU clusters and optimize inference pipelines
- Collaborate closely with biologists, drug discovery scientists, and clinicians to translate modeling advances into actionable experimental hypotheses
- Publish peer-reviewed research, present findings to internal and external audiences, and contribute to the open-source community
- Drive reproducible ML engineering practices, including versioning, benchmark creation, and model evaluation on biological tasks
Education
- PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field
- Postdoctoral or equivalent industrial research experience preferred
Benefits
- Competitive base salary, bonus, and equity
- Flexible and open work environment with hybrid/remote options
- Comprehensive benefits package (health, dental, vision — summary available to applicants)
- Career development opportunities and collaboration with interdisciplinary experts
- Opportunities to publish and contribute to open-source projects
Average salary estimate
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