Deep Learning Intern

Vilya•Seattle, WA

2d•Onsite

About The Position

RESPONSIBILITIES: Design, develop, and maintain a real-world deep learning framework that improves or expands upon Vilya’s macrocycle drug design pipeline. Collaborate and communicate with fellow experts in deep learning as well as the broader interdisciplinary Vilya team, including our research scientists in chemistry and biology. Get hands-on experience working with heterogeneous data gathered by our chemistry and biology teams, including a variety of in vitro assay measurements. BASIC QUALIFICATIONS: Current PhD student in computer science, computational biology, bioinformatics, or a field applicable to computational drug discovery. Strong background in the fundamentals of machine learning, including linear algebra, calculus and statistics. Experience developing, deploying, and managing deep learning models and formatting large datasets for real-world problems. Proficiency in Python and PyTorch. Ability to work well with an interdisciplinary team and to communicate complex scientific ideas to diverse audiences. Availability to work for 12-16 weeks during Summer 2026 Ability to work on site in our Seattle, WA office. No relocation costs will be provided through this internship. PREFERRED QUALIFICATIONS: Experience with Slurm, Singularity, BigQuery and/or other cloud and high-performance computing infrastructure. Experience with concepts and deep learning techniques relevant to drug discovery and protein design, such as transformers, graph neural networks, equivariance, diffusion generative modeling, etc. Experience profiling and optimizing deep learning training/inference performance, including techniques such as pruning/quantization, parameter-efficient fine-tuning (e.g., adapters/LoRA), and implementing or integrating custom CUDA/Triton kernels or fused ops. Specific experience working with structure prediction or backbone diffusion models, such as AlphaFold, RosettaFold, Chroma, Boltz, etc. Experience working with reinforcement learning or active learning techniques, e.g. for optimizing molecules based on feedback from assays or expensive computational oracles. Experience with molecular property prediction, e.g. solubility, partition coefficients, toxicity, etc. Exposure to concepts in drug discovery such as ADME / Tox / DMPK. VILYA BENEFITS: Opportunity to work in a disruptive startup with a talented, experienced, and dedicated team Monthly commuter stipend Salary: $10,000/month for the duration of the internship

Requirements

Current PhD student in computer science, computational biology, bioinformatics, or a field applicable to computational drug discovery.
Strong background in the fundamentals of machine learning, including linear algebra, calculus and statistics.
Experience developing, deploying, and managing deep learning models and formatting large datasets for real-world problems.
Proficiency in Python and PyTorch.
Ability to work well with an interdisciplinary team and to communicate complex scientific ideas to diverse audiences.
Availability to work for 12-16 weeks during Summer 2026
Ability to work on site in our Seattle, WA office. No relocation costs will be provided through this internship.

Nice To Haves

Experience with Slurm, Singularity, BigQuery and/or other cloud and high-performance computing infrastructure.
Experience with concepts and deep learning techniques relevant to drug discovery and protein design, such as transformers, graph neural networks, equivariance, diffusion generative modeling, etc.
Experience profiling and optimizing deep learning training/inference performance, including techniques such as pruning/quantization, parameter-efficient fine-tuning (e.g., adapters/LoRA), and implementing or integrating custom CUDA/Triton kernels or fused ops.
Specific experience working with structure prediction or backbone diffusion models, such as AlphaFold, RosettaFold, Chroma, Boltz, etc.
Experience working with reinforcement learning or active learning techniques, e.g. for optimizing molecules based on feedback from assays or expensive computational oracles.
Experience with molecular property prediction, e.g. solubility, partition coefficients, toxicity, etc.
Exposure to concepts in drug discovery such as ADME / Tox / DMPK.

Responsibilities

Design, develop, and maintain a real-world deep learning framework that improves or expands upon Vilya’s macrocycle drug design pipeline.
Collaborate and communicate with fellow experts in deep learning as well as the broader interdisciplinary Vilya team, including our research scientists in chemistry and biology.
Get hands-on experience working with heterogeneous data gathered by our chemistry and biology teams, including a variety of in vitro assay measurements.

Benefits

Opportunity to work in a disruptive startup with a talented, experienced, and dedicated team
Monthly commuter stipend
Salary: $10,000/month for the duration of the internship

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