About Us: We are a forward-thinking technology company focused on harnessing AI to accelerate advancements in drug discovery and the broader chemistry domain. Our interdisciplinary team leverages state-of-the-art deep learning to drive breakthroughs with tangible real-world impact.
Role Overview: We are seeking an experienced AI Engineer with a strong track record in building and training large-scale deep learning models, with a focus on PyTorch and the Deep Graph Library (DGL). The ideal candidate will have hands-on experience with multi-GPU training using Distributed Data Parallel (DDP), and a passion for applying AI to real-world problems in drug discovery or chemistry.
Responsibilities:
Design, build, and train advanced deep learning models using PyTorch and DGL, targeting large-scale datasets and real-world chemical/biological data.
Implement efficient multi-GPU training pipelines leveraging PyTorch Distributed Data Parallel (DDP) or equivalent distributed frameworks.
Optimize and scale models for high-performance training and inference.
Collaborate with chemists, biologists, and other engineers to translate domain expertise into robust AI solutions.
Stay up-to-date with the latest research and innovations in AI, drug discovery, and computational chemistry.
Contribute to code reviews, documentation, and mentorship of junior engineers.
Publish findings and advancements where appropriate.
Qualifications:
Education: Master’s degree (preferred) in Computer Science, Computational Chemistry, Machine Learning, or a related discipline.
Experience: Minimum of 5 years of hands-on industry or post-grad research experience building, training, and optimizing large deep learning models.
Technical Skills:
Expert in PyTorch; strong experience with DGL or other graph neural network libraries.
Proven track record implementing multi-GPU/distributed training (PyTorch DDP required).
Proficient in Python and key scientific computing packages.
Experience with model deployment, performance profiling, and troubleshooting distributed systems.
Domain Advantage:
Previous exposure to drug discovery, cheminformatics, computational chemistry, or related biosciences is a strong plus.
Other Skills:
Excellent communication skills and the ability to work effectively in interdisciplinary teams.
Strong problem-solving skills with attention to detail.
Nice to Have:
Prior publications or contributions to open source projects in AI for science.
Familiarity with high-performance computing clusters, cloud AI environments, or MLOps pipelines.
Experience integrating traditional cheminformatics approaches with deep learning.
Why Join Us?
Make a real-world impact in a rapidly evolving field.
Work alongside a diverse, motivated, and collaborative team.
Access to state-of-the-art computational resources.
[List unique benefits, e.g., conference stipends, flexible work, equity, etc.]
How to Apply: Send your resume and a cover letter detailing your relevant experience through the contact page, along with links to any publications, project portfolios, or GitHub repositories, to rajesha.m@sravathi.ai or samir.anapat@sravathi.ai.
We are committed to creating a diverse environment and are proud to be an equal opportunity employer.