ML Engineering Intern

We are looking for a Software Engineering Intern who will be instrumental in bringing open-source bio models onto our internal platform. You will work on the infrastructure layer that supports large-scale inference and fine-tuning pipelines, helping integrate AI models into cutting-edge biological applications.

• Model Integration: Take open-source biological ML models (protein language models, DNA/RNA transformers, etc.) and implement them on our GPU-based infrastructure for real-time inference.

• Infrastructure & Deployment: Containerize models in Docker and contribute to deployment workflows, ensuring high availability and efficient resource utilization.

• Collaboration & Documentation: Collaborate with AI researchers, software engineers, and external partners to create clear, user-friendly documentation. This includes best practices around model usage and guidance for researchers on how to integrate models into their workflows.

• Scientific Context & Model Usability: Conduct literature reviews and incorporate domain-specific knowledge to optimize models for biological applications. Help develop APIs and documentation that empower scientists to leverage these models effectively in real-world biotech scenarios.

• Performance Optimization: Investigate bottlenecks and improve runtime performance of the model pipelines—e.g., distributed training, fast data loading, GPU utilization.

• Programming Skills: Proficiency in Python. Experience with one or more ML frameworks (e.g., PyTorch, TensorFlow) is highly preferred.

• Software Engineering Foundations: Familiarity with version control (Git), code reviews, and a solid understanding of data structures and algorithms.

• Cloud & Containerization: Some exposure to AWS, GCP, or Azure. Experience with Docker/Kubernetes for container orchestration is a plus.

• Data Processing: Understanding of distributed data frameworks (e.g., Spark, Dask) and Python data libraries (Pandas, NumPy).

• Biology & AI Enthusiasm: Some familiarity with life sciences (proteins, genomics, etc.) is a strong plus, and a genuine curiosity for how AI can drive biotech innovation is strongly encouraged.

• Team Player & Fast Learner: Comfort working in a dynamic startup environment, with the ability to pick up new concepts quickly and communicate effectively across multidisciplinary teams.

• Hands-On Experience: Work on real-world AI infrastructure projects that directly impact cutting-edge biotech research.

• Mentorship: Learn from a founding team with deep industry experience in both AI/ML and biotechnology.

• Ownership: Interns at BioLM.ai have the opportunity to own significant portions of our codebase and make meaningful contributions.

• Exposure to Biotech & ML Research: Gain insight into how advanced AI is used to solve high-impact biological problems, from drug discovery to protein engineering.

If you're excited about the intersection of AI and biology, and you want to help build infrastructure that will power the next wave of biotech breakthroughs, we'd love to hear from you!