Research Engineer (LLMs and Generative Models)

Key Responsibility

• Build, refine, productionize, and serve generative models and workflows for biological simulation and experiment orchestration.
• Work at the intersection of science, product, and engineering to identify problems, implement solutions, and measure their impact on AIDO.
• Develop APIs and pipelines for model serving, distillation, and tool integration with AIDO’s foundation models.
• Design, implement, and evaluate LLM-based agents to collaborate with users and other agents to plan virtual experiments, run studies, and synthesize knowledge.
• Support on-demand finetuning and model adaptation of foundation models with specialized datasets.
• Work at the intersection of science, product, and engineering to prototype end-to-end research workflows and the tools to support them.
• Contribute to system robustness by improving uncertainty quantification, observability, and traceability in generative workflows.
• Maintain close attention to efficiency and scalability of models to ensure simulations are cheap, fast, and reproducible.
• Automate everything.

Qualifications

• Ph.D. or equivalent practical experience in Computer Science, Engineering, or related field. Experience in life sciences or healthcare is a plus.
• 2+ years of experience developing, deploying, and evaluating LLMs or generative models (transformers, diffusion models, VAEs, autoregressive architectures, etc.).
• Proficiency with major deep learning frameworks such as PyTorch, HuggingFace Transformers & Accelerate, or Megatron-LM/DeepSpeed.
• Strong programming skills in Python, and modern web development frameworks, and familiarity with GPU-accelerated tools (e.g., CUDA, cuDNN, Triton).
• Familiarity with resource management and scheduling systems (e.g., SLURM, Kubernetes) and associated automation frameworks (e.g. Kubeflow, Argo Workflows, Apache Airflow, Metaflow).
• Ability to work in a fast-moving research environment, balancing rigor with rapid prototyping.
• Proficiency in back-end frameworks like Django, Flask, or Node.js, and database technologies (e.g., PostgreSQL, MongoDB).
• Expertise in cloud computing (GCP, AWS).
• Familiarity with version control systems like Git and CI/CD pipelines. 

Preferred Qualifications

• Ph.D. degree in Computer Science, Engineering, or related field. Experience in life sciences or healthcare is a plus.
• Exposure to distributed training, model distillation, and serving infrastructure.
• Experience with multimodal or multiscale modeling (text, sequence, structure, image).
• Familiarity with biological data modalities (DNA, RNA, protein, cell imaging), associated bioinformatics tools, and their unique challenges.
• Hands-on experience with agentic systems (LangChain, AutoGPT, custom planner-executor loops).
• Strong understanding of RESTful APIs, authentication, and deployment pipelines
• Strong communication skills, with the ability to collaborate across research, engineering, and product teams.
• Interest in building tools that democratize access to biology and empower both expert researchers and newcomers.