Research Engineer (LLMs and Generative Models)

Key Responsibility

• Design and own AIDO’s internal model ecosystem, including scalable infrastructure for serving, finetuning, distillation, and inference across many model sizes and architectures.
• Develop reusable pipelines for on-demand model finetuning using internal and external datasets, ensuring reproducibility and cost-efficiency.
• Build APIs and inference tools that integrate deeply with downstream biology simulators.
• Productionize foundation model interfaces, including transformer-based LLMs and diffusion/auto-regressive architectures, with an emphasis on biological data modalities (DNA, RNA, protein, etc.).
• Collaborate with research and product teams to enable virtual experiments powered by generative AI, including agentic workflows and user-facing tooling.
• Support distillation, quantization, and routing strategies to optimize model throughput and enable multi-model orchestration.
• Prioritize observability, reliability, and safety in generative workflows through better logging, traceability, and rollback mechanisms.
• Ensure scalability and automation throughout the model lifecycle: training, testing, deployment, and adaptation.
• Automate everything.

Qualifications

• M.S. or equivalent practical experience in MLOps, Computer Science, Engineering, or related field.
• 2+ years of experience developing, deploying, and evaluating LLMs or generative models (transformers, diffusion models, VAEs, autoregressive architectures, etc.).
• Proficiency with deep learning research and production stacks, such as PyTorch, HuggingFace Transformers & Accelerate, or Megatron-LM/DeepSpeed.
• Strong programming skills in Python, with experience developing model services and backend APIs (Flask, FastAPI, or similar). 
• Familiarity with GPU-accelerated tools (e.g., CUDA, cuDNN, Triton) and profilers (PyTorch Profiler, Nsight Systems, TensorBoard)
• Familiarity with resource coordination platforms (e.g., SLURM, Kubernetes), and managed solutions (Vertex AI, SageMaker, OCI Data Science)
• Familiarity with ML automation frameworks (e.g. Kubeflow, Argo Workflows, Apache Airflow, Metaflow).
• Expertise in cloud computing (GCP, OCI, AWS).
• Strong software engineering practices: testing, version control, CI/CD pipelines.
• Ability to work in a fast-moving research environment, productionizing new models as they become available.

Preferred Qualifications

• Ph.D. degree in Computer Science, Engineering, or related field. Experience in life sciences or healthcare is a plus.
• Experience with biological data modalities (e.g., DNA, RNA, protein sequences, cell imaging).
• Prior work on multimodal or multiscale models across text, sequences, images, or structure.
• Background in model distillation, quantization, and memory/latency optimization.
• Knowledge of RESTful API design and data security.
• Strong written and verbal communication skills, especially across research, product, and engineering.
• Deep curiosity about biology and excitement to build tools that democratize access to scientific exploration.