• 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

Join us as we embark on this journey to redefine the future of biology and medicine.

We are an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. GenBio AI participates in the U.S. Department of Homeland Security’s E-Verify program to confirm the employment eligibility of all newly hired employees. For more information on E-Verify, please visit www.e-verify.gov.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.