Associate Director, Scientific Computing and AI Engineer
dnli · South San Francisco ·
- Category
- AI engineering
- Experience
- 3+ years
dnli · South San Francisco ·
Neurodegenerative diseases are one of the largest medical challenges of our time. Denali Therapeutics is a biotechnology company dedicated to developing breakthrough therapies for neurodegenerative diseases through our deep commitment to degeneration biology and principles of translational medicine. Denali is founded on the collaboration of leading scientists, industry experts, and investors who share the vision that scientific discovery energetically applied to translational medicine is the key to delivering effective therapies to patients.
We invite you to consider an opportunity with Denali to help achieve our goal of delivering meaningful therapeutics to patients.
Key Accountabilities/Core Job Responsibilities:
Lead the architecture, build-out, and ongoing optimization of on-premise GPU clusters, hybrid cloud HPC environments, and supporting storage and networking infrastructure
Partner with research scientists to profile workloads, size infrastructure, and iteratively improve job performance and researcher self-service capabilities
Design, operate, and support compute environments for computationally intensive workloads such molecular dynamics (Schrödinger), CryoEM, genomics, structural biology, and AI model training
Implement job scheduler configurations (Slurm/LSF), parallel file systems, and interconnect optimization to maximize throughput and utilization for scientific users
Architect cloud-burst strategies for elastic scaling of peak HPC demand and ML training workloads
Design and engineer production AI/ML systems and generative AI solutions spanning cloud infrastructure, data pipelines, vector databases, RAG architectures, and LLM application layers
Build and deploy agentic AI workflows that automate or augment scientific and processes
Develop and maintain AI evaluation frameworks, prompt engineering standards, and model lifecycle management practices (MLOps) that ensure reliable, auditable outputs in a GxP-adjacent environment
Prototype and pilot emerging AI capabilities (AI agents, digital twins, foundation model fine-tuning) and transition proven solutions to production at scale
Collaborate with cross-functional stakeholders to scope AI use cases, define success criteria, and demonstrate concrete business value through working proof-of-concept and production deployments
Implement Infrastructure as Code and CI/CD pipelines with integrated security and compliance controls
Serve as the senior technical partner for scientific computing and AI platform decisions; set engineering standards, reference architectures, and technology guardrails in collaboration with Enterprise Architecture
Mentor and develop engineers across the IT organization and elevate team-wide engineering practices
Translate complex technical concepts for non-technical stakeholders including senior leadership and R&D scientists
Evaluate vendor and open-source technologies; lead proof-of-concept assessments and build vs. buy recommendations for new platform capabilities
Participate in architecture review processes to ensure cross-functional alignment and long-term platform coherence
Bachelor’s or Master’s degree in Computer Science, Engineering, or a closely related field
Typically, 10 - 12+ years of progressive experience in platform engineering, scientific computing, or infrastructure engineering, with at least 3 years operating at a senior/principal individual contributor level
Deep, hands-on expertise in two or more of the following: HPC cluster administration and optimization (Slurm/LSF, parallel file systems, GPU/CUDA environments); cloud platform engineering at production scale; AI/ML platform engineering including model deployment, MLOps pipelines, and LLM application development; generative AI and agentic system design using modern frameworks (LangChain, LangGraph, AutoGen) and foundation models
Proficiency in Infrastructure as Code and CI/CD tooling; strong Python and scripting skills
Experience working in or supporting regulated biotech, pharmaceutical, or life sciences environments with exposure to GxP, 21 CFR Part 11, or equivalent data integrity frameworks
Demonstrated ability to lead technical initiatives end-to-end—from architecture through production delivery—in a lean, resource-constrained organization
Hands-on experience with vector databases knowledge graphs, and RAG architectures for scientific or enterprise applications
This compensation and benefits information is based on Denali’s good faith estimate as of the date of publication and may be modified in the future.
Denali is committed to its core company value of unity by creating a diverse and inclusive environment. We are proud to be an equal opportunity employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, basis of disability, or any other federal, state, or local protected class.