Associate Director, Scientific Computing and AI Engineer
Denali Therapeutics
August 09, 2026
Remote friendly (South San Francisco, CA)
United States
IT
Scientific Computing & HPC Platform Engineering
- 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 (e.g., molecular dynamics, 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
- Architect cloud-burst strategies for elastic scaling of peak HPC demand and ML training workloads
Applied AI Engineering & Generative AI Solutions
- Design and engineer production AI/ML and generative AI solutions across cloud infrastructure, data pipelines, vector databases, RAG architectures, and LLM application layers
- Build and deploy agentic AI workflows to automate or augment scientific processes
- Develop and maintain AI evaluation frameworks, prompt engineering standards, and MLOps practices for reliable, auditable outputs in a GxP-adjacent environment
- Prototype and pilot AI capabilities (AI agents, digital twins, foundation model fine-tuning) and transition to production at scale
- Collaborate with stakeholders to scope AI use cases, define success criteria, and deliver proof-of-concepts and production deployments
- Implement Infrastructure as Code and CI/CD pipelines with integrated security and compliance controls
Technical Leadership & Architecture Guidance
- Serve as senior technical partner for platform decisions; set engineering standards and guardrails
- Mentor engineers and elevate engineering practices
- Translate technical concepts for non-technical stakeholders
- Evaluate technologies and lead proof-of-concept assessments and build-vs-buy
- Participate in architecture reviews for cross-functional alignment
Requirements
- Bachelor’s or Master’s in Computer Science/Engineering or closely related field
- Typically 10–12+ years progressive experience; at least 3 years at senior/principal IC level
- Deep hands-on expertise in 2+ areas: HPC (Slurm/LSF, parallel file systems, GPU/CUDA), production cloud engineering, AI/ML platform engineering (deployment, MLOps, LLM apps), generative/agentic systems (LangChain, LangGraph, AutoGen) and foundation models
- Proficiency in Infrastructure as Code and CI/CD; strong Python/scripting
- Experience with regulated biotech/pharma/life sciences; exposure to GxP and/or 21 CFR Part 11
- Proven end-to-end technical initiative leadership
- Hands-on vector databases, knowledge graphs, and RAG architectures
Compensation/Benefits
- Salary range: $159,000.00 to $207,000.00; 401k, healthcare coverage, ESPP, and other benefits.
Application
- Learn more at https://www.denalitherapeutics.com/careers
- 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 (e.g., molecular dynamics, 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
- Architect cloud-burst strategies for elastic scaling of peak HPC demand and ML training workloads
Applied AI Engineering & Generative AI Solutions
- Design and engineer production AI/ML and generative AI solutions across cloud infrastructure, data pipelines, vector databases, RAG architectures, and LLM application layers
- Build and deploy agentic AI workflows to automate or augment scientific processes
- Develop and maintain AI evaluation frameworks, prompt engineering standards, and MLOps practices for reliable, auditable outputs in a GxP-adjacent environment
- Prototype and pilot AI capabilities (AI agents, digital twins, foundation model fine-tuning) and transition to production at scale
- Collaborate with stakeholders to scope AI use cases, define success criteria, and deliver proof-of-concepts and production deployments
- Implement Infrastructure as Code and CI/CD pipelines with integrated security and compliance controls
Technical Leadership & Architecture Guidance
- Serve as senior technical partner for platform decisions; set engineering standards and guardrails
- Mentor engineers and elevate engineering practices
- Translate technical concepts for non-technical stakeholders
- Evaluate technologies and lead proof-of-concept assessments and build-vs-buy
- Participate in architecture reviews for cross-functional alignment
Requirements
- Bachelor’s or Master’s in Computer Science/Engineering or closely related field
- Typically 10–12+ years progressive experience; at least 3 years at senior/principal IC level
- Deep hands-on expertise in 2+ areas: HPC (Slurm/LSF, parallel file systems, GPU/CUDA), production cloud engineering, AI/ML platform engineering (deployment, MLOps, LLM apps), generative/agentic systems (LangChain, LangGraph, AutoGen) and foundation models
- Proficiency in Infrastructure as Code and CI/CD; strong Python/scripting
- Experience with regulated biotech/pharma/life sciences; exposure to GxP and/or 21 CFR Part 11
- Proven end-to-end technical initiative leadership
- Hands-on vector databases, knowledge graphs, and RAG architectures
Compensation/Benefits
- Salary range: $159,000.00 to $207,000.00; 401k, healthcare coverage, ESPP, and other benefits.
Application
- Learn more at https://www.denalitherapeutics.com/careers