Associate Director, Scientific Computing and AI Engineer

Denali Therapeutics Inc; South San Francisco (CA); On-site; USD 159,000 - 207,000; Full time

Benefits offered by this job

401(k)

Healthcare coverage

Employee Stock Purchase Plan

Job summary

Denali Therapeutics Inc is looking for a senior platform engineer in South San Francisco specializing in scientific computing and AI solutions. Responsibilities include leading HPC cluster architecture, collaborating with research scientists to optimize workflows, and designing production AI systems.

The ideal candidate has over 10 years of related experience, significant expertise in HPC optimization, and strong skills in Python. A competitive salary range of $159,000 to $207,000 is offered, along with a comprehensive benefits package.

Qualifications

• 10–12+ years of experience in platform engineering or scientific computing.
• At least 3 years at a senior/principal individual-contributor level.
• Deep expertise in HPC cluster optimization and AI/ML platform engineering.

Responsibilities

• Lead the architecture and optimization of on-premise GPU clusters.
• Collaborate with research scientists to improve job performance.
• Design production AI/ML systems and generative AI solutions.

Skills

HPC cluster administration and optimization

Cloud platform engineering

AI/ML platform engineering

Python scripting

Infrastructure as Code

Education

Bachelor’s or Master’s degree in Computer Science or Engineering

Tools

Slurm/LSF

GPU/CUDA environments

Vector databases

Job description

Key Accountabilities / Core Job Responsibilities

• 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 such as 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.
• Applied AI Engineering & Generative AI Solutions: 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 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.
• Technical Leadership & Architecture Guidance: 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.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a closely related field.
• 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 Part11, 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.

Salary and Benefits

Salary Range: $159,000.00 to $207,000.00. Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies, and experience.

Denali offers a competitive total rewards package, which includes a 401(k), healthcare coverage, ESPP and a broad range of other benefits.

Equal Opportunity Employer

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.