As the Scientific Computing and AI Engineer, you will provide hands-on technical leadership in designing and evolving our GPU clusters, cloud-scale workloads, and generative AI solutions. Architecting systems that accelerate drug discovery and clinical development, you will bridge deep engineering craft with scientific impact to deliver robust, production-grade platforms.
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 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 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
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
- 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
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 401k, healthcare coverage, ESPP and a broad range of other benefits. Learn more at https://www.denalitherapeutics.com/careers
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.
