Associate Director, Scientific Computing and AI Engineer

Scientific Computing & HPC Platform Engineering

• Lead architecture, build-out, and optimization of on-premise GPU clusters, hybrid cloud HPC environments, and supporting storage/network infrastructure
• Partner with research scientists to profile workloads, size infrastructure, and iteratively improve job performance and self-service
• Design, operate, and support compute environments for computational workloads (e.g., molecular dynamics, CryoEM, genomics, structural biology, AI model training)
• Implement scheduler configurations (Slurm/LSF), parallel file systems, and interconnect optimization to maximize throughput/utilization
• Architect cloud-burst strategies for elastic scaling of peak HPC/ML training demand

• Design/engineer production AI/ML and generative AI systems (cloud infrastructure, data pipelines, vector databases, RAG, LLM application layers)
• Build/deploy agentic AI workflows to automate/augment scientific processes
• Develop AI evaluation frameworks, prompt engineering standards, and MLOps for reliable, auditable outputs in a GxP-adjacent environment
• Prototype/pilot AI capabilities (AI agents, digital twins, foundation model fine-tuning) and transition to production
• Scope AI use cases, define success criteria, and demonstrate value via proofs of concept and deployments
• Implement Infrastructure as Code and CI/CD with integrated security/compliance controls

• Set engineering standards, reference architectures, and technology guardrails; mentor engineers
• Translate technical concepts for non-technical stakeholders
• Evaluate technologies; lead proof-of-concept assessments and build‑vs‑buy decisions
• Participate in architecture reviews for platform coherence

• Bachelor’s/Master’s in CS, Engineering, or related field
• Typically 10–12+ years experience; 3+ years at senior/principal IC level
• Deep expertise in 2+ areas: HPC (Slurm/LSF, parallel file systems, GPU/CUDA), cloud production platform engineering, AI/ML platform engineering (model deployment, MLOps, LLM apps), generative/agentic systems (Lang Chain, Lang Graph, Auto Gen, foundation models)
• Infrastructure as Code and CI/CD proficiency; strong Python/scripting
• Experience in regulated biotech/pharma/life sciences with GxP or 21 CFR Part 11/data integrity
• Hands‑on vector databases/knowledge graphs and RAG architectures

Salary range: $159,000–$207,000; 401k, healthcare coverage, ESPP, and other benefits

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