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Python Phd Hpc information

What is a Python PhD HPC specialist?

A Python PhD HPC specialist is a professional with a doctoral degree who uses the Python programming language to develop and optimize high-performance computing (HPC) applications. These specialists often work on complex scientific or engineering problems that require significant computational resources, such as simulations, data analysis, or machine learning at scale. They possess deep expertise in both Python programming and HPC environments, including parallel computing, distributed systems, and cluster management. Their work often bridges the gap between theoretical research and practical implementation on supercomputers or large compute clusters.

What are the typical challenges faced by a Python PhD HPC specialist when working on large-scale computational projects?

Professionals in a Python PhD HPC role often encounter challenges related to optimizing Python code for high-performance computing environments, particularly when scaling computations across multiple nodes or clusters. Managing memory usage, debugging parallel code, and ensuring compatibility with various HPC libraries and frameworks are common hurdles. Additionally, effective collaboration with interdisciplinary teams—such as domain scientists, system administrators, and data engineers—is crucial for project success. Staying updated with advancements in both Python and HPC technologies also requires ongoing learning and adaptability.

What are the key skills and qualifications needed to thrive as a Python PhD HPC specialist, and why are they important?

To excel as a Python PhD HPC specialist, you need advanced knowledge of Python programming, parallel computing concepts, and a doctoral degree in a computationally intensive field. Familiarity with HPC clusters, job schedulers like SLURM, and libraries such as NumPy, SciPy, and MPI for Python is typically required. Strong analytical thinking, problem-solving, and collaboration skills set candidates apart in this role. These competencies ensure efficient development, optimization, and deployment of large-scale computational models that drive scientific research and innovation.

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Infographic showing various Python Phd Hpc job openings in Missouri as of August 2026, with employment types broken down into 3% Internship, 93% Full Time, and 4% Contract. Highlights an 85% In-person, and 15% Remote job distribution.

Principal Machine Learning Engineer

California, MO • On-site

Jobtailor
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Posted 20 days ago

Job description

  • Architect and deploy autonomous agents that utilize tools, retrieve scientific evidence, and execute multi-step reasoning across drug discovery workflows
  • Design and implement advanced agent memory architectures and context management for long-horizon scientific tasks
  • Build reliable interfaces between agents and genomic, chemical, and clinical data sources
  • Design, build, and optimize large-scale distributed training and inference systems for foundation models
  • Own production Python/PyTorch codebases that turn research into enterprise-grade software
  • Establish best practices for experiment tracking, system observability and monitoring, evaluation harnesses, CI/CD, and infrastructure
  • Define the long-term engineering roadmap for AI4DD’s agentic and foundation models
  • Serve as a technical authority on ML infrastructure for Genentech leadership
  • Architect cross-functional platforms and elevate the engineering bar across gRED
  • Partner with ML Scientists and domain experts to translate scientific problems into scoped, shippable, and efficient systems
Requirements
  • BS, MS, or PhD in Computer Science, Machine Learning, Engineering, or a related quantitative field
  • PhD with 5+ years, MS with 8+ years, or BS with 10+ years of industry experience building, shipping, and owning large-scale ML systems and infrastructure end-to-end
  • Exceptional Python programming skills
  • Rigorous software engineering fundamentals, including Git, automated testing, CI/CD, documentation, and architecture design
  • Extensive hands-on experience with PyTorch and JAX
  • Experience deploying ML infrastructure on AWS or HPC environments, including distributed training tools
  • Practical experience designing agent orchestration frameworks, such as LangGraph or MCP-based tool integration
  • Experience managing persistent agent memory and building self-improving loops
  • Strong passion for applying frontier AI and agentic science to AI for Drug Discovery, biology, and chemistry
  • Preferred: deep expertise in LLM serving, test-time compute, sampling/search strategies, model routing, batching, caching, and latency/cost/quality tradeoffs
  • Preferred: experience with molecular modalities, including protein sequences, chemical graphs, and structured molecular data
  • Preferred: public portfolio of significant technical contributions to open-source ML, systems, or MLOps libraries
Core Competencies

Demonstrates expertise in architecting and deploying autonomous agents for drug discovery, with a strong foundation in Python and PyTorch. Capable of designing large-scale ML systems and infrastructure while collaborating with cross-functional teams to translate scientific challenges into effective solutions.

Highest-signal resume keywords
  • Python Programming
  • PyTorch
  • ML Infrastructure Deployment
  • Agent Orchestration Frameworks
  • Large-Scale ML Systems
Hard Skills
  • Machine Learning
  • Software Engineering Fundamentals
  • Automated Testing
  • CI/CD
  • Architecture Design
  • Agent Memory Management
  • Distributed Training
  • JAX
  • Experiment Tracking
  • System Observability
Soft Skills
  • Collaboration
  • Technical Authority
  • Problem-Solving
Industry Keywords
  • Drug Discovery
  • AI for Drug Discovery
  • Biology
  • Chemistry
  • Molecular Modalities
Tools & Technologies
  • AWS
  • HPC Environments
  • LangGraph
  • MCP-based Tool Integration
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