Advanced Technology Senior Software Engineer

About this role:

We are seeking a High-Performance Computing (HPC) Engineer with experience in Machine Learning to optimize and scale AI/ML workloads. The ideal candidate will have experience with distributed training, model parallelization, GPU acceleration, and performance optimization across diverse hardware platforms. Experience or strong interest in Large Quantitative Models of High-Frequency Time Series is a strong advantage.

Learn more about the career areas and lines of business at wellsfargojobs.com

In this role, you will:

• Design, develop, and optimize HPC solutions for large-scale ML workloads.

• Optimize data pipelines for high-throughput model training (Dask, Ray, NVIDIA RAPIDS)

• Profile, optimize, and accelerate deep learning models on GPUs, TPUs, and multi-node clusters.

• Work on low-level performance tuning – vectorization, memory optimization.

• Develop and benchmark custom kernels for AI models using CUDA, ROCm, OpenACC, OpenMM.

• Implement distributed training strategies using MPI, DeepSpeed, PyTorch/XLA

• Collaborate with ML researchers and engineers to deploy scalable ML models.

• Research and implement new HPC techniques.

• Evaluate and adopt new technologies like Distributed Ledger or Blockchain

• Create new solutions to be deployed along existing enterprise software

• Work as part of team that follows the agile methodology

• Lead and mentor junior developers who are learning advanced technologies

• Lead or participate in complex initiatives on selected domains

• Assure quality, security and compliance for supported systems and applications

• Serve as a technical resource in finding software solutions

• Review and evaluate user needs and determine requirements

• Provide technical support, advice, and consultation with the issues relating to supported applications

• Create test data and conduct interfaces and unit tests

• Design, code, test, debug and document programs using Agile development practices

• Understand and participate to ensure compliance and risk management requirements for supported area are met and work with other stakeholders to implement key risk initiatives

• Conduct research and resolve problems in relation to processes and recommend solutions and process improvements

• Assist other individuals in advanced software development

• Collaborate and consult with peers, colleagues and managers to resolve issues and achieve goals.

• Design, develop, and optimize HPC solutions for large-scale ML workloads.

• Optimize data pipelines for high-throughput model training (Dask, Ray, NVIDIA RAPIDS)

• Profile, optimize, and accelerate deep learning models on GPUs, TPUs, and multi-node clusters.

• Work on low-level performance tuning – vectorization, memory optimization.

• Develop and benchmark custom kernels for AI models using CUDA, ROCm, OpenACC, OpenMM.

• Implement distributed training strategies using MPI, DeepSpeed, PyTorch/XLA

• Collaborate with ML researchers and engineers to deploy scalable ML models.

• Research and implement new HPC techniques.

Required Qualifications:

• 4+ years of Specialty Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

• 1 year experience in HPC & Parallel Computing: distributed computing frameworks, multi-threading, and vectorization techniques. Hands-on experience with GPU computing.

• 1 year experience optimizing ML workloads on NVIDIA, AMD, or custom AI Accelerators.

• 1 year experience in Machine Learning Optimization: Frameworks as PyTorch, TensorFlow, JAX. Model paralelization (pipe-line and tensor paralelism)

• 1 year Data Processing and I/O optimization experience : Large datasets processing with Parallel I/O.  Optimization of memory and data storage.

• 1 year experience with Cluster HPC,  HPC  schedulers and familiarity with cloud-based HPC (AWS Parallel Cluster, Azure ML, Google  Cloud TPUs.

Desired Qualifications:

• 1+ years of experience in HPC, ML optimization or/and infrastructure.

• Hands-on experience in deploying ML workloads on large-scale HPC clusters

• M.S./Ph.D. in Computer science or related field is a plus, Academic work (thresis, research articles, projects, etc.) in the areas of interest mentioned above count as work experience