HPC / MLOps Infrastructure Engineer – Insight Softmax Consulting team

Together with our partner, Insight Softmax Consulting, we are looking for experiencedi nfrastructure engineer to work for the prominent client from the aerospace industry. Please note that this can be seen as a single position, or as two separate positions HPC Infrastructure Engineer and MLOps Engineer, depending on the candidate’s skillset and experience.

While the exact nature and purpose of this work is confidential, it is an extremely exciting set of projects and objectives that touch HPC, ML, Simulation, and Quantum swim lanes. Insight Softmax Consulting’s platform team is building Computational Fluid Dynamics (CFD) services and architecture that will integrate directly with ML and Simulation services. You will interface and work with some of the most experienced and talented people on the planet across these swim lanes.

Key responsibilities:

• The ideal candidate will have a strong background in building and maintaining high-performance computing services, expertise in distributed systems, and a passion for systems engineering, scaling, and data architecture. This role involves working closely with our development teams to design, implement, and optimize HPC and/or ML solutions that meet our growing needs.
• Design, build, and maintain high-performance computing (HPC) infrastructure.
• Develop and implement scalable distributed systems for complex computing tasks.
• Scaling services and systems from smaller deployments of ~50 nodes into larger ~250+ node clusters.
• Using orchestration, configuration management, virtualization, linux, CLI, deploy tools, monitoring, APM.
• Monitor HPC systems performance and implement improvements to ensure scalability and efficiency.
• Optimizing engineering and deliverables to balance feature development, product quality, service reliability.
• Design, build, and maintain scalable and reliable machine learning and data science services.
• Implement and manage data architectures, ensuring efficient data storage, processing, and retrieval mechanisms.
• Develop and optimize batch scheduling systems to manage workflows and data pipelines efficiently.
• Collaborate with data scientists, machine learning engineers, and software developers to integrate machine learning models into production environments seamlessly.
• Stay up-to-date with the latest technologies and trends in machine learning infrastructure, data management, and distributed systems.

Required Skills:

• At least 3-5 years of experience in HPC infrastructure, systems engineering, or a similar role.
• Proven experience in machine learning infrastructure, data management, and architecture.
• Strong understanding of batch scheduling, distributed systems, and systems engineering principles.
• Experience with scaling and operating machine learning models and data processing pipelines in production environments (eg: DVC, MLflow).
• HPC CFD experience.
• Experience with containerization (Docker) and container orchestration tools (e.g., Kubernetes).
• Strong understanding of systems engineering principles and scaling strategies.
• Deep knowledge of at least one of AWS, GCP, or Azure. Preferably AWS.
• Strong linux chops.
• Experience with data architecture and large-scale data processing.
• Building on-prem systems (on-premise, data center, rack-n-stack, etc).

Additional Information

• Significant overlap with US CT time (-6 to UTC) is expected.
• On-call rotations have not yet started in our business, but will likely begin sometime in 2024.
• Travel for up to 4 weeks per year to customer destinations.

What we offer