Sr. Engineering Manager, AI Runtime

United States Digital Space LLC; Mountain View (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

United States Digital Space LLC is seeking a Senior Engineering Manager to lead the team responsible for the AIR product's training infrastructure and customer-facing experience. You will shape the architecture for scalable GPU training, drive the roadmap, and collaborate with product, research, platform, and customers to deliver end-to-end solutions.

You will mentor a high-performing team, implement reliability and observability practices for long-running multi-node jobs, and partner with

Qualifications

• 8+ years of software engineering experience, with 3+ years in engineering management.
• Experience building and operating managed GPU training infrastructure at scale (hundreds to thousands of GPUs).
• Deep familiarity with distributed training frameworks (PyTorch, DeepSpeed, Megatron-LM) and parallelism strategies (FSDP, tensor/pipeline).
• Experience with training resilience: checkpointing, elastic training, failure recovery for long-running jobs.
• Understanding of GPU performance fundamentals (NCCL, interconnects, memory optimization).

Responsibilities

• Lead, mentor, and grow a high-performing engineering team responsible for the Custom Training product and its foundational infrastructure, including distributed training orchestration, cluster lifecycle, fault tolerance, and training efficiency.
• Define and own the product and technical roadmap for AIR, balancing customer experience, functionality, and foundational investments.
• Collaborate with product, research, platform, infrastructure teams, and customers to drive end-to-end delivery, from ideation and prioritization to launch and operation.
• Drive architectural decisions and product design for managed GPU training at scale.
• Advocate for customer needs through direct engagement, ensuring engineering decisions translate to clear product impact.
• Build observability and reliability practices for long-running, multi-node training jobs, including checkpoint strategies, failure recovery, and operational runbooks.
• Partner with recruiting to attract, hire, and develop top-tier engineering talent.

Skills

Leadership

Distributed training

PyTorch

DeepSpeed

Megatron-LM

NCCL

GPU performance

Education

BS/MS in CS/EE

Tools

Cluster orchestration

NVIDIA GPUs

Tensor parallelism

Job description

Overview

At the company, we are passionate about enabling data teams to solve the world's toughest problems, from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business.

the company' AI Runtime (AIR) product provides enterprises with an API for training and fine-tuning deep learning and LLM models with on-demand GPUs. Whether it's a transformer model for drug discovery or a fine-tuned foundation model, customers use this team's training infrastructure to build state-of-the-art frontier models.

As a Senior Engineering Manager, you will lead the team owning both the product experience and the foundational infrastructure of AIR. You\'ll shape customer-facing capabilities while designing for scalability, extensibility, and performance of GPU training and adjacent areas, collaborating closely across the platform, product, infrastructure, and research organizations.

Responsibilities

• Lead, mentor, and grow a high-performing engineering team responsible for the Custom Training product and its foundational infrastructure, including distributed training orchestration, cluster lifecycle, fault tolerance, and training efficiency.
• Define and own the product and technical roadmap for AIR, balancing customer experience, functionality, and foundational investments.
• Collaborate closely with product, research, platform, infrastructure teams, and customers to drive end-to-end delivery, from ideation and prioritization to launch and operation.
• Drive architectural decisions and product design for managed GPU training at scale.
• Advocate for customer needs through direct engagement, ensuring engineering decisions translate to clear product impact.
• Build observability and reliability practices for long-running, multi-node training jobs, including checkpoint strategies, failure recovery, and operational runbooks.
• Partner with recruiting to attract, hire, and develop top-tier engineering talent.

Qualifications

• 8+ years of software engineering experience, with 3+ years in engineering management.
• Track record building and operating managed GPU training infrastructure at scale (100s/1000s GPUs).
• Deep familiarity with distributed training frameworks (PyTorch, DeepSpeed, Composer, Megatron-LM) and parallelism strategies (FSDP, tensor/pipeline parallelism).
• Experience with training resilience patterns: checkpointing, elastic training, and automated failure recovery for long-running jobs.
• Understanding of GPU performance fundamentals including NCCL, interconnect topologies, and memory optimization.
• Experience building platform products with clear SLAs where you\'ve owned the customer experience, not just the backend.
• Strong cross-functional leadership across platform, product, and research teams, with the ability to lead through ambiguity and deliver complex projects.
• Excellent collaboration and communication skills across engineering, product, and research organizations.
• BS/MS in Computer Science, Electrical Engineering, or related technical field.