NVIDIA

2 weeks ago

AI Infrastructure Software Engineer — CosmosLab

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 28 minutes agoStatus: Live

Job Summary

Join NVIDIA’s Cosmos Lab Infra team to design, assemble, and improve training infrastructure for Physical AI world foundation models. You’ll build pre-training, SFT, and RL post-training pipelines, plus the inference and evaluation stack for high throughput and low latency. Work across clusters with a control plane, integrate simulation/robotics environments, and optimize distributed training and reliability metrics across many GPUs.

Location: Beijing, Shanghai, Shenzhen

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Seniority: Mid level

Key Responsibilities

•Create and implement training infrastructure across pre-training, SFT, and RL post-training for Physical AI world foundation models, including a cluster control plane.
•Develop and scale pre-training and SFT pipelines with large-scale data loading, distributed training, and checkpointing for throughput and scalability.
•Build and improve the inference and evaluation stack, including inference/generation pipelines, continuous batching, and KV-cache management for low latency.
•Integrate and orchestrate simulation and robotics environments as RL environments to drive the simulation↔rollout↔training loop at scale.
•Improve distributed training backend efficiency, scalability, and resiliency, including sharding/parallelism, mixed precision, fault tolerance, and reliability/efficiency metrics.

Key Requirements

•5+ years developing software infrastructure for large-scale AI or distributed systems.
•Bachelor’s degree or higher in Computer Science or related technical field (or equivalent experience).
•Proficiency in Python (plus scripting) and strong software engineering practices (testing, defensive programming, version control, CI).
•Proven track record building and scaling large-scale distributed systems, ideally distributed training or inference.
•Hands-on experience with AI training and/or inference infrastructure, including RL/post-training, training frameworks, or inference serving.

Experience:5+ yearsAIDistributed systemsMachine learningReinforcement learning

Education:Bachelor's in Computer Science or a related technical field

Skills:DebuggingTriageCommunicationCollaborationProblem-solving

Tech Stack:PythonPyTorchFSDPDTensorMegatronCC++CUDARLPPOGRPODPOKV-cacheDistributed trainingMixed precisionActivation checkpointing

Company Brief

NVIDIA

Designs and manufactures GPUs, AI accelerators, and system-on-chip products for gaming, data centers, professional visualization, and automotive markets, enabling advanced graphics, AI, and high-performance computing solutions worldwide.

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor

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NVIDIA

2 weeks ago

AI Infrastructure Software Engineer — CosmosLab

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 28 minutes agoStatus: Live

Job Summary

Location: Beijing, Shanghai, Shenzhen

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Seniority: Mid level

Key Responsibilities

•Create and implement training infrastructure across pre-training, SFT, and RL post-training for Physical AI world foundation models, including a cluster control plane.
•Develop and scale pre-training and SFT pipelines with large-scale data loading, distributed training, and checkpointing for throughput and scalability.
•Build and improve the inference and evaluation stack, including inference/generation pipelines, continuous batching, and KV-cache management for low latency.
•Integrate and orchestrate simulation and robotics environments as RL environments to drive the simulation↔rollout↔training loop at scale.
•Improve distributed training backend efficiency, scalability, and resiliency, including sharding/parallelism, mixed precision, fault tolerance, and reliability/efficiency metrics.

Key Requirements

•5+ years developing software infrastructure for large-scale AI or distributed systems.
•Bachelor’s degree or higher in Computer Science or related technical field (or equivalent experience).
•Proficiency in Python (plus scripting) and strong software engineering practices (testing, defensive programming, version control, CI).
•Proven track record building and scaling large-scale distributed systems, ideally distributed training or inference.
•Hands-on experience with AI training and/or inference infrastructure, including RL/post-training, training frameworks, or inference serving.

Experience:5+ yearsAIDistributed systemsMachine learningReinforcement learning

Education:Bachelor's in Computer Science or a related technical field

Skills:DebuggingTriageCommunicationCollaborationProblem-solving

Tech Stack:PythonPyTorchFSDPDTensorMegatronCC++CUDARLPPOGRPODPOKV-cacheDistributed trainingMixed precisionActivation checkpointing

Company Brief

NVIDIA

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor