AI Infra Engineer - Model Training Infrastructure; LLM​/VLM​/Agent RL

Location:

San Jose

Employment Type:

Regular

Job Code: A189395

About the Team:
We are dedicated to building the training infrastructure for ultra-large-scale language models, vision-language models, and frontier agentic models. Our mission is to provide a robust, scalable, and high-performance foundation for post-training, multimodal learning, and reinforcement learning at the hundred-billion-parameter scale and beyond. You will work on some of the most challenging problems in large-model training systems, from multimodal data efficiency to convergence optimization for next-generation foundation models.

• Build and evolve unified training infrastructure for large models across post-training workflows, modalities, and training paradigms.
• Design and optimize distributed training strategies for 100B to 1T parameter models, including DP, TP, PP, EP, operator fusion, memory optimization, and cluster-level MFU improvement.
• Develop training and evaluation systems for Reasoning RL and Agent RL, including benchmarks, harnesses, convergence optimization, and rollout efficiency.
• Enable multimodal training across image, text, audio, and video, and support emerging architectures such as MoE and Linear Attention with correctness and convergence validation.

• Minimum Qualifications:
  • Bachelor's degree or above in Computer Science, Software Engineering, Artificial Intelligence, Mathematics, or related fields.
  • 2+ years of experience in large-scale ML systems, training infrastructure, or performance optimization.
  • Strong programming skills in Python and C++.
  • Strong understanding of PyTorch and distributed training frameworks such as Deep Speed, Megatron, and FSDP.
  • Experience with distributed training for ultra-large models and strong debugging skills in convergence and system bottlenecks.
• Preferred Qualifications:
  • Experience with PPO, GRPO, or Agent RL.
  • Experience building large-model evaluation systems, agentic harnesses, or benchmarking infrastructure.
  • Familiarity with multimodal training, post-training systems, MoE, or Linear Attention.
  • Experience with training optimization for 100B+ parameter models is a plus.

Job Information

The base salary range for this position in the selected city is $156000 - $387600 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

For Los Angeles County (unincorporated) Candidates:

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;

2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems;

3. Exercising sound judgment.