Responsibilities

About the Team We are dedicated to building the inference infrastructure for ultra-large-scale language models, vision-language models, and frontier multimodal AI systems. Our mission is to provide a robust, scalable, and high-performance foundation for distributed serving, heterogeneous scheduling, and low-latency inference at massive scale. You will work on some of the most challenging problems in large-model online serving, spanning traffic orchestration, throughput and latency optimization, kernel efficiency, and production reliability for next-generation AI systems. Responsibilities - What You'II Do - Build and evolve next-generation inference systems for large-scale online traffic, including global scheduling across heterogeneous compute resources, high-concurrency load balancing, and efficient batch formation - Optimize distributed inference for 200B+ models and complex multimodal models through TP, EP, DP, and related strategies to improve throughput and latency in production - Develop high-performance kernels for frontier model architectures such as MoE, emerging attention mechanisms, and multimodal fusion layers using CUDA, Triton, and related tools - Explore AI-driven infrastructure for inference systems, including AI Agents for kernel optimization, performance tuning, consistency validation, deployment pipelines, and intelligent operations

Qualifications

Minimum Qualifications: - Bachelor's degree or above in Computer Science, Software Engineering, Artificial Intelligence, Mathematics, or related fields - 2+ years of experience in high-performance computing, distributed scheduling systems, or large-model inference engine development - Familiarity with large-model architectures and strong system design skills for complex, high-concurrency environments - Strong understanding of asynchronous scheduling, resource pooling, and load balancing in distributed microservice systems - Strong engineering skills in performance optimization and production system development Preferred Qualifications - Deep understanding of inference frameworks such as vLLM and SGLang, with hands-on experience in customization and production optimization - Familiarity with GPU microarchitecture and operator-level optimization using CUDA, Triton, Cutlass, or related tools - Experience with LLM inference optimization, such as PTQ, QAT, KV cache optimization, or PD disaggregation - Experience deploying and optimizing VLMs or multimodal models in production

Job Information

[For Pay Transparency]Compensation Description (Annually)

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).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

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; and

3. Exercising sound judgment.