Research Engineer - LLM​/VLM Inference Optimization (Seed Infra)

Research Engineer - LLM/VLM Inference Optimization (Seed Infra)

Location:

Seattle

Team:
Technology

Employment Type:

Regular

Job Code: A236224

About the Team The Seed Infrastructures team oversees the distributed training, reinforcement learning framework, high-performance inference, and heterogeneous hardware compilation technologies for AI foundation models.

1. Design, develop, and optimize high-performance inference systems for large-scale LLMs and VLMs, covering inference engines, serving frameworks, and end-to-end deployment pipelines.

2. Build state-of-the-art model inference engines through advanced performance optimization techniques such as compiler-level optimizations, parallel computing, graph fusion, efficient CUDA kernel development, low-precision computation, streaming inference, and high-concurrency request optimization.

3. Collaborate closely with other research teams to identify performance bottlenecks, conduct in-depth performance analysis, and optimize large models; contribute to the development of model tool chains and the broader technical ecosystem.

Minimum Qualifications:

1. Bachelor's degree or above in Computer Science, Electrical Engineering, Software Engineering, or a related field.

2. Strong proficiency in C/C++ and Python; solid foundations in algorithms, data structures, and systems programming; familiarity with containerization and server-side debugging.

3. Hands-on experience with at least one mainstream machine learning framework (e.g., PyTorch, Tensor Flow).

4. Experience deploying or optimizing LLM/VLM inference at production scale, with demonstrated impact on latency, throughput, or serving cost.

5. Familiarity with GPU architecture and experience optimizing compute-intensive operators (e.g., Flash Attention, GEMM, GEMV, Conv2D).

Preferred Qualifications:

1. Experience with large-scale LLM serving infrastructure or equivalent production LLM deployment experience.

2. Experience in GPU programming (CUDA/OpenCL) and familiarity with frameworks such as TensorRT, Triton, or CUTLASS.

3. Experience in performance modeling, profiling, and optimization, or strong knowledge of CPU/GPU architectures.

4. Familiarity with model/data parallelism frameworks for distributed inference.

Compensation Description (Annually):
The base salary range for this position in the selected city is $232560 - $427500 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.…