Backend Inference Runtime Engineer Graduate (AML Inference) - 2027 Start

ByteDance; San Jose (CA); On-site; USD 128,000 - 256,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k) with company match

Paid parental leave

Disability coverage

Life insurance

Wellbeing benefits

10 paid holidays per year

10 paid sick days per year

Paid Personal Time

Job summary

ByteDance in San Jose seeks a Senior GPU Inference Engineer to drive the iteration of large model inference engines and optimize GPU memory, latency, and throughput across multi-card systems.

You will design distributed parallel solutions (TP/PP/sequence/MoE) and push performance across vLLM, TensorRT-LLM, and related frameworks while collaborating with cross-functional teams to scale cutting-edge AI capabilities for internal products.

Qualifications

• Bachelor's/Master's degree in Software Development, CS, or related technical discipline.
• Solid foundation in computer low-level knowledge; proficient in C/C++ and Python.
• Proficient in CUDA programming; familiar with GPU memory models and scheduling.
• Experience with deep learning operators, graph optimization, and memory optimization.
• Experience using GPU performance tools like Nsight/Profiler and profiling for bottlenecks.
• Familiar with large model inference frameworks and multi-card parallelism concepts.

Responsibilities

• Iterate architecture of large model inference engine and optimize GPU latency and throughput.
• Adapt to GPU/NPU hardware architectures and ensure high performance across devices.
• Lead design and optimization of distributed parallel solutions (TP/PP/sequence/MoE).
• Stay updated on global large model inference tech and benchmark against frameworks like vLLM and TensorRT-LLM.

Skills

C/C++

Python

CUDA

GPU hardware

Performance analysis

Education

Bachelor's/Master's degree in CS/Engineering

Tools

Nsight

Profiler

TensorRT-LLM

Job description

Responsibilities

Data AML is ByteDance's Machine Learning mid-platform, providing training and inference systems for recommendation/advertising for businesses such as Douyin, Jinri Toutiao, and Xigua Video. It provides powerful Machine Learning computing power for internal business units within the company and conducts research on some general and innovative algorithms for issues in these businesses.

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.

Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.

Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

Job Description

• Responsible for the iteration of the underlying architecture of the large model inference engine and end-to-end GPU performance optimization, through means such as operator fusion and compilation optimization, deeply optimizing GPU memory access, computing pipeline, and Stream asynchronous scheduling, eliminating inference computing bottlenecks, improving single-card inference throughput, and reducing inference latency.
• Adapt to all series of GPU/NPU hardware architectures, refine the universality of the inference engine and hardware adaptability, and build a high-performance, low-loss underlying base for large model inference.
• Lead the design, development, and optimization of distributed parallel solutions for large model inference scenarios, with a focus on implementing multi-dimensional parallel strategies such as tensor parallelism (TP), pipeline parallelism (PP), sequence parallelism, and MoE expert parallelism, to address core issues such as multi-card splitting and deployment of ultra-large models, high cross-card communication overhead, load imbalance, and low parallel efficiency.
• Follow up on cutting-edge technologies such as global large model inference, GPU high-performance computing, distributed parallelism, and cache optimization, benchmark against mainstream inference frameworks such as vLLM and TensorRT-LLM, complete the implementation of solutions and technological innovation, continuously iterate and optimize the performance and cost advantages of the inference system, and build the core technological barriers of the team.

Qualifications

Minimum Qualifications:

• Individuals who are completing or have recently completed a Bachelor's/ Master\'s degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline, or a related discipline.
• Solid foundation in computer low-level knowledge, proficient in C/C++ and Python programming, skilled in CUDA programming and familiar with GPU hardware architecture principles, and well-versed in GPU memory models, computing scheduling, and communication mechanisms;
• Proficiently master the underlying development and implementation of various basic operators in Deep learning, be well-versed in GPU adaptation and optimization of core operators such as matrix operations, normalization, and activation functions, and be able to independently complete operator handwritten reconstruction, memory access optimization, vectorization acceleration, and precision alignment to ensure high performance and high stability of operator inference.
• Familiar with the end-to-end process of deep learning inference compilation, understand core compilation technologies such as computational graph optimization, operator fusion, constant folding, memory reuse, scheduling optimization, and quantization compilation, and be able to simplify the inference process, reduce GPU memory usage, and decrease inference latency through compilation-level improvements, thereby significantly enhancing the throughput efficiency of model inference.
• Proficient in using GPU performance analysis tools such as Nsight and Profiler, able to accurately identify performance bottlenecks such as computing power waste, memory access blockage, and scheduling redundancy during the inference process, possess the thinking of software-hardware collaborative optimization, capable of outputting systematic optimization solutions and completing implementation iterations, and adaptable to the requirements of industrial-level high-concurrency, low-latency inference business.
• Possess good cross-team collaboration skills, communication and presentation skills, and document writing skills, have strong sense of responsibility and stress tolerance, and be able to drive the resolution of complex technical issues and the implementation of projects;

Preferred Qualifications

• Thoroughly understand the core principles of large model inference, proficiently master the core technologies of model parallelism, have experience in implementing distributed inference solutions such as tensor parallelism, pipeline parallelism, and sequence parallelism, and be familiar with multi-card communication, load balance, and parallel efficiency optimization methods.
• Those with experience in secondary development and Performance optimization of mainstream large model inference frameworks such as vLLM, SGLang, TensorRT-LLM, etc. are preferred.

Job Information

【For Pay Transparency】Compensation Description (Annually)
The base salary range for this position in the selected city is $128000 - $256000 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:

• Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
• Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
• Exercising sound judgment.

About Us

Founded in 2012, ByteDance's mission is to inspire creativity and enrich life. With a suite of more than a dozen products, including TikTok, Lemon8, CapCut and Pico as well as platforms specific to the China market, including Toutiao, Douyin, and Xigua, ByteDance has made it easier and more fun for people to connect with, consume, and create content.

Why Join ByteDance

Inspiring creativity is at the core of ByteDance's mission. Our innovative products are built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and enrich life - a mission we work towards every day.

As ByteDancers, we strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our Company, and our users. When we create and grow together, the possibilities are limitless. Join us.

Diversity & Inclusion

ByteDance is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At ByteDance, our mission is to inspire creativity and enrich life. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.

Reasonable Accommodation

ByteDance is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at https://tinyurl.com/RA-request