Sr. Research Engineer​/Scientist Efficient Models San Jose Regular

Sr. Research Engineer/Scientist(all levels), Efficient Models

Location:

San Jose

Team:
Technology

Employment Type:

Regular

Job Code: A116738A

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About the Team:
The Vision‑Applied Research team focuses on applied research in Generative AI and CV/Multimodal Understanding, and delivering intelligent solutions to Byte Dance products, enabling users to make and share creative content in a much easier way. The team has research groups dedicated to generative models for content creation, image generation, video synthesis, intelligent image/video editing, and virtual humans.

The team is looking for a Research Engineer / Scientist who can take initiatives in designing and implementing efficient models for large‑scale generative AI, with a particular emphasis on large model distillation and compression. The candidate will work on developing methods and infrastructure for transferring capabilities from foundation models into smaller, more efficient models, enabling scalable training, optimization, and deployment.

Responsibilities may include, but are not limited to, distillation frameworks, model acceleration, hardware‑efficient inference, and their applications.

• Develop efficient algorithms and architectures for large‑scale generative and multimodal models, using techniques such as step distillation, cfg distillation, quantization, and other methods to improve model efficiency (e.g., image generation, video generation, VLM).
• Advance scalable generative modelling approaches, including diffusion and autoregressive models, with a focus on acceleration and efficiency.

Minimum Qualifications:

• B.S. in Computer Science or related fields, or equivalent experience
• Expertise in efficient models with deep understanding of computational bottlenecks and acceleration methods
• Proficiency in training generative AI or LLM models using widely adopted frameworks and tools such as PyTorch and JAX
• Strong communication and collaboration skills in fast‑paced environments

Preferred Qualifications:

• Ph.D. in GenAI, MLSys or equivalent experience
• Extensive research experiences in broad GenAI, MLSys, LLM areas
• Proven experiences in at least one of the following areas: image/video generation and editing; model compression (e.g., quantization, step/cfg distillation); efficient architectures (e.g., MoE, window attention); efficient model design; or reinforcement learning training methods (e.g., RLHF, DPO, GRPO)

The base salary range for this position in the selected city is $208800 - $616000 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.

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…