Software Development Engineer - Language Models, AML

Software Development Engineer - Large Language Models, AML

Location:

San Jose

Team:

Technology

Employment Type:

Regular

Job Code:

A210012

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The Applied Machine Learning Enterprise team combines system engineering and machine learning to develop and operate a Large Language Model (LLM) service platform that offers businesses Model-as-a-Service solutions (MaaS) to both the big model vendors and users. The Applied Machine Learning Enterprise US team drives the design, development, and operation of MaaS solutions in the US and overseas of mainland China.

We are building full‑stack end‑to‑end solutions covering text and multi‑modality LLM algorithms, LLM training/fine tuning/inference frameworks, heterogeneous hardware architectures, prompting engineering, LLM model alignment, and intelligent agents, etc. We are actively seeking talented Software Development Engineers specializing in LLMs to join our dynamic team. In this role, you will be at the forefront of development and implementation of cutting‑edge techniques for LLM solutions including model fine‑tuning, evaluation, and inference capabilities.

To ensure we have the ability to run current and future models at increasingly high scale with increasing efficiency, your primary responsibilities will be to:

• Develop high‑efficient and high‑capacity LLM platform, build innovative products atop it.
• Work closely with cross‑functional teams to implement projects harnessing LLMs for diverse purposes and vertical domains.
• Maintain a deep passion for contributing to the success of LLMs, essential in this innovative and fast‑paced team environment.

Minimum Qualifications:

• Ph.D./Master in Computer Science/Computer Engineering, Artificial Intelligence, or a related field. (Undergraduate with exceptional experience can be considered.)
• Have prior experience working with machine learning platforms and/or training and inference of LLMs.
• Strong understanding of cutting‑edge LLM techniques (e.g., attention variants, multi‑modality, prompt engineering, alignment, agent, etc.) and possess practical expertise in effectively implementing these systems.
• Proficiency in programming languages such as Python, Go, C++, or Rust and a track record of working with deep learning frameworks (e.g., PyTorch, Deep Speed, Megatron, etc.).
• Strong understanding of distributed computing framework & performance tuning and verification for training/fine tuning/inference. Familiarity with Ray, PEFT, or RL is a plus.

Preferred Qualifications:

• Excellent problem‑solving skills to address complex engineering problems and AI challenges. Demonstrated ability to build systems from ideas, producing tangible outcomes.
• Experience with inference tuning and inference acceleration. Deep understanding of GPU and/or other AI accelerators, experience with large‑scale AI networks, PyTorch 2.0 and similar technologies.
• Experience with large scale machine learning systems’ scheduling and orchestration, familiar with Kubernetes and Docker.
• Experience with deploying AI models into production environments, testing and evaluation of AI systems, LLM application & agent development is desirable.

The base salary range for this position in the selected city is $136,800 – $359,720 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…