Machine Learning Engineer, LLM Infrastructure at ScaleAI

Scale is looking for a Machine Learning Engineer to join our Machine Learning Infrastructure team to help build systems that accelerate the development and deployment of machine learning models, especially large language models (LLMs). You will partner closely with Machine Learning researchers and internal users to understand requirements and apply your own domain expertise to build high performance and reusable APIs.

The ideal candidate is someone who has strong ML fundamentals and can also apply them in real production settings. In particular, this role has a core focus on optimizing inference and fine tuning for LLMs. They should also be comfortable with infrastructure and large scale system design, as well as diagnosing both model performance and system failures.

You will:

• Build highly available, observable, performant, and cost-effective APIs for model inference and fine tuning for LLMs.
• Engage with ML researchers and stay up to date on the latest trends from industry and academia.
• Participate in our team’s on call process to ensure the availability of our services.
• Own projects end-to-end, from requirements, scoping, design, to implementation, in a highly collaborative and cross-functional environment.
• Exercise good taste in building systems and tools and know when to make build vs. buy tradeoffs, with an eye for cost efficiency.

Ideally you'd have:

• 2+ years of experience building machine learning training pipelines or inference services in a production setting.
• Experience with LLM deployment, fine tuning, training, prompt engineering, etc.
• Experience with LLM inference latency optimization techniques, e.g. kernel fusion, quantization, dynamic batching, etc.
• Experience with CUDA, model compilers, and other model-specific optimizations.

Nice to haves:

• Experience working with a cloud technology stack (eg. AWS or GCP).
• Experience building, deploying, and monitoring complex microservice architectures.
• Experience with Python, Docker, Kubernetes, and Infrastructure as code (e.g. terraform).

The base salary range for this full-time position in San Francisco is $212,800 - $255,360. Compensation packages at Scale include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Scale employees are also granted Stock Options that are awarded upon board of director approval. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.