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.
- 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.
At Scale, we believe that the transition from traditional software to AI is one of the most important shifts of our time. Our mission is to make that happen faster across every industry, and our team is transforming how machine learning can build innovative products. Our products provide access to human-powered data for hundreds of use cases and are used by industry leaders such as Open AI, Lyft, Meta, GM, Samsung, Airbnb, NVIDIA, and many more. We’ve recently raised $325 million in Series E funding at a valuation of $7B+ and are expanding our team to accelerate the development of AI applications.
We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.
We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at [email protected]. Please see the United States Department of Labor's EEO poster and EEO poster supplement for additional information.
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