Scale is looking for an AI Infrastructure Engineer to join our Machine Learning Infrastructure team to build out our Training Platform. You will partner closely with Machine Learning researchers to understand their requirements and apply your own domain expertise and our compute resources to accelerate experimentation throughput.
The ideal candidate is someone who has strong fundamentals in machine learning, backend system design, and has prior ML Infrastructure experience. You 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 training.
- 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.
- 4+ years of experience building machine learning training pipelines or inference services in a production setting.
- Experience with distributed training techniques such as DeepSpeed, FSDP, etc.
- Experience building, deploying, and monitoring complex microservice architectures.
- Experience with Python, Docker, Kubernetes, and Infrastructure as code (e.g. terraform).
- Experience with LLM inference latency optimization techniques, e.g. kernel fusion, quantization, dynamic batching, etc.
- Experience working with a cloud technology stack (eg. AWS or GCP).
Compensation packages at Scale for eligible roles 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. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. 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.
About Us:
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 organizations build and deploy AI. Our products power the world's most advanced LLMs, generative models, and computer vision models. We are trusted by generative AI companies such as OpenAI, Meta, and Microsoft, government agencies like the U.S. Army and U.S. Air Force, and enterprises including GM and Accenture. We 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 affirmative action employer and 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 accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information.
We comply with the United States Department of Labor's Pay Transparency provision.
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