Software Engineer, ML Infrastructure - Evaluation Platform at Scale AI
New York, NY 10001
About the Job
As a software engineer on the ML Infrastructure team, you will work on developing the platform for orchestrating post-training and model evaluation jobs
At Scale, we are constantly developing new data sources and running experiments to understand their impact on ML models
To support this effort, we are looking for engineers who are comfortable navigating cloud infrastructure challenges as well as research challenges in benchmarking and tuning LLMs.The ideal candidate is someone who has strong fundamentals in machine learning, backend system design, and has prior ML Infrastructure experience
They should also be comfortable with infrastructure and large scale system design, as well as diagnosing both model performance and system failures.You will:Develop re-usable platforms for running in-house and open-source LLM-benchmarks.Ensure correctness and performance of post-training and eval jobs on the platform.Improve APIs for managing ML workflows.Contribute to foundational infrastructure at the company for model inference and 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.Ideally you'd have:4+ years of experience developing ML platforms.Passion for working closely with researchers to drive business impact.Experience training and/or benchmarking LLMs.Experience with Python, Docker, Kubernetes, and Infrastructure as code (e.g
terraform).Nice to haves:Experience building, deploying, and monitoring complex microservice architectures.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.Please reference the job posting's subtitle for where this position will be located
For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:$175,000—$220,000 USDPLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role
This allows us to ensure a fair and thorough evaluation of all applicants.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
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. PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates
We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws
Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data
Please see our privacy policy for additional information.
At Scale, we are constantly developing new data sources and running experiments to understand their impact on ML models
To support this effort, we are looking for engineers who are comfortable navigating cloud infrastructure challenges as well as research challenges in benchmarking and tuning LLMs.The ideal candidate is someone who has strong fundamentals in machine learning, backend system design, and has prior ML Infrastructure experience
They should also be comfortable with infrastructure and large scale system design, as well as diagnosing both model performance and system failures.You will:Develop re-usable platforms for running in-house and open-source LLM-benchmarks.Ensure correctness and performance of post-training and eval jobs on the platform.Improve APIs for managing ML workflows.Contribute to foundational infrastructure at the company for model inference and 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.Ideally you'd have:4+ years of experience developing ML platforms.Passion for working closely with researchers to drive business impact.Experience training and/or benchmarking LLMs.Experience with Python, Docker, Kubernetes, and Infrastructure as code (e.g
terraform).Nice to haves:Experience building, deploying, and monitoring complex microservice architectures.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.Please reference the job posting's subtitle for where this position will be located
For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:$175,000—$220,000 USDPLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role
This allows us to ensure a fair and thorough evaluation of all applicants.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. PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates
We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws
Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data
Please see our privacy policy for additional information.
