Machine Learning Engineer, Public Sector at Scale AI

Job description

The goal of a Machine Learning Engineer at Scale is to leverage techniques in the fields of generative AI, computer vision, reinforcement learning, and agentic AI to improve Scale's products and customer experience in production environments. Our machine learning engineers take advantage of robust internal infrastructure and unique access to massive datasets to deliver improvements to our customers. Our Public Sector Machine Learning team is focused on deploying cutting-edge models to mission-critical government systems through products like Donovan and Thunderforge . Our work spans multiple modalities, with a strong focus on both large language models and computer vision. On the LLM side, we are developing agentic systems that help solve complex operational and planning challenges for government partners. This includes building agent frameworks that integrate with custom retrieval pipelines and production APIs, as well as evaluation tools to benchmark and refine agent behavior. We're also advancing research in areas like reinforcement learning for agentic LLMs, with successful deployment into real-world operational environments. On the computer vision front, we're training advanced models to increase labeling throughput and automate perception tasks. Our efforts include building large-scale fine-tuning pipelines, training models across multiple modalities, and developing generalizable vision foundation models to support a wide range of defense applications. You will: Take state of the art models developed internally and from the community, use them in production to solve problems for our customers and taskers Improve and maintain production models through retraining, hyperparameter tuning, and architectural updates, while preserving core performance characteristics Collaborate with product and research teams to identify and prototype ML-driven product enhancements, including for upcoming product lines Work with massive datasets to develop both generic models as well as fine tune models for specific products Build scalable machine learning infrastructure to automate and optimize our ML services Serve as a cross-functional representative and advocate for machine learning techniques across engineering and product organizations Be comfortable learning new technologies quickly and managing multiple priorities in a fast-paced environment Comfortable with light travel (approximately 10%) for customer interaction and team needs This role will require an active TS security clearance Ideally You’d Have: 2+ years of experience building and deploying applied ML systems in production environments Extensive experience with GenAI, Agentic AI, natural language processing, deep learning and deep reinforcement learning, or computer vision in a production environment Solid background in algorithms, data structures, and object-oriented programming Strong programming skills in Python, experience in Tensorflow or PyTorch Nice to Haves: Experience deploying software into environments you can't reach from your laptop - on-prem, edge, air-gapped, or otherwise restricted networks. Regulated industries count; the constraint is the point, not the sector. Any prior exposure to government or defense work: military or civilian service, a cleared internship, or time at a federal contractor. Hands-on fine-tuning of open-weight models - LoRA/PEFT, instruction tuning, or training embedding models, at work or on your own. Having written evaluations for a system whose output isn't deterministic: benchmarks, LLM judges, or a regression suite that caught something real. Experience with geospatial data or maps - GIS tooling, spatial reference systems, or imagery. A shipped project with real users behind it, where you owned it after launch rather than handing it off at merge. 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 and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, 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. The base salary range for this full-time position in the location of Washington DC is: $235,200 — $294,000 USD PLEASE 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, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. 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 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.

Requirements

Must-haves

Take state of the art models developed internally and from the community, use them in production to solve problems for our customers and taskers
Improve and maintain production models through retraining, hyperparameter tuning, and architectural updates, while preserving core performance characteristics
Collaborate with product and research teams to identify and prototype ML-driven product enhancements, including for upcoming product lines
Work with massive datasets to develop both generic models as well as fine tune models for specific products
Build scalable machine learning infrastructure to automate and optimize our ML services
Serve as a cross-functional representative and advocate for machine learning techniques across engineering and product organizations
Be comfortable learning new technologies quickly and managing multiple priorities in a fast-paced environment
Comfortable with light travel (approximately 10%) for customer interaction and team needs
This role will require an active TS security clearance
2+ years of experience building and deploying applied ML systems in production environments
Extensive experience with GenAI, Agentic AI, natural language processing, deep learning and deep reinforcement learning, or computer vision in a production environment
Solid background in algorithms, data structures, and object-oriented programming
Strong programming skills in Python, experience in Tensorflow or PyTorch

Nice-to-haves

Experience deploying software into environments you can't reach from your laptop - on-prem, edge, air-gapped, or otherwise restricted networks. Regulated industries count; the constraint is the point, not the sector.
Any prior exposure to government or defense work: military or civilian service, a cleared internship, or time at a federal contractor.
Hands-on fine-tuning of open-weight models - LoRA/PEFT, instruction tuning, or training embedding models, at work or on your own.
Having written evaluations for a system whose output isn't deterministic: benchmarks, LLM judges, or a regression suite that caught something real.
Experience with geospatial data or maps - GIS tooling, spatial reference systems, or imagery.
A shipped project with real users behind it, where you owned it after launch rather than handing it off at merge.

Application prep

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Keywords to mirror

machine learningaillmmlpython

Platform: greenhouse
Effort: quick

Benefits

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