Machine Learning Engineer, Public Sector

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.

• 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 Py Torch

• 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…