Senior Machine Learning Engineer, Public Sector
About this role
Engineers who thrive on tackling unsolved technical problems and building resilient AI systems for complex environments will find a natural fit within the Public Sector Machine Learning team at Scale AI. The group builds advanced agent frameworks, retrieval pipelines, and geospatial reasoning tools deployed to mission-critical government systems like Donovan and Thunderforge. Engineers here own the design and delivery of end-to-end agent capabilities, taking state-of-the-art models into production under strict constraints such as classified settings and limited compute.
Day-to-day responsibilities involve setting architectural patterns, optimizing production models through retraining and hyperparameter tuning, and building rigorous evaluation benchmarks. The team functions in a fast-paced environment where members act as force multipliers, mentoring peers, and translating direct user feedback from government subject matter experts into technical direction. Success requires deep technical ownership, clear communication of architectural tradeoffs, and the ability to treat security and compliance as core design constraints.
What you will do
- Own the complete design and delivery of agent capabilities, including architecture, implementation, and performance evaluation.
- Define new engineering patterns for ambiguous problem spaces and lead the team in building them.
- Deploy internal and community models into production to solve customer problems while preserving core performance.
- Maintain and improve production models and agents through retraining, architectural updates, and hyperparameter tuning.
- Build agent-level evaluation benchmarks, LLM judges, and verifiers to actively improve system performance.
- Collaborate directly with government users and subject matter experts to translate insights into technical direction.
- Act as a primary reviewer and mentor for other engineers while communicating technical tradeoffs to non-technical stakeholders.
What you need
- Have at least 5 years of experience building and deploying applied machine learning systems in production.
- Demonstrate extensive background in generative AI, agentic AI, natural language processing, deep learning, deep reinforcement learning, or computer vision.
- Possess a proven track record of owning architectural decisions and defending associated tradeoffs.
- Show hands-on experience shipping agentic systems with real production traffic and evaluation rigor.
- Bring a solid background in algorithms, data structures, and object-oriented programming.
- Demonstrate strong programming skills in Python alongside experience in PyTorch or TensorFlow.
Nice to have
- Graduate degree in Computer Science, Machine Learning, or Artificial Intelligence specialization
- Experience working with cloud platforms like AWS or GCP for deploying machine learning models
- Familiarity with ML evaluation frameworks, agentic model design, and inference optimization
- Experience deploying ML in classified, air-gapped, or IL5+ environments
- Geospatial or GEOINT experience, alongside fine-tuning experience with SFT, RL, or embedding models
Benefits
- Comprehensive health, dental, and vision coverage
- Retirement benefits
- Learning and development stipend
- Generous paid time off
- Commuter stipend
- Equity-based compensation subject to Board of Director approval
Skills and tech stack
Questions candidates ask
What is the compensation range for this position?
The base salary range for this full-time position in Washington DC is $235,200 to $294,000 USD. Eligible roles also include equity-based compensation and benefits.
What security clearance is required for this role?
This role requires an active TS security clearance. Candidates must be comfortable working within classified environments.
Is travel required for this position?
The role requires light travel of approximately 10% for customer interaction and team needs. Work locations include Denver, Honolulu, and Washington, DC.
What is the policy for reapplying if I am not selected?
The company requires a 90-day waiting period before reconsidering candidates for the same role. This ensures a fair and thorough evaluation of all applicants.