Senior Machine Learning Engineer, Public Sector

You will:

• Own the design and delivery of agent capabilities end to end - architecture, implementation, and the evaluation that proves they work
• Define net-new patterns in problem spaces with no established approach, propose them to the wider team, and lead the work to build them
• Take state of the art models developed internally and from the community and put them into production to solve problems for our customers and taskers
• Improve and maintain production models and agents through retraining, hyperparameter tuning, and architectural updates, while preserving core performance characteristics
• Build agent-level evaluation benchmarks, LLM judges, and verifiers - and use it to hillclimb performance rather than just report on it
• Partner with product and research teams to scope and shape high-impact initiatives, including for upcoming product lines
• Build scalable machine learning infrastructure to automate and optimize our ML services
• Work directly with government users and subject-matter experts, and translate what you learn into technical direction
• Act as a force multiplier and a primary reviewer for your team, mentoring at least one engineer, and your manager's go-to on feasibility questions
• Communicate technical tradeoffs clearly to non-technical stakeholders
• Treat security and compliance as design constraints to engineer around rather than blockers to route past
• 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

Ideally You'd Have:

• 5+ 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
• A track record of owning architectural decisions and defending the tradeoffs behind them - not just implementing a design handed to you
• Experience shipping agentic systems with real production traffic and evaluation rigor, rather than prototypes or demos
• Solid background in algorithms, data structures, and object-oriented programming
• Strong programming skills in Python, experience in PyTorch or Tensorflow
• Experience mentoring or reviewing the work of other engineers

Nice to Haves:

• Graduate degree in Computer Science, Machine Learning or Artificial Intelligence specialization
• Experience working with cloud platforms (eg. AWS or GCP) and deploying machine learning models in cloud environments
• Experience with computer vision, generative AI models, large language models, or agentic systems
• Familiarity with ML evaluation frameworks and agentic model design
• Experience deploying ML in classified, air-gapped, or IL5+ environments
• Geospatial or GEOINT experience
• Inference optimization experience
• Fine-tuning experience: SFT, RL, or embedding models