Senior Machine Learning Engineer, Public Sector

Responsibilities: Own design and end-to-end delivery of agent capabilities (architecture, implementation, evaluation) Define and lead new problem-space patterns with no established approach Deploy state-of-the-art models into production to solve customer problems Improve production models through retraining, tuning, and architectural updates while preserving performance Build agent evaluation benchmarks, judges, and verifiers to drive performance improvements Collaborate with product and research to scope high-impact initiatives and new product lines Develop scalable ML infrastructure to automate and optimize ML services Engage with government users and subject-matter experts to extract technical direction Mentor at least one engineer and serve as a go-to for feasibility questions Communicate trade-offs clearly to non-technical stakeholders Treat security and compliance as design constraints Act as cross-functional ML advocate across engineering and product teams Learn rapidly and manage multiple priorities in a fast-paced setting Potential light travel (~10%) for customer interaction and team needs

Key requirements: 5+ years of experience in building and deploying applied ML systems in production Extensive experience with GenAI, Agentic AI, NLP, deep learning, deep RL, or CV in production Track record of owning architectural decisions and defending trade-offs Experience shipping agentic systems with production traffic and rigorous evaluation Strong programming skills in Python; experience with PyTorch or TensorFlow Experience mentoring or reviewing work of other engineers clear communication of technical tradeoffs to non-technical stakeholders leadership and mentorship cross-functional collaboration GenAI and agentic AI NLP and LLMs reinforcement learning