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Conduct formal-methods research for software, hardware, and mathematical verification. Apply Lean or similar proof assistants to verify safety-critical systems, develop algorithms that improve AI-driven theorem proving, and collaborate with AI researchers to train reliable reasoning systems. The role requires technical research project ownership and may involve programming languages, safety-critical verification, open-source tools, and high-quality research contributions.

The Software Engineer will collaborate with AI researchers to implement AI algorithms and develop scalable software for practical applications, ensuring efficiency and reliability in software systems.

The Machine Learning Engineer will develop production-grade ML pipelines, optimize research code, and implement cloud infrastructure for ML workloads at Harmonic.

This role involves optimizing reinforcement learning systems for high performance by managing the entire stack, improving training throughput, and enhancing inference engine efficiency.

Use proof assistants and Aristotle to translate design intent into formal properties, execute proofs, diagnose failures, scope projects with customers, deliver reproducible workflows, and feed verification insights back to product and research. Travel to customer sites as needed.

The Software Engineer will work closely with AI researchers to develop scalable software integrating AI technologies, manage software lifecycle, and collaborate with cross-functional teams.

Design, build, and maintain full-stack features and high-performance backend infrastructure. Architect scalable APIs and data flows, integrate frontend and backend systems, collaborate with product, design, and research to ship user-facing products, and ensure reliability, observability, and iterative experimentation across the platform.