Harmonic
Software Engineer, ML Systems
About this role
Harmonic seeks a pragmatic Software Engineer to productionize ML research pipelines for their mathematical reasoning engine. You'll transform research ideas into scalable, robust systems deployed on cloud infrastructure, working closely with researchers to bridge algorithm and implementation.
What you'll do
- Build and manage end-to-end ML pipelines including ETL and automated evaluation systems
- Identify performance bottlenecks in research code and refactor for scalability
- Establish best practices for experiment tracking, versioning, and CI/CD for ML models
- Deploy and scale workloads on Kubernetes with observability tooling
- Design complex data-loading and evaluation systems for non-deterministic models
- Collaborate with researchers to translate algorithmic requirements into engineering solutions
What they're looking for
- Expert Python programming with disciplined software engineering practices
- End-to-end ML pipeline development
- Kubernetes and containerization (Docker)
- Cloud infrastructure management (AWS, GCP, or Azure)
- Workflow orchestration tools (Kubeflow, Airflow, Metaflow)
- Data engineering and model debugging
- Experiment tracking and monitoring systems
- Full-stack ML systems experience
Benefits
- Unlimited paid time off
- 401(k) matching
- 100% employer-paid health, vision, and dental for employees; 50% for dependents
- Health Savings Account (HSA) available
- Work on cutting-edge AI reasoning systems
- Collaborative environment with elite technical team
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Harmonic
Harmonic builds AI systems for mathematical reasoning and theorem proving, combining reinforcement learning with formal methods to achieve rigorous, verifiable problem-solving. The company is hiring Research Engineers to optimize its ML infrastructure and algorithms, Software Engineers to productionize research into scalable systems, and full-stack engineers to build user-facing products around these capabilities.
View all jobs at HarmonicLikely interview questions
- Walk us through how you've taken research-grade ML code and refactored it into production-ready, scalable systems. What were the biggest bottlenecks you identified and how did you resolve them?
- Describe your experience building end-to-end ML pipelines. How have you handled data versioning, experiment tracking, and reproducibility in a research environment?