Software Engineer, ML Systems

About the role

ABOUT HARMONIC

At Harmonic, we believe the future of AI depends on proof. Most AI makes its best guess. Aristotle uses formal methods and reinforcement learning to check its own reasoning and results.

AI agents can write software in seconds, but edge cases and silent bugs still get through. We're building the verification engine to find them before code ships. With support for C and Verilog, Aristotle is built for the hardware and software where failure is expensive.

Backed by some of the world’s most prominent investors, we are intentionally scaling an elite technical team.

Try Aristotle for yourself here https://aristotle.harmonic.fun/!

ABOUT THE ROLE

We are looking for a pragmatic, Software Engineer to own the productionization of our research pipelines. This is an implementation-heavy role designed for an engineer who can take a nascent research idea and build the robust, scalable machinery required to prove it at scale within our cloud infrastructure.

KEY RESPONSIBILITIES

- Pipeline Engineering: Build and manage end-to-end ML pipelines (ETL and automated evaluation) that are the bedrock of our RL research.

- Bottleneck Resolution: Identify and refactor inefficient research code. You act as the primary engineer ensuring that a promising idea reaches its full potential through scalable code.

- Standardization: Establish best practices for versioning, experiment tracking, and CI/CD for ML models to ensure reliability.

- Cloud Infrastructure & Observability: Manage the deployment and scaling of workloads on Kubernetes. Implement the tooling and telemetry that allows the team to understand agent behavior and training health at a glance.

MINIMUM QUALIFICATIONS

- BS in Computer Science, a related technical field, or equivalent industry experience

- 2+ years of relevant industry experience

- Expert-level Python skills and a disciplined approach to software engineering (testing, versioning, and modular design).

- Experience building and managing end-to-end ML pipelines in a production or research-intensive environment.

PREFERRED QUALIFICATIONS

- Full-stack ML experience: Comfortable moving from data engineering to model debugging.

- Experience refactoring research-grade code into high-quality, scalable production packages.

- Proven ability to design and implement complex data-loading and evaluation systems for non-deterministic models.

- Experience with workflow orchestration tools (e.g., Kubeflow, Airflow, or Metaflow).

- Experience managing large-scale experiments on cloud providers (AWS, GCP, or Azure).

- Proven track record collaborating directly with researchers to translate algorithmic requirements into engineering roadmaps.

- Hands-on experience with containerization (Docker) and orchestration (Kubernetes).