Software Engineer, ML Systems
AI in this role
About Harmonic
At Harmonic, we are building a mathematical reasoning engine that operates with absolute precision. While most AI makes maximum-likelihood guesses, Harmonic's Aristotle uses Lean 4 and reinforcement learning to verify its reasoning and results.
Following our Gold Medal-level performance on the 2025 International Math Olympiad (IMO) and the successful resolution of long-standing open problems, we are proving that AI can master the most rigorous domains of human thought. Backed by some of the world’s most prominent investors, we are intentionally scaling an elite technical team.
Visit our company blog to learn more about what we are working on!
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).
What We Offer
Unlimited PTO
401(k) matching
100% employer-paid health, vision, and dental benefits for employees and 50% coverage for dependents. Harmonic offers varied health coverage options to select what is best for you and your family.
Health Savings Account (HSA) available for qualifying health plans
Equal Opportunity Statement
Harmonic is committed to diversity and inclusivity in the workplace. We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or any other legally protected status.
How we rate this
Software Engineer, ML Systems at Harmonic rates 94 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- What's a project where you used Bedrock hands-on?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: Bedrock. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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