About the RoleAs a
Model Engineer, you'll help define the intelligence layer for this infrastructure: training models that deeply understand networked systems, anticipate failure before it happens, and take autonomous corrective action. You'll work at the intersection of applied ML research and production systems engineering, collaborating closely across hardware and software teams.
What You'll Do- Train end-to-end models applied to fault prediction, network state modeling, and autonomous repair.
- Build multi-modal models over structured networking data and implement function-calling to make network-wide decisions autonomously.
- Evaluate model performance in both real-world hardware and virtualized environments, iterating to improve reliability and efficiency.
- Contribute to shaping the technical direction and culture of a new applied research organization from the ground up.
What We're Looking ForRequired:- Hands-on experience building and training ML models for large-scale infrastructure, monitoring, automation, or systems optimization - this is a dealbreaker requirement.
- Experience working with distributed systems data, telemetry, and anomaly detection to improve reliability and performance.
- Proficiency in Python and one or more ML frameworks (e.g., PyTorch, TensorFlow).
- Experience with MLOps: model deployment, monitoring, versioning, and automation pipelines in production.
- Strong cross-functional communication skills, including presenting findings to both technical and non-technical stakeholders.
- Strong CS fundamentals.
- Willingness to work on-site in San Francisco, CA (Mission District).
Nice to Have:- Prior experience at a startup or fast-paced early-stage company; comfort operating with ambiguity and influencing without formal authority.
- Background in networking, systems, or physical infrastructure domains.
Compensation & Benefits- Salary: $200,000 - $300,000 USD annually, depending on experience.
- Visa sponsorship is not available for this role.
LocationThis is a
full-time, on-site role based in
San Francisco, CA. Regular in-person collaboration is expected.
