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# Sr. Machine Learning Engineer, Network Engineering

- **Company:** Tesla Motors
- **Location:** Fremont, CA, United States
- **Experience:** Expert
- **Salary:** $168,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Border Gateway Protocol, Complex Networks, Continuous Integration, Distributed Computing Environment, Ethernet, IP Addressing, Python (Programming Language), Machine Learning, Networking Basics, Routing, Open Shortest Path First (OSPF), Operational Data Store, Simple Network Management Protocols, Virtual Local Area Networks, Data Processing, Computer Networking Systems, Data Ingestion, Pytorch, Delivery Pipeline, Large Language Models, Model Validation, Backend, Containerization, Machine Learning Operations, Software Version Control, Microservices
- **Published:** July 23, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/85673278/1

## About the Role

We are seeking a highly skilled Senior Software Engineer to lead development of a platform that combines large scale LLM training with advanced network data processing and intelligent diagnostic capabilities., * 5+ years of experience in applied ML, ML systems, or backend platform engineering

 * Strong experience with LLM training, including SFT, LoRA/PEFT, RLHF, reward modeling, and PPO
 * Proficiency with PyTorch and distributed training technologies
 * Solid backend engineering skills in Python, including APIs, microservices, data modeling, and containerization
 * Strong understanding of networking fundamentals such as IP addressing, Ethernet, VLANs, routing protocols (BGP/OSPF/ISIS), and topology concepts
 * Experience working with networking telemetry or operational data (SNMP, flow data, metrics, logs, routing/forwarding tables)
 * Ability to interpret and model complex network states, device relationships, and diagnostic patterns

## Description

This role requires strong machine-learning engineering skills, deep software engineering fundamentals, and a solid understanding of network systems.
What You'll Do

 * Design, build, and maintain pipelines for LLM pretraining, supervised fine-tuning (SFT), reward modeling, PPO/RLHF, and model evaluation
 * Develop scalable backend services and distributed training infrastructure
 * Build data-ingestion and transformation pipelines for networking datasets including device inventory, topology data, ARP/MAC tables, routing state, metrics, telemetry, and logs
 * Normalize and model heterogeneous networking data to support ML workflows and agent inference
 * Develop intelligent agent capabilities, including intent classification, context tracking, troubleshooting logic, and action-routing workflows
 * Collaborate with networking domain experts to translate operational needs into model and platform capabilities
 * Implement CI/CD, model versioning, orchestration, and production-grade deployment pipelines
 * Drive architectural decisions to ensure scalability, modularity, reliability, and performance


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