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# Sr. Software Engineer, Infrastructure, Manufacturing Quality

- **Company:** Tesla Motors
- **Location:** Fremont, CA, United States
- **Experience:** Expert
- **Salary:** $120,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Airflow, Component-Based Software Engineering, Databases, DevOps, Python (Programming Language), Reliability Engineering, Prometheus, Single Sign-On, Software Deployment, SQL Databases, Systems Integration, Data Logging, Grafana, Agentic-AI, Git, Kubernetes, Information Technology, Apache Flink, Github Enterprise, Production Code, Performance Monitor, Hashicorp, Apache Kafka, Build Tools, Machine Learning Operations, Front End Software Development, Vertica, Kibana, Splunk, Data Pipelines, Docker, Pagerduty, Jenkins
- **Published:** October 10, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/88577222/1

## About the Role

* Master's Degree in Computer Science or a quantitative discipline, or equivalent
 * 5+ years in infrastructure, DevOps, site reliability or platform engineering
 * Kubernetes, Docker, Jenkins and Git for production deployment
 * Vault, Helm, Prometheus, Grafana, Splunk, Kibana, Airflow and OpsGenie
 * Data and ML pipeline operations for training and inference delivery
 * Python and Go, REST and gRPC, SQL and column stores such as ClickHouse
 * Collaboration across teams under tight deadlines

## Description

* Develop and maintain Kubernetes configuration and infrastructure as code, and deploy APIs, frontends, SSO, ingress, storage, platforms and systems to clusters worldwide, including multi-region expansion
 * Develop and maintain Jenkins CI/CD pipelines, including build validation, and integrate them with GitHub Enterprise
 * Standardize, create and maintain HashiCorp Vault and Kubernetes secrets across 50+ services and applications, and design and scale Airflow infrastructure worldwide with Helm
 * Design and operate machine learning lifecycle infrastructure for training and inference, including training-data pipelines, Kafka and Flink, and platform services for AI agents and agentic workflows such as stateful execution, retries, failure recovery, scheduling, metadata and long-running orchestration
 * Build logging, metrics and alerting, and set service level objectives for availability, latency and system health with Prometheus, Grafana, Splunk, Kibana and OpsGenie, including MLOps and application components
 * Manage on-call and incident response, run postmortems, drive corrective actions to closure and partner with development and quality engineers on reliability, testing and release practices
 * Design, develop and maintain the Python and Go platform services and APIs (REST and gRPC), databases and integrations that pipelines, agents and internal tools depend on, and troubleshoot production infrastructure and data pipeline systems
 * Lead major initiatives to eliminate speed bottlenecks, database contention, and infrastructure issues through proactive monitoring and automation
 * Write production-grade code and build tools to reduce toil and improve deployment, monitoring, and operational workflows