Sr Systems Engineer

About the Role

As a Sr. Systems Engineer on Uber Freight's Platform Engineering team, you'll lead the design and implementation of AI-powered developer experience tools that transform how 150+ engineering, product, and design teams work. You'll combine deep infrastructure expertise with Claude API mastery to architect autonomous workflows, intelligent dashboards, and organization-wide automation that multiplies engineering productivity.

This is technical leadership for internal DevEx. You'll define standards for AI-first tooling, lead cross-functional initiatives, and mentor engineers on building production AI systems. You'll architect solutions that serve the entire CTO organization — not just write code, but establish patterns others follow.

Your working style is AI-first. You default to Claude API and MCP for every automation opportunity. You think in autonomous agents, not scripts. You ship tools that learn and adapt, and you set the technical direction for how the org builds intelligent automation.

What the Candidate Will Do

Architect AI-Powered DevEx Systems

• Design and lead implementation of organization-wide DevEx platforms (like Athena: 150K JIRA issues, $35M budget tracking, CTO-wide visibility)
• Architect autonomous workflows — multi-agent systems for incident response, code review, deployment validation, and operational toil reduction
• Define MCP server standards — establish patterns for exposing internal systems (Vault, JIRA, Datadog, Jenkins) to Claude Code
• Lead technical initiatives — drive cross-team projects from requirements through production deployment
• Establish AI-first best practices — prompt engineering patterns, agent orchestration frameworks, error handling, observability for AI systems

Drive Organization-Wide Impact

• Set technical standards for DevEx tooling across Platform Engineering, SRE, and Data Engineering teams
• Lead critical incident response for internal tools — advanced root cause analysis, system-wide reliability improvements
• Optimize organization-level systems — architect solutions that improve scalability, efficiency, and developer productivity metrics
• Define observability strategy for AI-powered tools — metrics, dashboards, alerts, and runbooks that ensure operational excellence
• Establish security and compliance frameworks — audit AI workflows, manage secrets (Vault), enforce RBAC and network policies

Technical Leadership & Mentorship

• Mentor engineers on AI integration patterns, MCP server development, and autonomous agent design
• Lead code reviews for complex AI systems — ensure quality, maintainability, and adherence to standards
• Define and enforce operational best practices — CI/CD patterns, deployment strategies, monitoring standards
• Collaborate cross-functionally — work with SRE, DevOps, Data Engineering, Finance, and Product to align tooling with organizational goals
• Drive technical innovation — research emerging AI capabilities, prototype new approaches, evangelize successful patterns

Own End-to-End Systems

• Architect multi-region, hybrid-cloud solutions for high-availability DevEx tools (GCP primary, OCI, Azure)
• Design and implement organization-level CI/CD systems integrated with AI agents for auto-remediation
• Lead integration projects spanning multiple teams — JIRA + GitHub + Datadog + Vault + PagerDuty data pipelines
• Manage infrastructure at scale — Kubernetes (GKE Autopilot), Terraform/Terragrunt, ConfigSync GitOps
• Ensure compliance with security standards, audit requirements, and reliability SLOs

Basic Qualifications

• Education: Bachelor's degree in Computer Science, Engineering, or related field (or equivalent experience)
• Experience: 8-12 years in platform engineering, DevOps, SRE, or infrastructure roles
• Proven AI expertise: Track record of building production systems with LLM APIs (Claude, OpenAI, etc.)
• Technical depth: Expert-level Python, Go, or TypeScript — can architect and build complex web applications and distributed systems
• Infrastructure mastery: Deep hands-on experience with Kubernetes, Terraform, multi-cloud platforms, and production operations
• Leadership: Demonstrated experience leading technical initiatives and mentoring engineers

Preferred Qualifications

AI & Automation Leadership

• Claude API mastery — advanced patterns: prompt caching, extended thinking, batch processing, streaming with tool use, multi-agent orchestration
• MCP protocol expertise — built production MCP servers or contributed to MCP ecosystem; deep understanding of protocol design
• Agent framework design — architected custom agent orchestration systems, not just used off-the-shelf frameworks
• Autonomous systems — built self-healing, self-improving AI workflows that operate at production scale
• Prompt engineering excellence — published patterns, reusable templates, measurable improvements in AI system performance

DevEx & Platform Leadership

• Organization-wide tool adoption — shipped tools used by 50+ engineers; measured and improved adoption metrics
• Engineering productivity systems — implemented DORA metrics, JIRA velocity tracking, deployment analytics, or incident management platforms
• Observability platform expertise — designed and operated Datadog, Dynatrace, or New Relic at scale; created org-wide monitoring standards
• API architecture — designed multi-service API ecosystems with versioning, rate limiting, auth, and comprehensive documentation
• Developer experience design — user research, UX for technical tools, onboarding optimization

Infrastructure & Cloud Expertise

• Kubernetes at scale — operated production GKE/EKS/AKS clusters serving high-traffic applications (5+ years); expertise in security, networking, autoscaling, disaster recovery
• Multi-cloud architecture — designed hybrid solutions spanning GCP, Azure, AWS, or OCI; expertise in Terraform modules and Terragrunt for DRY patterns
• GitOps leadership — established ConfigSync, ArgoCD, or Flux patterns for declarative deployments across teams
• CI/CD architecture — designed organization-level pipeline frameworks (Jenkins Job DSL, GitHub Actions, Buildkite); integrated AI agents for auto-remediation
• Service mesh & traffic management — Istio, Envoy, multi-region routing, BCP/DR strategies

Engineering Excellence & Leadership

• Cross-functional leadership — led projects spanning Platform, SRE, Data, Finance teams; aligned technical solutions with business goals
• Standards definition — established coding standards, architectural patterns, deployment practices adopted org-wide
• Incident management expertise — led critical incident response, performed advanced RCA, implemented system-wide reliability improvements
• Security & compliance — managed org-wide security audits, risk assessments, and compliance frameworks (SOC2, ISO27001, etc.)
• Technical mentorship — proven track record developing junior/mid-level engineers; created training materials, led workshops

About Uber Freight 

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EEOC

Uber Freight is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regards to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.