Welcome to the Principal AI Infrastructure Engineer Learning Repository! This advanced individual contributor (IC) curriculum prepares senior engineers for principal-level technical leadership, cross-team impact, and technical innovation.
By completing this curriculum, you will be able to:
Technical Excellence:
- Design and implement systems at massive scale (10M+ users)
- Solve novel technical problems with industry-leading solutions
- Drive technical decisions across multiple teams
- Create frameworks and platforms used company-wide
- Master advanced distributed systems and performance optimization
Technical Leadership:
- Lead technical strategy for major initiatives
- Mentor senior and staff engineers effectively
- Conduct architecture and code reviews at scale
- Make high-impact technical decisions independently
- Drive technical standards and best practices
Cross-Organizational Impact:
- Influence technical direction across engineering organization
- Lead cross-functional technical initiatives
- Partner with product and business on strategy
- Represent engineering in company-wide decisions
- Build technical roadmaps aligned with business goals
Innovation & Research:
- Develop proof-of-concepts for emerging technologies
- Drive R&D initiatives and experiments
- Contribute to open source at significant scale
- Publish technical blog posts and talks
- Create intellectual property (patents)
Total Duration: 680 hours (17 weeks full-time, 34 weeks part-time) Difficulty: Principal/Expert Prerequisites: Staff AI Infrastructure Engineer (6+ years experience)
| Module | Topic | Duration | Level |
|---|---|---|---|
| 601 | Principal Engineer Mindset | 15 hours | Principal |
| 602 | System Design at Scale | 25 hours | Expert |
| 603 | Advanced Distributed Systems | 25 hours | Expert |
| 604 | Performance Engineering Mastery | 25 hours | Expert |
| 605 | Technical Leadership & Mentorship | 20 hours | Principal |
| 606 | Cross-Team Architecture | 20 hours | Principal |
| 607 | Technical Strategy & Roadmaps | 20 hours | Principal |
| 608 | Research & Innovation | 20 hours | Principal |
| 609 | Technical Debt Management | 15 hours | Principal |
| 610 | Incident Response & Reliability | 20 hours | Expert |
| 611 | Technical Communication | 15 hours | Principal |
| 612 | Open Source Leadership | 20 hours | Principal |
| Project | Description | Duration | Scope |
|---|---|---|---|
| 01 | Distributed Training Framework | 100 hours | Company-wide |
| 02 | Cross-Team Platform Integration | 80 hours | Multi-team |
| 03 | Performance Optimization Initiative | 100 hours | Critical Path |
| 04 | Technical Innovation POC | 80 hours | R&D |
| 05 | Technical Leadership Capstone | 80 hours | Portfolio |
Before starting, you should have:
- 6+ years in software engineering (AI/ML infrastructure focus)
- 2+ years at senior or staff level
- Deep expertise in distributed systems, Kubernetes, cloud platforms
- Proven track record of delivering complex systems
- Some mentorship experience (tech leads, mentoring seniors)
- Strong communication skills (writing, presenting)
This curriculum is designed for:
- Staff Engineers ready for principal-level impact
- Senior Engineers with exceptional technical depth
- Tech Leads wanting to stay on IC track
- Experienced engineers (8-10 years) targeting principal roles
- ICs who want maximum impact without people management
This is the Individual Contributor (IC) track for engineers who want to maximize technical impact without becoming people managers.
Junior Engineer → Engineer → Senior → Staff → Principal → Distinguished
(0-2 years) (2-5) (5-8) (8-12) (12+)
Alternative: Management track (→ Team Lead → EM → Director)
ai-infra-principal-engineer-learning/
├── README.md # This file
├── CURRICULUM.md # Detailed curriculum
├── lessons/ # Principal-level modules
│ ├── mod-601-principal-mindset/
│ ├── mod-602-system-design-scale/
│ ├── mod-603-distributed-systems/
│ ├── mod-604-performance-mastery/
│ ├── mod-605-technical-leadership/
│ ├── mod-606-cross-team-architecture/
│ ├── mod-607-technical-strategy/
│ ├── mod-608-research-innovation/
│ ├── mod-609-technical-debt/
│ ├── mod-610-incident-reliability/
│ ├── mod-611-technical-communication/
│ └── mod-612-open-source-leadership/
├── projects/ # High-impact projects
│ ├── project-01-distributed-training/
│ ├── project-02-platform-integration/
│ ├── project-03-performance-optimization/
│ ├── project-04-innovation-poc/
│ └── project-05-technical-leadership/
├── assessments/ # Technical assessments
├── resources/ # Papers, talks, frameworks
└── community/ # Principal engineer network
- Module Assessments (12 × 20 points = 240 points)
- Technical Projects (5 × 100 points = 500 points)
- Code Review Excellence (100 points)
- Technical Mentorship (60 points)
- Technical Writing/Speaking (100 points)
- Total: 1,000 points
- Overall Passing: 700/1,000 points (70%)
- Each Project: Minimum 70/100 points
- Technical Writing: Published blog post or talk required
- Mentorship: Demonstrated through project reviews
You're ready for Principal Engineer when you can:
- Design systems handling 10M+ users or 100TB+ data
- Lead technical initiatives across 3+ teams
- Make architectural decisions with company-wide impact
- Mentor senior and staff engineers effectively
- Communicate technical concepts to all levels
- Drive technical strategy and innovation
- Resolve complex cross-functional technical problems
- Principal Engineer - AI Infrastructure
- Principal Engineer - ML Platform
- Principal Engineer - MLOps
- Staff Engineer (Senior Staff) - L7/L8
- Distinguished Engineer (next step)
- Principal Engineer (L7): $280,000 - $500,000
- Senior Principal (L8): $350,000 - $650,000
- Distinguished Engineer (L9): $450,000 - $800,000+
- Fellow (L10): $600,000 - $1,000,000+
Note: Total compensation = base + equity + bonus Varies significantly by company (FAANG pays higher)
From:
- Staff AI Infrastructure Engineer (L6)
- Senior Engineer with exceptional depth
- Tech Lead ready for broader impact
To:
- Senior Principal Engineer (L8)
- Distinguished Engineer (L9)
- Fellow / Chief Engineer (L10)
Alternative Path: Can switch to management at any time
- Principal → Engineering Manager
- Principal → Director of Engineering
- Distributed Systems: Consensus, replication, partitioning, CAP theorem
- System Design: Scalability, reliability, performance at massive scale
- Performance: Profiling, optimization, hardware acceleration (GPU/TPU)
- ML Infrastructure: Training at scale, model serving, MLOps platforms
- Architecture: Cross-team systems, technical strategy, ADRs
- Mentorship: Growing senior → staff → principal pipeline
- Code Review: Teaching through reviews, raising code quality bar
- Technical Decisions: Data-driven, documented, communicated
- Cross-Team Projects: Leading initiatives spanning multiple teams
- Technical Strategy: 6-12 month roadmaps, technology choices
- Problem Solving: Unblocking teams, resolving complex technical issues
- Innovation: POCs, research, emerging technology evaluation
- Technical Writing: Design docs, blog posts, documentation
- Presentations: Technical talks, lunch-and-learns, conferences
- Stakeholder Management: Product, business, executive alignment
- Conflict Resolution: Technical disagreements, building consensus
Build company-wide distributed training platform:
- Support PyTorch, TensorFlow, JAX
- Multi-node, multi-GPU training (100+ GPUs)
- Fault tolerance and checkpointing
- Performance optimization (90%+ GPU utilization)
- Developer-friendly API and documentation
Impact: Enables all ML teams to train at scale Deliverables: Framework, docs, migration guide, talk/blog post
Integrate 3+ team platforms into unified system:
- Requirements gathering from stakeholders
- Integration architecture and API design
- Migration strategy and backward compatibility
- Performance and reliability guarantees
- Rollout plan and monitoring
Impact: Reduces fragmentation, improves efficiency Deliverables: Integration platform, ADRs, migration docs
10x performance improvement for critical path:
- Profiling and bottleneck identification
- Algorithm optimization and caching
- Hardware acceleration (GPU, custom kernels)
- Distributed computing strategies
- A/B testing and validation
Impact: Major cost savings or user experience improvement Deliverables: Optimized system, performance report, metrics
Research and prototype emerging technology:
- Technology evaluation (e.g., quantum ML, edge AI)
- Proof-of-concept implementation
- Feasibility analysis and recommendations
- Business case and ROI estimation
- Presentation to technical leadership
Impact: Informs R&D strategy and roadmap Deliverables: POC, technical report, recommendation
Demonstrate principal-level leadership:
- Technical portfolio (projects 1-4)
- Mentorship program design
- Technical talks or blog post series
- Open source contributions
- Personal technical strategy
Impact: Principal engineer brand and visibility Deliverables: Portfolio, publications, OSS contributions
- MapReduce - Dean & Ghemawat (Google)
- The Google File System - GFS paper
- Bigtable - Chang et al.
- Dynamo - Amazon's key-value store
- Spanner - Google's distributed database
- Borg - Google's cluster management
- TAO - Facebook's distributed data store
- Scaling Laws for Neural Language Models - OpenAI
- GPipe - Google's pipeline parallelism
- Megatron-LM - NVIDIA's model parallelism
- ZeRO - Microsoft's memory optimization
- Ray - UC Berkeley's distributed framework
- Designing Data-Intensive Applications - Kleppmann
- Site Reliability Engineering - Google SRE Book
- The Staff Engineer's Path - Tanya Reilly
- Staff Engineer: Leadership Beyond the Management Track - Will Larson
- A Philosophy of Software Design - John Ousterhout
- Release It! - Michael Nygard
- Internal: Design docs, ADRs, technical RFCs
- External: Blog posts on company engineering blog
- Personal: Medium, personal blog, Dev.to
- Books: Contribute chapters, write technical books
- MLOps World - ML infrastructure track
- AI Infrastructure Alliance
- QCon - Architecture and ML tracks
- Kubecon - Cloud-native AI
- ICML/NeurIPS - Systems for ML workshops
- Major Projects: Kubernetes, Ray, Kubeflow, MLflow
- Create OSS: Your own framework or tool
- Maintainership: Become core contributor/maintainer
- CNCF: Join CNCF projects or working groups
- File patents on novel technical innovations
- Publish research at conferences (MLSys, SysML)
- Technical reports on breakthroughs
- Principal engineer cohorts (monthly meetups)
- Cross-company principal networks
- Technical book clubs
- Paper reading groups
- Mentor senior → staff engineers
- Be mentored by distinguished engineers
- Reverse mentoring (learn from juniors)
- External mentorship (industry connections)
MIT License - See LICENSE for details
- Principal and Distinguished Engineers who reviewed curriculum
- Staff Engineers who contributed project ideas
- Open source community for technical excellence
- Everyone who shared their principal engineer journey
- Principal AI Infrastructure Architect (Architecture focus)
- AI Infrastructure Team Lead (Management track)
- Senior Principal Engineer (L8)
- Distinguished Engineer (L9)
- Fellow / Chief Engineer (L10)
Or transition to management:
- Engineering Manager (manage teams)
- Director of Engineering (manage managers)
Ready to reach the pinnacle of IC technical excellence? 🚀 Begin with Module 601: Principal Engineer Mindset
Questions? Open an issue or join our principal engineer network!
Last Updated: October 2025 Version: 1.0.0 Maintained by: AI Infrastructure Curriculum Team Contact: ai-infra-curriculum@joshua-ferguson.com