Levels at big tech, the most common career paths, and what comes after making it to Staff
Staff archetypes
Most career ladders define a single, uniform set of expectations for Staff engineers operating within the company. Everyone benefits from clear role expectations, but career ladders are a tool that applies better against populations than people. This is particularly true for Staff-plus engineers, whose career ladders often paper over several distinct roles hidden behind a single moniker. The more folks I spoke with about the role of Staff-plus engineers at their company, the better their experiences began to cluster into four distinct patterns. Most companies emphasized one or two of the patterns, and one pattern only existed in companies with many hundreds or thousands of engineers. A few companies didn’t feature any technical leadership pattern and pushed all their experienced engineers towards engineering management. In literature, recurring character patterns are called archetypes, such as the “hero” or the “trickster,” and the archetype term is helpful for labeling these frequent variants of Staff-plus engineers.
Engineering managers have a new job description
Why engineering managers are expected to be hands-on again and how AI tools are making it possible to stay technically sharp while still leading.
Hiring (and Retaining) a Diverse Engineering Team
Stories from six engineering leaders who succeeded in building and growing diverse teams. Hiring approaches, retention tactics and strategies.
Neuroscience needs a career path for software engineers
Few institutions have mechanisms for the type of long-term positions that would best benefit the science.
Engineering Leadership Skill Set Overlaps
How Staff Engineer, Engineering Manager (EM), Product Manager (PM), Tech Lead Manager (TLM) and Technical Program Manager (TPM) positions overlap in Big Tech and at high-growth startups – and their di
Scaling Engineering Teams: Lessons from Google, Facebook, and Netflix
After spending over a decade in engineering leadership roles at some of the world’s most chaotic innovation factories—Google, Facebook, and Netflix—I’ve learned one universal truth: scaling e…
Software Innovation Lab
Scale your engineering power. We enable deep-tech startups to achieve their vision, from research to product delivery.
The Impact of Artificial Intelligence on Education and Workforce Trajectories in Tech: A Workshop
Convened by the National Academies’ Action Collaborative on Education and Workforce Trajectories in Tech, The Impact of Artificial Intelligence on Education and Workforce Trajectories in Tech is a one-day exploratory workshop examining how AI is altering the value of human expertise, organizational workforce structures, and career pathways in the tech sector. Bringing together leaders from higher education, industry, and research institutions, discussions will consider how education and workforce systems can prepare individuals with the ethical, technical, and analytical capabilities needed to adapt and thrive in an AI-impacted landscape.
The Engineering Leadership Report 2026
Our survey of 600 engineering leaders exploring how the role is evolving, where the challenges lie, and what the future holds for the job.
I lost my UX career to AI (I'm done)
Slow down to speed up: so much has changed in 6 months’ time
An overview of what’s changed in engineering during the last six months, how various tech companies are changing how they work, and why slowing down could be a sensible strategy
What do professional software developers need to know to succeed in an age of Artificial Intelligence?
Generative AI is showing early evidence of productivity gains for software developers, but concerns persist regarding workforce disruption and deskilling. We describe our research with 21 developers at the cutting edge of using AI, summarizing 12 of their work goals we uncovered, together with 75 associated tasks and the skills & knowledge for each, illustrating how developers use AI at work. From all of these, we distilled our findings in the form of 5 insights. We found that the skills & knowledge to be a successful AI-enhanced developer are organized into four domains (using Generative AI effectively, core software engineering, adjacent engineering, and adjacent non-engineering) deployed at critical junctures throughout a 6-step task workflow. In order to "future proof" developers for this age of AI, on-the-job learning initiatives and computer science degree programs will need to target both "soft" skills and the technical skills & knowledge in all four domains to reskill, upskill and safeguard against deskilling.
Pure and impure software engineering
Why do solo game developers tend to get into fights with big tech engineers? Why do high-profile external hires to large companies often fizzle out? Why is AI-assisted development amazing for some engineers and completely useless for others?
Highline Beta | Corporate Venture Studios & Venture Building
We build ventures and venture studios with corporate partners and family offices through rigorous process, entrepreneurial grit, and capital.
Staff Engineer and Public Speaker | Szymon Chudy
Staff engineer who cares about the why, not just the how. Writing about frontend, productivity, and growing in tech.
