The Engineer Who Wrote So Much Code That Meta Created a New Job Title: Inside the "Coding Machine" Archetype

How Michael Novati became Meta's first "coding machine," writing 5-10k commits annually while progressing from intern to E7 in just 6 years through exceptional productivity.

Key Takeaways

• Meta created the "coding machine" archetype specifically for Michael Novati, who progressed from intern to E7 (senior staff) in just 6 years
• The coding machine role involves writing massive amounts of high-impact code—5-10k commits annually—while unblocking teams and accelerating product development
• Meta's archetype system enables senior IC career progression beyond traditional tech lead roles, with patterns like fixer, tech lead, and coding machine
• Internal tools at Meta were treated as full products built on the same codebase as user-facing features, creating exceptional engineering productivity
• The hiring committee process included open observation sessions where engineers could shadow decision-making to understand calibration standards
• Career acceleration required exceptional manager relationships, detailed work documentation, and focus on impact rather than just code volume
• Meta's engineering culture emphasized "move fast and break things" with real consequences—outages were learning opportunities rather than career killers
• Modern tech interview processes remain largely unchanged since pre-LeetCode era, focusing on stack-agnostic problem-solving skills

Timeline Overview

• 2009: Intern Arrival — Michael joins 200-person Facebook as intern, immediately ships horizontal org chart tool used company-wide
• 2009-2015: Rapid Progression — Four promotions in six years from intern to E7, becoming known for exceptional code output and impact
• 2015: Archetype Creation — Meta formally creates "coding machine" archetype with VP sponsorship to recognize unique contribution pattern
• Product Team Roles — Worked on internal tools, Facebook Groups, Workplace, News Feed, Messenger for Kids while doing company-wide refactoring
• French Flag Incident — Caused major outage when 100M users adopted profile picture template, overloading single database node
• Hiring Committee Participation — Shadowed and participated in open hiring committee sessions to understand calibration standards
• 2017: Departure — Left Meta after eight years to co-found Formation, engineering interview preparation company
• Current Role — Continues "coding machine" approach at Formation with 20+ commits daily, applying lessons from Meta experience

The Birth of an Archetype: What Makes a Coding Machine

• Meta's archetype system provides fair senior IC career progression by pattern-matching engineers against existing high performers rather than forcing management transitions
• The "coding machine" archetype emerged because Michael's contribution pattern didn't fit existing categories like "fixer" (writes few high-impact lines) or "tech lead" (guides technical direction)
• A coding machine delivers impact through volume and speed: refactoring massive codebases, unblocking multiple teams, and shipping features that typically require 5-10 person teams
• The controversial nature stems from anyone theoretically being able to write lots of code, but true coding machines create outsized impact through what their code accomplishes

The archetype system reflects Meta's "pro sports team" philosophy where everyone at senior levels can perform basic functions, but individuals excel in specific specialties that benefit the entire organization.

Michael's pattern involved spending 30% of time contributing as a senior engineer on his assigned product team, while dedicating 70% to company-wide refactoring and infrastructure improvements that multiplied other engineers' productivity.

• The coding machine role requires both technical excellence and strategic thinking about what code will have maximum leverage across the organization
• Executive sponsorship from Tom Allison (now head of Facebook app) was crucial for creating institutional recognition of this contribution pattern
• The archetype enables exceptional individual contributors to reach E7+ levels without transitioning to management or traditional tech lead responsibilities
• Success metrics focus on unblocking others, accelerating project timelines, and eliminating technical debt that slows down entire engineering teams

The creation process involved extensive back-and-forth with VPs to establish that Michael's impact matched other E7s despite following a completely different model.

From Intern to E7: The Rapid Ascension Strategy

• Michael's progression from intern to senior staff in six years required exceptional focus on impact documentation and strategic manager relationships
• Day three as intern: rewrote company-wide org chart tool from vertical to horizontal layout, instantly used by entire company including Mark Zuckerberg
• The horizontal design choice accidentally reinforced Facebook's flat hierarchy values, showing interns on same visual level as executives
• Career acceleration required treating each promotion as individual challenge rather than overwhelming long-term goal—focus on next step only

The rapid progression strategy centered on two key pillars: exceptional manager relationships built on trust and transparency, plus meticulous documentation of impact through detailed work logs.

Manager relationships involved radical honesty about areas for improvement combined with unwavering commitment to maintain high output velocity. This created trust that enabled managers to provide direct feedback without defensive reactions.

• Maintained detailed notepad of accomplishments for performance reviews, enabling managers to understand full scope of contributions beyond assigned projects
• Used work documentation to bucket contributions into promotion requirements, identifying gaps and addressing them systematically
• Manager trust enabled honest feedback like "you're causing too many bugs in this area" without slowing down overall development pace
• The approach required managers who understood how to translate individual contributions into performance review language and calibration standards

The documentation strategy proved crucial because 70% of coding machine work happened outside official project assignments, making impact invisible without explicit tracking.

Meta's Engineering Culture: Internal Tools as Products

• Internal tools received full product treatment using the same codebase as user-facing features, deployed to employee subset rather than public
• Tools team was considered most exciting assignment because building for 700 employees moved faster than building for 200 million users
• Everything built from scratch including servers, hardware, infrastructure due to pre-cloud era constraints and control requirements
• This mentality created company-wide product engineering mindset where every employee felt part of technology company solving problems through software

The internal tools philosophy meant meeting room booking systems, org charts, and employee directories received the same engineering rigor as News Feed or messaging features.

Custom tool development extended to version control (SVN backbone with Mercurial local branches), code review systems (Phabricator), build tools (Buck), and experimentation platforms (later spawning companies like StatSig).

• Mark Zuckerberg's 2009 statement about training people to be good founders reflected intentional culture of building engineering excellence that would seed industry
• Product review culture ("Zuck reviews") involved weekly 15-minute presentations from all product teams with immediate feedback and direction changes
• "Move fast and break things" represented genuine value where breaking something while pursuing bold ideas was rewarded rather than punished
• The culture attracted passionate engineers willing to be "all in" on Facebook's mission rather than treating it as stepping stone career move

Internal tool quality often exceeded external alternatives because they solved specific Facebook workflow problems with unlimited engineering resources.

The French Flag Outage: When Coding Machines Break Things

• Profile picture template system launched during Paris terrorist attacks, enabling users to overlay French flag as solidarity gesture
• 100 million users adopted template within days, overwhelming single database node due to prototype-level edge design
• Two-way edges from template to profiles created write-heavy load on single machine hosting the French flag template object
• Database node failure caused cascading effects throughout system since user profiles and other objects shared same federated storage

The outage illustrates classic systems design tradeoffs between prototype speed and production scalability, showing how even experienced engineers make architectural decisions with unintended consequences.

The two-way edge design choice was deliberate for prototype simplicity—enabling direct counting of template usage without complex logging infrastructure that would require PM education and maintenance overhead.

• Similar fan-out problems affected Twitter's "fail whale" outages when high-profile users like Justin Bieber tweeted to massive audiences
• Resolution involved switching to one-way edges and productionizing with proper logging infrastructure to replace lost edge-based metrics
• Site reliability team maintained well-stocked alcohol bar from grateful engineers sending thank-you gifts after infrastructure saves
• The incident demonstrates how rapid prototyping decisions can have massive-scale consequences when features exceed usage expectations

Meta's culture treated outages as learning opportunities rather than career-limiting events, encouraging engineers to move fast while taking responsibility for consequences.

The Open Hiring Committee: Democratizing Decision Transparency

• Hiring committee meetings were technically open, allowing any engineer to observe decision-making processes for calibration understanding
• Committees required quorum of three director/VP-level engineers to review candidate packets and make final hiring decisions
• Candidate packets included interview feedback, interviewer calibration data (historical rating patterns), question history, and experience levels
• Primary focus involved level calibration—ensuring candidates received appropriate offers consistent with existing employees at similar performance levels

The open committee structure reflected Meta's transparency values while enabling engineers to understand how hiring decisions connected to company performance standards.

Michael's regular attendance stemmed from concerns about maintaining hiring bar quality during rapid company growth from hundreds to thousands of employees.

• Interview feedback packets included interviewer reliability metrics showing whether reviewers typically gave strong yes/no ratings or nuanced assessments
• Question familiarity data revealed whether interviewers asked problems for first time versus having refined through hundreds of iterations
• Level discussions dominated committee time, addressing whether candidates demonstrated senior capabilities despite mid-level backgrounds or vice versa
• The process prioritized finding right role fit rather than binary hire/no-hire decisions, recognizing mismatches didn't indicate engineering incompetence

Committee participation provided unique insight into calibration standards that later informed Michael's interview coaching approach at Formation.

Modern Interviewing: Why the Process Never Changed

• Technical interview formats remain largely unchanged since before LeetCode existence (founded 2012), focusing on language-agnostic problem-solving skills
• Meta's interview structure: recruiter screen, technical screen, onsite with two coding interviews, behavioral interview, and systems design
• Whiteboarding style with minimal small talk creates efficiency but can surprise candidates expecting rapport-building time
• Team matching process added extra layer between successful onsite and final offer, creating additional competition bottleneck

The consistency across companies stems from shared talent pipelines and proven effectiveness at identifying core engineering problem-solving abilities regardless of specific technology stack experience.

Modern market changes include increased online assessments as initial filtering mechanism and more competitive team matching processes that extend decision timelines.

• Google's long-standing team matching process spread to Meta and other companies as hiring volume increased and role specialization grew
• Interview preparation requires recurring investment similar to physical fitness—skills atrophy without practice regardless of past capability
• Formation's repeat customer pattern shows engineers returning every 2-3 years for interview preparation despite previous success
• The process tests fundamental problem-solving skills that remain relevant across different technology stacks and company contexts

Understanding interviewer calibration patterns and question familiarity helps candidates optimize performance within existing system rather than fighting fundamental structure.

Lessons for Aspiring Coding Machines

• Impact matters more than raw code volume—focus on unblocking others, accelerating projects, and eliminating friction for multiple teams
• Document everything systematically to make invisible contributions visible during performance reviews and promotion discussions
• Build exceptional manager relationships through transparency and trust rather than attempting to be personally likable
• Choose work that creates multiplicative effects across engineering organization rather than just completing assigned tasks efficiently

The coding machine approach requires strategic thinking about which technical work will have maximum leverage across the entire engineering organization.

Success depends on developing reputation for reliability and technical excellence that causes other teams to seek your involvement in critical projects.

• Maintain detailed work logs capturing impact beyond assigned projects to enable managers to effectively advocate for recognition
• Focus on one promotion level at a time rather than being overwhelmed by long-term career trajectory requirements
• Seek opportunities to contribute to company-wide infrastructure and refactoring efforts that benefit multiple product teams
• Balance assigned team responsibilities with broader organizational contributions to demonstrate both reliability and initiative

The archetype works best for engineers who genuinely enjoy coding and find satisfaction in writing large amounts of high-quality code rather than those seeking recognition through other means.

The Meta Legacy: Training an Industry

• Many internal tools spawned successful companies as ex-employees productized Facebook's custom solutions for broader market
• Engineering culture emphasized building products quickly and iterating based on real user feedback rather than theoretical planning
• The focus on individual contributor career paths influenced industry thinking about senior engineering roles beyond traditional management transitions
• Formation and similar companies apply Meta interview insights to help engineers navigate increasingly competitive technical hiring processes

Meta's influence extends beyond specific tools to cultural approaches around engineering excellence, rapid iteration, and data-driven decision making.

The company's willingness to experiment with organizational structures like archetypes provided models for other companies seeking to retain senior technical talent.

• StatSig emerged from internal experimentation platform, GraphQL from internal API needs, React from UI framework requirements
• Open source releases of Buck, Mercurial tools, and other infrastructure enabled industry adoption of Facebook's engineering practices
• The emphasis on internal tools as products influenced startup culture around investing in developer experience and productivity
• Interview process standardization across tech companies partly reflects Meta's success in identifying and developing engineering talent

Current AI company hiring practices draw heavily from Meta's playbook, showing continued relevance of engineering culture developed during rapid growth period.

Common Questions

Q: What exactly makes someone a "coding machine" versus just writing lots of code?
A: Impact through code volume—unblocking teams, refactoring entire systems, and enabling others to move faster rather than just high commit counts.

Q: How did Michael progress so quickly from intern to E7 in just 6 years?
A: Exceptional manager relationships, detailed impact documentation, and focusing on company-wide contributions beyond assigned projects.

Q: Why did Meta create custom internal tools instead of using existing solutions?
A: Pre-cloud era required custom infrastructure, and treating internal tools as products created better solutions for specific Facebook workflows.

Q: How does Meta's hiring committee process actually work?
A: Open meetings with director/VP quorum review candidate packets including interview feedback and interviewer calibration data to ensure appropriate leveling.

Q: What caused the French flag outage and how was it fixed?
A: 100M users overwhelmed single database node due to two-way edges; fixed by switching to one-way edges and proper infrastructure scaling.

Michael Novati's story reveals how exceptional individual contributors can create massive impact through strategic code volume and company-wide thinking. The "coding machine" archetype demonstrates that senior engineering careers don't require management transitions when engineers focus on multiplicative impact across organizations. His experience at Meta provides valuable insights into engineering culture, rapid career progression, and the interview processes that continue shaping how tech companies identify and develop talent.

The lessons about documentation, manager relationships, and strategic contribution selection remain relevant for any engineer seeking to maximize their impact and career advancement.