Best Company Cultures: Why NVIDIA and ServiceNow are Winning
By
Ethan Fahey
•
Feb 17, 2026
Heading into 2025–2026, the decision-making calculus for AI engineers, ML researchers, infrastructure engineers, and LLM specialists has changed. Compensation still matters, but it’s no longer enough on its own. Engineers building enterprise AI systems, autonomous agents, and next-gen infrastructure are weighing culture, real impact, and how responsibly companies use AI internally just as heavily as salary and title.
That’s one reason companies like NVIDIA and ServiceNow keep showing up on “Best Places to Work” and “Best Company Culture” lists, including Glassdoor and Newsweek in 2025. Those rankings reflect something concrete: engineers report working on meaningful problems, feeling supported when they take smart risks, and having clear paths to technical leadership without being pushed into management. For talent with options, that mix is powerful. Fonzi AI is built for engineers who want both competitive compensation and strong cultural alignment. Through curated, transparent Match Day events, Fonzi connects experienced AI, ML, and engineering talent with companies that commit to salary clarity and fast, structured hiring, using AI to reduce friction and bias while keeping human judgment at the center. In the rest of this article, we’ll break down what makes cultures like NVIDIA and ServiceNow stand out, how agentic AI is reshaping hiring, and how to position yourself for companies with similar DNA.
Key Takeaways
NVIDIA and ServiceNow consistently top 2025–2026 “Best Places to Work” lists from Glassdoor and Newsweek, earning especially high marks from AI engineers, ML researchers, and infrastructure specialists who value both technical challenge and supportive environments.
Great company culture in 2025–2026 means high-impact work, psychological safety for experimentation, clear career paths, and responsible use of AI, not just free snacks or unlimited PTO.
Fonzi AI is a curated, engineer-first talent marketplace that connects AI/ML, infrastructure, and LLM specialists with top cultures through fast, 48-hour “Match Day” hiring events where companies commit to salary ranges upfront.
Fonzi uses AI to reduce noise, fraud, and bias in hiring while keeping human recruiters and hiring managers at the center of every decision; no offer is ever generated solely by a model.
What “Great Culture” Means for AI & Engineering Talent in 2025–2026
“Great culture” for AI engineers is no longer vague. It can be defined with specific traits visible at NVIDIA, ServiceNow, and top AI startups. Extensive research into what makes team members feel engaged shows that culture for technical professionals comes down to a few things that can be observed, tested, and verified during the hiring process.
Meaningful technical problems sit at the core. Engineers don’t want to optimize click-through rates on banner ads forever. They want to work on challenges that push the field forward—training infrastructure for LLMs, reliability at scale, or systems that ship to millions of users. Companies with great cultures connect everyday actions to a larger impact.
Autonomy with accountability means engineers own their work without micromanagement, but also face real expectations. At leading organizations, this looks like small, autonomous teams with direct access to decision-makers, not endless approvals from layers of management. Squarespace, for example, maintains “very few levels of management between teams and executives, allowing easy access to decision-makers,” and top cultures replicate this pattern.
Psychological safety for experimentation allows engineers to take technical risks without career consequences when experiments fail. Pixar’s “dailies” and “brain trusts” offer candid input without ego, a model that great engineering cultures emulate for code review, architectural feedback, and incident retrospectives.
Bias-aware performance evaluation addresses a common pain point: unclear or politically driven advancement. Companies like Zappos use skill tests as advancement criteria specifically to “avoid issues like favoritism and office politics.” For technical talent, this meritocratic approach matters because skills are measurable, but only if the organization commits to measuring them fairly.
Transparent compensation bands eliminate the exhausting dance of salary negotiation. When companies commit to base salary ranges upfront, engineers can focus on culture fit and technical alignment rather than wondering if they’re about to be lowballed.
Support for continuous learning is non-negotiable in fields where the landscape shifts every six months. This means conference budgets, internal research time, and cultures that treat learning velocity as a core competency. Zapier’s culture statement captures this directly: “Achieving these goals requires us to regularly learn and improve.”
In 2025–2026, engineers also navigate agentic AI tools integrated into daily workflows, hybrid work norms, and cross-functional AI/infrastructure/product teams that require strong communication cultures. The contrast is stark: metric-obsessed, meeting-heavy environments that fragment attention versus cultures that protect deep work and empower employees to build.
NVIDIA: High-Performance Culture Without Burning Out Engineers
NVIDIA’s role in the AI ecosystem from 2024–2026 is difficult to overstate. The company ships the GPUs powering nearly every major LLM, from H100s running inference at scale to the Hopper and Blackwell architectures enabling next-generation training. The CUDA ecosystem remains the backbone of machine learning infrastructure. NVIDIA AI Enterprise provides the software stack enterprises use to deploy models in production. This isn’t just a hardware company, it’s the infrastructure layer for AI.
What shapes NVIDIA’s internal culture is the recognition that sustainable performance requires more than sprints. NVIDIA balances intense, high-impact work; shipping new GPU architectures on aggressive timelines, developing cutting-edge AI platforms, with deliberate investment in employee development and long-term retention. The executive team understands that burning out the engineers who design silicon and write CUDA kernels isn’t a viable strategy when competitors are happy to hire exhausted talent.
For AI engineers, NVIDIA’s cultural strengths include cross-team collaboration between research and product development groups. Engineers working on compiler optimizations interact directly with researchers pushing new training techniques. Opportunities exist to contribute to open-source tooling that becomes an industry standard. Perhaps most importantly, the organization’s culture recognizes deep technical excellence as a legitimate career path, promotions don’t require becoming a manager.
Employee reviews from 2024–2025 consistently cite strong leadership vision, work on projects that visibly move the field, and teams that are competitive but supportive rather than zero-sum. The core values emphasize both individual excellence and collaborative problem-solving.
If you want to thrive in an NVIDIA-style culture, here’s what’s required:
Strong systems fundamentals: You need to understand hardware-software co-design at a deep level, whether that’s memory hierarchies, parallelism models, or performance profiling.
Comfort with ambiguity: Cutting-edge work means problems without clear solutions. You’ll iterate quickly and change direction when experiments reveal new information.
Willingness to collaborate across domains: The best work happens at interfaces, between researchers and engineers, between hardware and software teams, between internal tools and external developers.
ServiceNow: Enterprise Software, Human-Centered Culture
ServiceNow’s trajectory from workflow automation software to a globally deployed enterprise platform is a case study in scaling while protecting culture. By 2025, the company’s products run inside Fortune 500 organizations worldwide, orchestrating everything from IT service management to HR workflows. Repeated recognition in 2024 and 2025 “Best Places to Work” lists reflects how the company approaches both product and people.
For backend, platform, and ML engineers, ServiceNow offers something specific: the chance to see models and systems used in production environments by massive enterprises. When you build a recommendation system or optimize a workflow engine at ServiceNow, you’re not shipping to a test environment, you’re affecting how major organizations operate.
ServiceNow’s cultural traits distinguish it from both scrappy startups and bureaucratic enterprises. Structured career paths for individual contributors mean you can advance technically without being pushed into management. The company’s focus on mentoring and internal mobility creates professional growth opportunities beyond title inflation. Employee resource groups foster connection and inclusion, and an explicit ethics framework for AI-powered workflows, developed around 2023–2024, signals that responsible AI adoption is a company’s values commitment, not just marketing.
The way ServiceNow uses AI internally matters for engineers evaluating the culture. Generative AI powers ticket triage and workflow recommendations, systems that augment employees instead of replacing them. Engineers see this philosophy in their daily work: AI handles repeatable tasks so humans can focus on judgment, creativity, and relationship-building.
If you’re evaluating ServiceNow-style cultures, here’s your checklist:
Clear documentation norms: Can you find answers without interrogating five senior leaders?
Stable product roadmaps: Is the business model sustainable, or are you joining a pivot every quarter?
User-centric design practices: Do engineers interact with actual users, or is everything filtered through product managers?
Emphasis on reliability and SLAs: Is production stability celebrated, or is it expected without recognition?
How Hiring Is Changing: From Keyword Screens to Agentic AI and Signal-Rich Processes
A decade ago, applicant tracking systems filtered resumes with keyword matching. If your resume said “Python” and the job required “Python,” you moved forward. If not, you disappeared into the void. The process was crude, but at least engineers understood the rules.
Today’s hiring landscape operates differently. Agentic AI systems don’t just match keywords, they can screen, summarize, route, schedule interviews, pre-score resumes, flag inconsistent data, and nudge human decision-makers with recommendations. When designed carefully, these systems create faster, fairer processes. When built carelessly, they amplify existing biases and create new ones.
For candidates, the pain points with legacy AI hiring are familiar: opaque rejection reasons (or no communication at all), resume black holes where applications vanish without acknowledgment, bias amplification from models trained on historically biased hiring data, and assessments that test algorithmic puzzles rather than actual job tasks. The employee experience starts before day one, and broken hiring processes signal broken cultures.
Leading organizations like NVIDIA and ServiceNow approach responsible AI in hiring with human-in-the-loop decisions. Models can screen candidates and flag potential concerns, but humans make final calls. Communication with candidates remains transparent, people know where they stand in the process. Oversight mechanisms audit models for bias regularly, not as a one-time compliance checkbox.
This creates a new bar for marketplaces and platforms: proving that AI-driven hiring flows are bias-audited, transparent, and genuinely faster, not just more automated. The company cares about how candidates experience the process, not just how efficiently it processes applications.
How Fonzi AI Uses Responsible AI to Help Engineers Find Great Cultures Faster
Fonzi AI is a curated talent marketplace designed specifically for experienced AI/ML, full-stack, backend, frontend, and data engineers who want to land at companies with NVIDIA- and ServiceNow-caliber cultures. The difference: Fonzi focuses on high-growth startups and established companies actively building strong culture, not just the mega-caps everyone already knows about.
The platform uses AI as infrastructure, not as the decision-maker. Automation handles matching algorithms, fraud detection, and scheduling logistics so human recruiters can focus on what matters: understanding each candidate’s goals, technical strengths, and culture preferences. New employees joining through Fonzi aren’t processed by algorithms, they’re matched by a dedicated team that treats candidates as people, not database entries.
Here’s what Fonzi’s AI layer actually does:
Bias-audited evaluation frameworks: The scoring and matching systems undergo regular audits to identify and correct bias patterns, ensuring equal opportunities for all candidates.
Standardized skill profiles: Rather than keyword-matching resumes, Fonzi builds structured profiles that represent actual capabilities and experience levels.
Fraud and exaggeration detection: The system reviews candidates, flagging inconsistent claims, cloned profiles, and resume exaggeration, protecting both employers and honest candidates.
Transparent communication automation: Candidates always know where they stand. Status updates happen automatically, but the content reflects real human decisions.
The business model aligns incentives correctly: Fonzi charges employers an 18% success fee only when a hire happens. The platform is free for candidates. This means Fonzi benefits from quality matches and long-term success, not from churning maximum volume through broken processes.
Salary transparency is built into the structure. Companies commit to salary ranges before engaging with candidates through Match Day. Engineers avoid the frustrating experience of investing weeks in interviews only to receive a lowball offer.
To be clear: no offer is ever generated solely by a model. Humans review signals, conduct interviews, evaluate engineer portfolio work, and make hiring decisions. AI removes friction, it doesn’t replace judgment.
Inside Fonzi Match Day: A 48-Hour, High-Signal Alternative to Endless Interview Loops
Match Day is Fonzi’s answer to the months-long hiring funnels that exhaust both candidates and employers. The concept is straightforward: a time-boxed, 48-hour hiring event during which vetted companies and pre-screened engineers engage in a dense burst of interviews, technical conversations, and decisions.
The candidate journey starts with applying to Fonzi and being vetted on experience (typically 3+ years of professional work), technical stack, and role interests. Once approved, you’re invited to a specific Match Day aligned with your profile; AI/ML-focused events, infrastructure engineering events, or full-stack developer events. Fonzi’s concierge recruiters handle the logistics: scheduling, time zone coordination, interview prep reminders, and post-interview follow-up.
Before Match Day begins, companies commit to salary bands. This information is shown to candidates from the start. You know the compensation range before your first conversation, a forward-thinking approach that respects engineers’ time and embodies the salary transparency that defines healthy work-life balance conversations.
During the 48-hour window, you engage with a curated slate of 3–6 serious conversations instead of 20 low-signal recruiter screens. Each company has already expressed interest based on your profile. Each conversation is designed to assess mutual fit, technical alignment, culture preferences, growth opportunities, and team dynamics. Flexible schedules accommodate different time zones and work situations.
Example scenario: An LLM engineer in late 2025 joins a Match Day focused on AI infrastructure roles. Within 48 hours, they receive two offers, one from a growth-stage AI infrastructure startup building tooling for model deployment, one from a SaaS company developing internal copilots for customer support teams. Both companies are committed to salary transparency upfront. The engineer chooses the startup based on the team members' feelings of the interviews, the company’s mission alignment with their interests, and a more compelling equity package. Total time from first interview to signed offer: two days.
Comparing Traditional Hiring vs. Fonzi Match Day for AI Engineers
Dimension | Traditional Hiring for AI Roles | Fonzi Match Day Hiring |
Timeline | 4–12 weeks from application to offer, with unpredictable gaps and ghosting | 48-hour event with decisions happening in real-time |
Salary Transparency | Unclear or backloaded salary bands; compensation discussed late in process | Companies commit to salary ranges before talking to you |
Number of Interviews | 6–10+ rounds including redundant screens, take-home projects, and panel marathons | 3–6 focused conversations with decision-makers |
Signal Quality | Low-signal recruiter screens focused on resume keywords | High-signal technical conversations with hiring managers and future teammates |
Use of AI | Opaque ATS filtering; candidates don’t know why they’re rejected | Bias-audited matching with transparent candidate communication |
Candidate Support | Minimal; candidates navigate process alone | Concierge recruiter support with scheduling, prep, and follow-up |
Bias Controls | Often absent or superficial | Regular audits, structured evaluation frameworks, human oversight |
Outcome Clarity | Weeks of silence followed by generic rejection emails | Clear decisions within the 48-hour window |
How Top Cultures Use AI to Support (Not Replace) People
The fear that “AI will replace engineers and recruiters” misunderstands how leading organizations actually deploy these tools. At NVIDIA, ServiceNow, and the best AI startups, artificial intelligence supports deeper, more human work rather than eliminating it.
Concrete examples are everywhere: agentic AI assistants summarize incidents for SREs so they can focus on root cause analysis rather than reading through log dumps. Tools generate test cases for ML engineers, handling the tedious permutations while humans focus on edge cases that require judgment. Recruitment copilots draft outreach messages while human recruiters decide whom to pursue based on culture fit and career goals.
Fonzi’s philosophy follows this same pattern. AI handles repeatable tasks; profile parsing, fraud checks, time zone coordination, and scheduling optimization so the Fonzi team can invest time in 1:1 calls, career advice, and matching based on culture preferences. A candidate might specify they want an environment that “resembles NVIDIA’s research pod structure” or “ServiceNow-style product stability and documentation culture,” and a human tech recruiter uses that context to curate the right Match Day opportunities.
Responsible use of AI includes explicit bias audits, regular model retraining with fairness constraints, and human override mechanisms for edge cases. The technology empowers humans rather than replacing their judgment.
When you’re interviewing at prospective employers, ask how they’re using AI internally. Some questions to repurpose:
“How do AI tools support engineers in their daily work here? What tasks do they automate, and what remains human-driven?”
“What oversight exists for AI systems that affect hiring, performance evaluation, or customer-facing decisions?”
“How do you ensure AI adoption improves employee well-being rather than increasing surveillance or pressure?”
How to Evaluate Whether a Company’s Culture Matches NVIDIA or ServiceNow Standards
Job descriptions and careers pages are marketing. Engineers need practical ways to test culture claims during the interview process itself. Happy employees don’t just appear on Glassdoor reviews, they’re visible in how companies handle real-time interactions with candidates.
Here are concrete evaluation levers:
Ask about ownership boundaries: Who decides what gets built? How much autonomy do individual contributors have? At companies with a strong culture, senior leaders set direction, but engineers influence technical decisions daily.
Probe on-call load and incident review practices: How often are engineers woken up at 3 AM? What happens after incidents, blame games or honest feedback and continuous improvement? Post-mortems should feel like learning opportunities, not inquisitions.
Understand promotion criteria for ICs: Can you advance technically, or does growth require management? Ask for specific examples of engineers who reached principal or senior engineer levels without becoming managers. A mission-driven organization invests in professional development across multiple tracks.
Ask about technical debt: Does leadership acknowledge it exists? Is there dedicated time to address it, or is the roadmap packed exclusively with new features? A strong sense of engineering culture includes maintaining the systems you’ve already built.
Request real examples of how mistakes are handled: What happened the last time a deploy broke production? Were people supported, or were they blamed? Psychological safety shows up in how organizations respond to failure.
Test collaboration claims: Ask how cross-functional teams work on AI features that touch compliance, security, or customer data. If the answer is “we don’t really collaborate,” that’s a red flag regardless of what the careers page says.
Triangulate your findings: Glassdoor reviews from 2024–2026, LinkedIn posts by current engineers, and the consistency of answers between hiring manager, recruiter, and future teammates. Inconsistencies often reveal the gap between aspirational culture and current culture.
Fonzi’s vetting of employers, verifying runway, leadership quality, and interview process quality, offloads some of this research. But the platform empowers candidates to make their own judgment calls based on firsthand interactions.
Preparing for Interviews at High-Expectations, High-Support Cultures
Cultures like NVIDIA and ServiceNow are supportive, but they’re also technically demanding. The companies attracting dream team engineers expect candidates to show up prepared, but that doesn’t mean grinding random LeetCode problems for six months.
For AI/ML researchers and engineers: Review recent papers in your domain. Be ready to discuss experiment design, model evaluation methodology, and tradeoffs between different architectures. If you’ve published or contributed to open-source AI projects, prepare narratives around your specific contributions.
For infrastructure and platform engineers: Expect distributed systems design questions that test your understanding of reliability, observability, and scaling. Review CAP theorem tradeoffs, consistency models, and how systems fail gracefully under load. Be prepared to discuss production incidents you’ve resolved.
For LLM engineers: Prompt engineering, RAG architectures, and latency/throughput tradeoffs are table stakes. Be ready to discuss context window management, model evaluation for generative tasks, and how you’ve optimized inference costs in production environments.
Compile a portfolio of 2–3 deep projects with clear narratives: What was the problem? What constraints did you face? What architecture did you choose, and why? What tradeoffs did you make? What metrics demonstrated success? What would you change with hindsight? Use this portfolio heavily in interviews, don’t wait for interviewers to ask.
Fonzi helps candidates with resume rebuilding and profile positioning, ensuring alignment between written materials and live interview narratives. A dedicated team reviews profiles to surface strengths that candidates often understate.
For the interview day itself:
Clarify expectations with the recruiter beforehand: Ask for the interview loop in advance, who you’ll meet, what each session covers, and what success looks like.
Reflect culture-fit questions back to the interviewer: If they ask about your ideal team environment, ask them to describe their team’s actual dynamics. This tests mutual alignment and shows you’re evaluating them, too.
Ask about personal growth and learning: Questions about conference budgets, internal training, and time for exploration reveal whether continuous learning is real or aspirational.
Conclusion
The cultures that consistently win in 2025–2026 (think companies like NVIDIA and ServiceNow) tend to share the same core traits: technically ambitious work that genuinely moves the field forward, psychological safety that allows engineers to experiment and learn from failure, clear growth paths that don’t require a jump into management, and responsible AI adoption that augments human capability instead of sidelining it. These aren’t abstract values, they show up in how teams hire, how incidents are handled, how promotions work, and ultimately how engineers feel about showing up on Monday morning.
Engineers don’t have to sit through months-long, opaque hiring cycles to reach companies with that kind of cultural DNA. Fonzi’s 48-hour Match Day model offers a more focused alternative: salary transparency upfront, direct conversations with real decision-makers, and a structured process designed to respect your time while still maintaining high technical standards. If you’re an AI, ML, or infra engineer, joining Fonzi gives you access to multiple culture-forward companies in a compressed window with offers that reflect your real market value. And if you’re a hiring manager building that kind of environment, Fonzi connects you with vetted, experienced engineers who care about impact and culture as much as compensation without dragging the search out for months.
