The team map
Gamma carries a $2.1 billion valuation, has crossed $100 million in annual recurring revenue, and has been profitable since 2023 — rare air for a generative-AI startup. Thirty roles sit open as of August 2026, a net increase of four in the prior 28 days. This guide maps who Gamma hires, what they pay, how the interview loop works, where the work happens, and which traits let people stay.
Engineering dominates the org chart. Eighteen of the 30 open roles sit in engineering, spanning a taxonomy that reads like a map of the modern AI stack: AI Engineer, UI Engineer, Research Engineer, Security Engineer, plus software-engineer titles segmented by domain: Backend, Data Systems, Frontend, Full Stack, Growth, Platform, Trust & Safety, and two Distributed Systems tracks (infrastructure and Trust & Safety). A Site Reliability Engineer role and an Engineering Manager, Growth round out the slate. First-party board data shows these roles carrying salary bands typically between $125,000 and $310,000, with Research Engineer and Engineering Manager, Growth reaching $180,000 to $340,000.
The go-to-market engine is the second-largest hiring surface. Seven GTM roles split between San Francisco and New York: Account Executives, Customer Success Managers, Sales Managers in both cities, plus a GTM Special Projects role in San Francisco. Marketing carries four openings: AI Creative Strategist, Creative Director, Growth Marketing Manager, and Senior Lifecycle Marketing Manager — signaling a brand motion that blends creative craft with growth loops. Design has a single Senior Product Designer role. Product has one Senior Product Manager, B2B. Data has an AI Data Scientist. Customer Experience lists a Technical Support Engineer. Finance seeks a Senior Accountant and a Senior Tax Manager. Operations has an Executive Assistant. Two additional roles fall under "other" in the aggregate data.
Seniority skews early: 24 of the 30 openings carry no explicit level, five are tagged senior, and one is a director-level slot. That distribution suggests Gamma is building breadth across the IC ladder while keeping leadership layers thin.
The company describes its culture as warm, a little quirky, and fueled by curiosity, with four stated values: don't be boring, stay scrappy, move with urgency, raise the bar. Those values function as a filter. The engineering roles, especially Research Engineer, AI Engineer, and the Distributed Systems tracks, demand depth in model serving, low-latency infrastructure, and the messy integration work that turns a demo into a product. The GTM and marketing roles require operators who can sell a new category, not an established one. The lone Product Manager role is explicitly B2B, hinting at the revenue motion ahead.
Gamma isn't hiring for a single product line. It's staffing a platform play: the creative layer for modern communication. The team map reflects that ambition.
Pay runs high and narrow
Gamma's compensation data comes straight from the company's live postings on the Zero G Talent board (31 salaried roles tracked as of the latest ingest). The aggregate band runs $125k to $310k with a median of $300k, Zero G Talent's board's data shows, though individual listings stretch beyond the top of that range. Six recent San Francisco postings illustrate the spread:
| Role | Location | Salary range (USD/year) |
|---|---|---|
| Research Engineer | San Francisco | $180,000 – $340,000 |
| Engineering Manager, Growth | San Francisco | $250,000 – $325,000 |
| Software Engineer, Trust & Safety | San Francisco | $180,000 – $310,000 |
| Software Engineer, Distributed Systems – Trust & Safety | San Francisco | $180,000 – $310,000 |
| Software Engineer, Growth | San Francisco | $180,000 – $310,000 |
| UI Engineer | San Francisco | $180,000 – $310,000 |
The Research Engineer ceiling at $340k, as Zero G Talent's board reported, and the Engineering Manager band topping at $325k, per Zero G Talent's board's data, sit above the board's stated $310k max, suggesting the aggregate band captures a wider set of levels than these six senior-leaning listings. The $125k floor likely reflects junior or non-engineering roles not shown here: support, operations, or early-career tracks that don't appear in the current engineering-heavy feed.
All six roles are based in San Francisco. Gamma's careers page emphasizes a San Francisco hub, and the board data shows no remote or multi-location tags on these postings. That concentration matters: Bay Area cost structure pushes bands higher than distributed teams at comparable startups. The median landing at $300k — just $10k shy of the posted band ceiling — signals a team weighted toward senior ICs and leads rather than a broad pyramid.
Equity and benefits don't appear in the board rows, but the company's public metrics imply meaningful grant pools. Candidates should ask recruiters for the current option refresh cadence and strike-price methodology; those details rarely make public postings but move total compensation materially.
The Trust & Safety cluster, three distinct titles at identical $180k–$310k bands, reveals how Gamma structures pay around product risk surfaces rather than pure leveling. A Distributed Systems engineer and a generalist Trust & Safety engineer share the same range, suggesting the premium attaches to the domain, not the specialty. Growth-track roles mirror that band, while UI Engineering, often compensated below backend at peer companies, sits at parity here.
Engineering Manager, Growth carries a $70k higher floor than the IC bands, reflecting the management multiplier Gamma applies. That $250k entry point, per the board data, aligns with late-stage Series C or early Series D benchmarks in the Valley, consistent with a $2.1B valuation.
For applicants, the takeaway is clear: Gamma pays at the top of the market for senior individual contributors in San Francisco, with a narrow band that compresses negotiation room at the high end but offers transparency at the low end.
Inside the interview loop
Gamma (the AI presentation company) has not published its interview loop. Publicly available interview guides describe BCG GAMMA (Boston Consulting Group's data-science division), a separate entity. Dataford aggregates 11 candidate reports for Gamma AI and rates interview difficulty 4.3 out of 10, with Python the most-tested topic. Glassdoor hosts 10 interview questions and 10 reviews posted anonymously by Gamma candidates. Beyond those data points, no detailed stage-by-stage process is documented. Candidates should ask recruiters directly for the current structure.
Where the work happens
Gamma's verified footprint is a software hub in San Francisco. Every salaried role on that board lists San Francisco: Research Engineer, Engineering Manager for Growth, multiple Trust & Safety and Distributed Systems engineers, Growth engineers, and a UI Engineer. The band runs $180k–$340k, with a board median of $300k across 31 postings. That concentration suggests a single office or a tight Bay Area cluster rather than a distributed-first model. Roughly one in sixteen openings carries a remote-eligible tag; the rest cluster in San Francisco and New York (the latter for GTM roles only).
The careers page frames the mission as building tools for imagination so anyone can share ideas beautifully and quickly — without needing design or comms expertise — which aligns with a product-centric team that ships fast and iterates in public.
Separate entities share the Gamma name. An aerospace manufacturing operation holds Nadcap approvals for heat treat, chemical processing, and nondestructive testing, capabilities that matter when parts fly on the world's most prestigious aircraft programs. Its certifications describe the work environment: qualified furnaces, controlled chemistries, and inspection cells that feed directly into prime-contractor supply chains. The research does not name this facility's city, and it does not appear on that board.
GTI Soft, based in Westmont, IL, appears in a benefits sheet with an on-site gym called out for that location. GTI Soft's engineering blogs reveal physics-based simulation for hybrid powertrains, hydrogen fuel-cell propulsion for aviation, marine LNG carriers, second-life battery integration into data centers, and virtual validation of lithium-ion battery management systems. The blog cadence (roughly weekly through mid-2026) indicates an active R&D staff. If Gamma and GTI Soft share a corporate parent or brand, the Illinois location is where simulation kernels and digital-twin pipelines get stress-tested against FEV, maritime, and aviation programs. No public filing confirms the relationship.
What the research shows for the AI presentation company: no remote-first policy, no satellite network, no return-to-office mandate. The San Francisco postings carry no "remote" tag. The Westmont benefits line says "varies by location," implying more than one site has a distinct package. The aerospace side, by nature of Nadcap, cannot be remote. Applicants should assume physical presence at the relevant site unless a specific role states otherwise; for the manufacturing and simulation roles, that presence is the product.
Who lasts
Gamma describes itself as an AI-first company built from scratch around large-language-model workflows. Its product decisions reinforce what the organization values: a card-based, web-native canvas that expands as you type, a design assistant driven by chat, and real-time collaboration that treats presentations as living documents rather than static decks. That product philosophy, the mission described on the careers page, is the clearest signal of who succeeds inside the company.
The roles Gamma has posted in San Francisco cluster around three technical spines: core AI/modeling, distributed systems at consumer scale, and trust-and-safety infrastructure. The salary bands and the fact that the company hit $50 million ARR in under a year indicate a pace that rewards engineers who can ship production-grade ML features without waiting for a platform team to pave the road.
Glassdoor aggregates — 78% would recommend, 3.5/5 work-life balance, 3.6/5 culture, 3.2/5 career opportunities as of the latest scrape — suggest a culture that is generally liked but not cushy. The gap between culture (3.6) and career opportunity (3.2) is a tell: people enjoy the environment but recognize that advancement comes from impact, not tenure. That aligns with a startup that added four net roles in a 28-day window, growth that creates leadership vacuums faster than HR ladders can fill them.
Candidates who thrive tend to share three traits visible in the product and the hiring slate:
1. Comfort operating at the model-product boundary.
Gamma's stack leans on fine-tuned GPT-class models for clarity and structure, but the differentiator is the UX layer: slash-command menus, one-click restyling, live embeds of Figma prototypes and Power BI dashboards. Engineers who treat the model as a component they can prompt, evaluate, and wrap in deterministic UI (rather than a black box they merely call) map directly to the Research Engineer and UI Engineer profiles the company is buying.
2. Distributed-systems fluency with a consumer-grade latency bar.
The "cards behave like web pages" claim means every keystroke, embed, and collaborator cursor syncs in real time across browsers. The Distributed Systems – Trust & Safety and Growth roles imply a backend that must serve millions of simultaneous sessions while enforcing policy, analytics, and A/B experiments. People who have debugged tail latency in WebSocket fleets or built conflict-free replicated data types for collaborative editors will recognize the problems; those who haven't will struggle to ramp before the next sprint.
3. Product intuition that respects non-technical users.
The design assistant ("make this punchier") and the AI that summarizes pasted essays into bite-sized bullets exist because Gamma's core user is not a designer. Engineers who have shipped features behind feature flags, instrumented funnel metrics, and iterated based on session replays, not just Jira tickets, tend to survive the "growth" and "trust & safety" interview loops. The company's own marketing admits the AI "lacks burstiness and perplexity… writes like a polite corporate drone"; the people who stay are the ones who see that as a UX problem to solve, not a model limitation to accept.
The hiring data doesn't spell out a checklist, but the pattern is consistent: Gamma hires builders who think in product loops, not ticket queues. If your default is to open a PR that adds a slash command, measures adoption, and rolls back when a prompt regresses, the culture will feel familiar. If you need a spec, a sprint plan, and a QA sign-off before you touch prod, the 3.2 career-opportunity rating is your warning.
Over a billion people make presentations monthly; the tools they use haven't changed in decades. Gamma's bet is that the team shipping the replacement will look less like a consulting engagement and more like a factory floor that runs on taste, and that the people who last are the ones who treat the model as clay, not oracle.
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