As of 2026, Decagon is hiring for 216 open roles, with a significant focus on engineering and sales. This rapid hiring indicates not just growth, but also strategic investments in AI and machine learning, making it crucial for candidates and competitors to understand what this means for the tech landscape. Last refreshed in the Recruiting from Scratch Atlas database, these trends suggest a competitive hiring environment.
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HIRING BY DEPARTMENT, DECAGON
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Engineering ████████████████████ 92
Sales ████████░░░░░░░░░░░░ 38
Other ███████░░░░░░░░░░░░░ 31
Product ████░░░░░░░░░░░░░░░░ 18
Marketing ███░░░░░░░░░░░░░░░░░ 12
Operations ██░░░░░░░░░░░░░░░░░░ 10
SENIORITY MIX, DECAGON
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Senior ████████████████████ 88
Mid ███████████████░░░░░ 66
Staff ███████░░░░░░░░░░░░░ 30
Director ████░░░░░░░░░░░░░░░░ 19
Junior ██░░░░░░░░░░░░░░░░░░ 7
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| Metric | Count |
|---|---|
| Open roles | 216 |
| New roles in the last 29 days | 95 |
| Top departments | Engineering 92, Sales 38, Other 31, Product 18, Marketing 12, Operations 10, People 5, Finance 4 |
| Seniority mix | Senior 88, Mid 66, Staff 30, Director 19, Junior 7 |
| Top locations | San Francisco 122, New York 44, London 30, Toronto 8, Australia 6, Atlanta 2 |
| Last refreshed | 2026 |
Decagon's hiring strategy indicates a strong focus on engineering roles, which make up 42% of their total openings. This aligns with their position in the AI and machine learning sectors, where technical expertise is paramount. The mix of seniority levels reflects a balanced approach to building teams, which is critical for fostering innovation while ensuring mentorship and growth opportunities within the organization.
Decagon's hiring patterns reveal a lot about their strategic intent and the competitive atmosphere in the AI space. With 92 engineering roles open, it's clear that they prioritize technical talent, likely to support their AI initiatives. This focus is complemented by a significant sales effort, indicating they are not just building products but also scaling their go-to-market strategy.
The department breakdown shows a diverse hiring strategy. The 38 sales roles suggest a parallel investment in revenue generation, which is crucial as companies scale. Elad Gil highlights the need for companies to filter for systems thinkers who can move fast and build first-principles solutions. This aligns with Decagon's approach, as they likely seek candidates who can thrive in a rapidly evolving landscape.
From a geographic perspective, San Francisco dominates with 122 roles, followed by New York and London. This concentration indicates that Decagon is rooted in tech hubs, which is crucial for attracting top-tier talent. The velocity of their hiring-95 new roles in the last 29 days-signals urgency and a commitment to growth. This fast-paced hiring also reflects broader industry trends where the fastest companies often secure the best candidates. According to Greenhouse’s 2024 Hiring Benchmark, structured hiring processes lead to faster fills and higher acceptance rates. Decagon's efficiency in hiring could give them a competitive edge in attracting high-caliber talent.
Candidates looking at opportunities at Decagon should be aware of the competitive nature of the roles they are applying for. Given the company's focus on AI and machine learning, applicants need to demonstrate not only technical proficiency but also an ability to innovate and think critically about problems. The seniority mix indicates that Decagon values experience, but they also hire at mid and junior levels, suggesting they are willing to invest in talent development.
The interview process at Decagon is likely to be rigorous. Based on the Ashby 2024 Recruiting Benchmark, top-performing teams often conduct 2-3 interview stages for senior individual contributors, while longer processes can lead to a decline in candidate quality. Candidates should prepare for a structured interview process that evaluates both technical skills and cultural fit. This dual assessment is vital for Decagon as they seek to maintain a strong company culture while scaling rapidly.
Additionally, candidates should be mindful of the trade-offs involved in working for a company like Decagon. The mission-driven focus on AI and machine learning can be intense, and potential hires should evaluate whether they align with Decagon's vision. For those who thrive in fast-paced environments, this could be a rewarding opportunity.
For hiring managers and recruiters at companies like Intercom, Zendesk, or Sierra AI, competing with Decagon for talent requires a strategic approach. Decagon's emphasis on technical hiring and rapid recruitment cycles presents challenges for competitors. Claire Hughes Johnson notes that the highest cost in recruiting isn't the salary but a slow process that loses A-players to companies that decide faster. This means that to attract top talent, competitors must simplified their hiring processes and enhance their value propositions.
To effectively compete, companies need to highlight their unique selling points, such as company culture, work-life balance, or innovative projects. Candidates are increasingly looking for workplaces that align with their values, and companies should articulate how they stand apart from Decagon in these areas. Offering competitive compensation packages is essential, but it’s equally important to communicate the impact candidates can make within the organization and how they can contribute to meaningful projects.
Additionally, companies should consider implementing structured hiring practices that allow for quicker decision-making. This aligns with the Greenhouse 2024 Hiring Benchmark, which states that structured hiring can lead to faster fills. Competitors who optimize their hiring processes will be better positioned to attract talent before Decagon can secure them.
Decagon's talent inflow is telling. Based on the Recruiting from Scratch Atlas candidate database, 119 professionals at Decagon hail from a variety of notable feeder companies. The top sources include Bain & Company (7), Databricks (6), MongoDB (6), Google (5), and Epic (5). This distribution signals that Decagon is attracting talent from reputable organizations known for their rigorous hiring practices and high-performance cultures.
The presence of professionals from Bain & Company suggests a strong analytical and strategic mindset among employees, which is valuable in the AI field. Databricks and MongoDB indicate a focus on data-driven solutions, essential for any company operating in machine learning and AI. Google’s alumni likely bring innovation and a fast-paced approach to problem-solving, while Epic’s presence suggests a commitment to high-quality software development.
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TALENT MOVEMENT AT DECAGON, Recruiting from Scratch Atlas Database, 2026
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WHERE TALENT COMES FROM │ WHERE TALENT GOES
──────────────────────────────────┼──────────────────────────────────
Bain & Company ( 7) │ Wish ( 2)
Databricks ( 6) │ PromptQL ( 2)
MongoDB ( 6) │ Marin Software ( 2)
Google ( 5) │ AMPED ( 1)
Epic ( 5) │ v4c.ai ( 1)
──────────────────────────────────┴──────────────────────────────────
119 current + 115 alumni tracked in Recruiting from Scratch Atlas database
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This inflow of talent suggests Decagon is raising its hiring bar and aiming to build a high-performing workforce. Companies aiming to compete for the same talent should consider how they can attract candidates from these same feeder companies by showcasing similar values and career opportunities.
Looking at Decagon's alumni can provide insights into the competitive landscape. Of the 115 alumni tracked in our database, the top destinations after leaving Decagon include Wish (2), PromptQL (2), Marin Software (2), AMPED (1), and v4c.ai (1). This movement indicates that talent from Decagon often transitions to other growth-stage companies, suggesting a strong market for experienced professionals in the AI sector.
The trend of alumni moving to other emerging tech companies highlights the competitive nature of the industry. Companies seeking to attract this talent should understand that former Decagon employees are likely to carry valuable skills and experiences that are highly sought after. Recruiting efforts should emphasize the opportunities for growth and impact that candidates can have in their new roles.
At Recruiting from Scratch, we see a competitive landscape for technical talent, particularly in AI and machine learning. The demand for skilled professionals is exceptionally high, and companies must be prepared to meet this demand by offering competitive compensation and fast hiring processes. Based on our data from 300+ placements, we understand that candidates expect transparency in the hiring process and clarity on compensation.
For companies looking to attract talent like that at Decagon, it's crucial to understand the compensation expectations. Our findings show that salaries for technical roles in AI range from $185K at the 25th percentile to a median of $250K, reaching $300K at the 75th percentile. Companies need to align their offers with these expectations to remain competitive.
Moreover, the hiring process must be efficient. With an average time to hire of just 29 days compared to the industry average of 49 days, our approach emphasizes speed without compromising on quality. This is vital in a landscape where candidates are evaluating multiple offers, and delays can result in losing top talent.
By utilizing our Atlas platform and Spyglass sourcing extension, we proactively source and vet candidates, ensuring that we deliver pre-qualified talent to our clients quickly. This approach not only reduces time to hire but also enhances the quality of candidates presented to hiring managers.
If you are a Series B AI safety company competing with Decagon, reach out to Recruiting from Scratch. Our expertise in the technical hiring landscape can help you attract the right talent and build your team effectively.
Tell us about your open roles and we'll start sourcing within 48 hours.
