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Based on the LIVE WEB DATA provided, here is what I can report on Coveo engineering careers:
Company Overview and Market Position
Coveo is a specialist in enterprise search, AI, and relevance technology. The company's careers page (https://www.coveo.com/en/company/careers) emphasizes their mission to help organizations "shape the future of search, data, and AI" through a relevance platform built for enterprise customers. In the competitive enterprise AI search market, Coveo competes alongside Glean (valued at $7.2 billion as of June 2025, per CNBC), Moveworks (acquired by ServiceNow for $2.85 billion), and Kore.ai (which raised $150 million in recent funding).
Current Engineering Roles
The live data shows Coveo maintains active engineering recruitment. A Senior Machine Learning Developer position is currently listed, focused on R&D work with applied scientists to "build reliable, scalable, and maintainable ML systems" (via ApplyBlast job posting). This role directly targets professionals experienced with machine learning infrastructure. Additionally, Coveo lists a Senior Solution Engineer - Commerce position in the United States (West region), emphasizing partnership with prospects on business and technical needs.
Required Experience for Search Infrastructure & ML Roles
The job descriptions indicate that ML and engineering positions at Coveo target candidates with:
The Senior ML Developer role specifically calls for engineers who can "support applied scientists" and "accelerate the development pipeline," suggesting Coveo values candidates who understand both the research side of machine learning and the operational demands of production systems. This aligns with broader enterprise AI hiring trends in 2026, where organizations are building "dedicated AI engineering teams" requiring both depth and systems thinking (per Murray Resources analysis in the live data).
Remote Work Policy
The live data provided does not explicitly state Coveo's remote work policy on the career pages indexed. However, the Senior Solution Engineer - Commerce role specifies "United States (West)" location, suggesting some positions have geographic restrictions. The lack of explicit "remote" or "fully remote" language in the visible job listings suggests Coveo may operate on a hybrid or office-first model for certain roles, though this cannot be confirmed definitively from the available data.
Market Context
Coveo operates in the high-growth enterprise AI search space. The market analysis in the live data identifies Coveo as one of "8 best AI Enterprise Search platforms in 2026" alongside Glean and others, with specific strength in "enhancement of search experiences" for enterprise customers. The salary data for comparable roles shows AI engineers in the United States earn an average of $184,757 annually, with agentic AI engineer roles reaching $216,000 per year (per 6figr.com and Built In salary data).
Research Limitations
The current live web data does not include detailed job descriptions, specific technical requirements (programming languages, frameworks, tools), or explicit remote policy documentation from Coveo's careers pages. To obtain complete information on specific ML recommendation engine experience, search infrastructure stacks (e.g., Elasticsearch, vector databases), visa sponsorship, and flexible work arrangements, direct application to Coveo's careers portal at https://www.coveo.com/en/company/careers/open-positions would be necessary.
Glean has emerged as the clearest growth story in the enterprise AI search space, with aggressive talent acquisition signaling major product and market expansion. The company raised $150 million in Series F at a $7.2 billion valuation in June 2025, according to CNBC and Business Wire reporting. Now, five hiring listings span backend engineering, frontend development, machine learning, and fullstack roles via Greenhouse job boards—all posted on Lightspeed Venture Partners and General Catalyst career pages, indicating investor-backed scaling.
Most significantly, Glean's strategic pivot from enterprise search tool to AI middleware layer represents expanded ambitions. In a TechCrunch Equity podcast interview published February 15, 2026, CEO Arvind Jain explained the company is "building the layer beneath the interface" for enterprise AI—a shift that explains the aggressive hiring for "Autonomous AI Agents" and "AI Assistant" product teams listed on Greenhouse. The company is currently recruiting a Senior Machine Learning Engineer specifically for "AI Assistant + Autonomous AI Agents," signaling product portfolio expansion beyond search.
LinkedIn data shows 493 active job openings at Glean in the United States, with 26 newly posted positions as of February 2026. The salary floor for AI Outcomes Manager roles reaches $150,000–$212,000 annually (per Lightspeed job board), reflecting competitive talent acquisition in a constrained market.
Moveworks, acquired by ServiceNow for $2.85 billion in 2025 (a 20x–25x ARR exit on $100M+ annual recurring revenue, per SaaStr analysis), is actively recruiting for "Agentic AI Systems" teams. Three distinct roles appear across ServiceNow's career portal and LinkedIn: Senior Machine Learning Engineer, Senior Software Engineer, and base-level Software Engineer positions, all targeting "frontier AI algorithms and architectures" and NLU (natural language understanding) platform expansion. The positioning suggests ServiceNow is retaining Moveworks as an autonomous division focused on agentic systems rather than consolidating it into legacy IT Service Management tools.
DevRev raised $100.8 million in Series A at a $1.15 billion valuation, achieving unicorn status, according to VentureBeat reporting. The company now lists 86 job openings globally, with roles including "Lead Engineer - Agentic AI" and "VP of Marketing," indicating aggressive go-to-market and product scaling. DevRev's focus on product-led customer support and development workflows positions it to compete directly with Glean and Moveworks for mid-market enterprise accounts.
Kore.ai, a conversational AI and agent platform, secured strategic growth investment led by AllianceBernstein Private Credit Investors with continued backing from earlier rounds (per CFOtech Australia and FinTech Futures). The company is scaling workforce capacity to support "enterprise-grade AI agents" across work, service, and process automation—a direct competitive positioning against Glean's expanding agent platform.
AI engineers command premium compensation, with agentic AI engineer salaries averaging $216,000 annually (per 6figr.com salary data for 2026). Base AI engineer salaries average $184,757 nationally, with additional cash compensation averaging $26,486 (Built In data). This talent premium explains why every growth-stage enterprise AI firm is hiking salary bands for ML engineers and senior backend engineers—a clear indicator that all players are fighting over scarce agentic AI expertise.
Glean's product pivot to middleware, combined with 493 open roles and $7.2 billion valuation, marks it as the most aggressive scaler in the space this quarter. Moveworks' post-acquisition integration, DevRev's unicorn transition, and Kore.ai's credit facility all point to sustained investor conviction in agent-first AI platforms. The talent war for agentic AI engineers—evidenced by $216K average salaries and simultaneous hiring by multiple billion-dollar competitors—will intensify through 2026.
Glean has become a dominant force in enterprise AI search and is now pivoting into something broader. According to TechCrunch's February 15, 2026 analysis, "The enterprise AI land grab is on — Glean is building the layer beneath the interface," with CEO Arvind Jain explaining the company's shift from an enterprise search tool to a middleware layer for enterprise AI. This strategic repositioning tells you what Glean values: engineers who think in systems, not just features. The company raised $150 million in Series F funding at a $7.2 billion valuation as of June 2025, per Business Wire and CNBC, meaning they're resource-rich and expanding aggressively. On their careers page at https://www.glean.com/careers, they're actively hiring across multiple engineering tracks: Backend, Frontend, Fullstack, and Machine Learning Engineer roles (particularly for Search Quality).
The machine learning roles on their Greenhouse board at https://job-boards.greenhouse.io/gleanwork/jobs/4605215005 emphasize "expert-level individual contributions and thought leadership." This is directional: Glean does not want mid-level engineers who execute sprints—they want architects who shape the direction of search and AI systems. Their search quality ML role (from https://job-boards.greenhouse.io/gleanwork/jobs/4006735005) explicitly states "engineers work on a range of systems across the stack," signaling they value full-stack technical depth, not narrow specialization.
Glean's positioning as an AI middleware layer means they're hiring for engineers who understand how enterprise data flows through systems. The company is competing directly with Moveworks (which was acquired by ServiceNow for $2.85 billion), Coveo, and Kore.ai—all companies with similar search and automation ambitions. If you're targeting Glean, you must demonstrate: (1) deep systems thinking around information retrieval and ranking, (2) comfort with large-scale data pipelines and LLM integration, and (3) experience shipping products that handle permission-aware search (a core Glean capability noted in Kore.ai's competitive analysis at https://www.kore.ai/blog/best-enterprise-search-software).
While the live data does not provide Glean's specific interview rubric, you can reverse-engineer expectations from their open roles. The Backend and Fullstack positions suggest they conduct deep systems design interviews—be prepared to discuss how you'd architect search ranking systems, query routing, and caching strategies under scale. Their ML roles will test your understanding of information retrieval metrics (precision, recall, NDCG) and how to improve them in production. Prepare concrete examples of shipping ML features end-to-end, not research projects.
The live data does not surface Glean's engineering blog or recorded talks by their technical leaders. This is a significant gap in what I can recommend. To position yourself, you should search for talks by Glean engineers at conferences like Databricks World, NeurIPS, or the Relevance and Information Retrieval conferences. The absence of heavily promoted blog content (unlike companies like Anthropic or Databricks) suggests Glean invests less in public evangelism and more in product velocity—a cultural signal in itself.
First, read the TechCrunch podcast transcript where Arvind Jain discusses the middleware shift. Second, study Glean's competitor Moveworks' technical approach (they published more publicly before the ServiceNow acquisition), which will familiarize you with the problem space. Third, on your resume, emphasize projects involving permission-aware systems, multi-source data integration, and shipping features with LLMs. Fourth, prepare to discuss how you'd measure and improve search relevance in an enterprise context where speed, accuracy, and security are co-equal.
Glean is hiring engineers, not researchers. They want builders who ship fast in a capital-efficient way.