Jobs at Exa (exa.ai)

Provide high-trust executive support to founders and C-suite: manage calendars, triage communications, coordinate meetings and recruiting, plan events and offsites, improve workflows, and handle ad-hoc operational tasks to keep the organization running smoothly.

Lead and grow the US East Forward Deployed Engineering team while remaining hands-on with technical customer integrations, onboarding, escalations, and architecture reviews. Own account outcomes including production adoption, consumption growth, retention, and expansion. Partner with sales on presales and handoffs, manage account health, develop customer-facing content, and relay product gaps to engineering and product teams.

Build and lead Exa’s global, AI-first 24/7 support organization. Hire and manage Support Engineers, establish tooling, SLAs, severity models, automations, and escalation processes, and oversee customer and engineering escalations. Use AI to resolve tickets efficiently, monitor account health and churn risks, partner with account teams, convert support trends into product improvements, and report on support performance. The role requires hands-on programming, API debugging, customer integration expertise, and technical leadership experience.

Provide around-the-clock technical support for Exa customers by troubleshooting APIs, SDKs, distributed systems, and integrations. Reproduce bugs, submit code fixes, escalate complex issues with detailed context, and own customer follow-up through resolution. Build AI-first support workflows, improve documentation and knowledge bases, manage ticket and SLA automation, coordinate follow-the-sun handoffs, monitor account risks, and convert recurring issues into product feedback and engineering fixes.

Lead the technical side of EMEA presales opportunities from discovery through technical win. Build tailored demos, sample applications, prototypes, workshops, and proofs of concept; evaluate performance, latency, quality, and cost; address security and architecture objections; and collaborate with account executives and engineers. The role also includes creating reusable presales assets, providing market feedback to Product, and handing customers off after close.

Own machine-learning signals and decision systems that determine which web pages to crawl, index, deduplicate, refresh, or exclude. Define quality and credibility objectives without established ground truth, build classifiers and rankers, validate improvements at web scale, and collaborate across crawling, indexing, and retrieval teams. Projects include page parsing, quality assessment, misinformation modeling, and semantic document deduplication.

Develop machine learning systems for large-scale web content understanding and search quality. Responsibilities include parsing web pages, classifying content, extracting metadata, evaluating quality and credibility, detecting misinformation and AI-generated content, deduplicating documents, designing supervision for unlabeled problems, and deploying accurate, cost-efficient models across the web.

Build and scale a distributed web crawler capable of processing more than 100 million pages daily. Responsibilities include optimizing crawl politeness and rate limiting, handling dynamic content and JavaScript rendering, addressing anti-bot measures, and developing intelligent crawl scheduling and prioritization algorithms. The role requires high-performance programming, web crawling expertise, and optimization of large-scale systems.

Own the design and development of a query engine retrieving hundreds of billions of claims across graph, vector, full-text, time-series, and geospatial backends. Responsibilities include storage and indexing, cost-based query planning, distributed execution, latency optimization, consistency during live updates and snapshots, and precision and completeness evaluation. The role requires deep distributed database or search-engine expertise and strong Go, Rust, or C++ skills.

Lead the technical presales process from discovery through technical win. Build tailored demos, sample applications, prototypes, workshops, and proofs of concept; evaluate Exa against alternatives on quality, latency, and cost; address architecture, security, and compliance objections; collaborate with account executives and forward-deployed engineers; and provide product feedback and reusable presales assets.

Own the technical sales cycle for Exa’s API in India, including customer discovery, solution design, demos, prototypes, POCs, technical evaluations, and objection handling. Partner with account executives, support expansion opportunities, advise customers on AI use cases, feed market insights to product and engineering, and develop regional playbooks. The role also supports onboarding and early implementations while representing Exa at technical events.

Own the full sales cycle for Exa’s AI search product, including prospecting, product demonstrations, customer strategy, contract negotiation, and closing. The role involves building the London go-to-market office, developing high-value customer relationships, collaborating with SDRs, maintaining pipeline momentum, meeting sales targets, and driving revenue growth in a fast-moving startup environment.

Build and maintain large-scale systems to extract, connect, retrieve, and reason over web knowledge. Implement petabyte-scale data pipelines, optimize for correctness/reliability/scale, and improve data quality to power factual QA and search.

Own the design and implementation of a high-scale query engine for hundreds of billions of claims. Responsibilities include storage layout, indexing, query planning, optimization, distributed retrieval across graph, vector, full-text, time-series, and geospatial backends, consistency, latency optimization, and correctness evaluation. The role requires deep distributed database or search infrastructure experience and strong proficiency in Go, Rust, or C++.

Own strategy, roadmap, metrics, and execution for products extending Exa’s search API. Partner directly with AI startups and enterprises to identify needs, unblock integrations, and turn customer pain points into scalable capabilities. Collaborate with engineering, marketing, sales, and field teams on launches, pricing, documentation, enablement, and adoption. Define success criteria across quality, coverage, cost, and speed while driving developer adoption and business outcomes.

Own the quality and completeness of Exa’s evaluation data for search models. Define evaluation metrics and success criteria, build localized feedback signals, acquire and manage external data vendors, and collaborate with ML researchers, data engineers, infrastructure engineers, product, and GTM teams to improve search model performance.

Leads Exa’s EMEA marketing strategy, budget, market prioritization, pipeline generation, sales alignment, product-led growth, developer programs, localized positioning, agencies, and team expansion. The role owns regional pipeline and revenue outcomes across the UK, Europe, Middle East, and Africa, combining enterprise account-based marketing with developer-focused campaigns, events, partnerships, and automated AI-enabled workflows.

Leads Exa’s Asia Pacific and Japan marketing strategy, budget, pipeline generation, go-to-market motions, developer marketing, account-based programs, sales partnership, localization, and regional events. The role manages agencies and partners, hires the APJ marketing team, and uses automation, campaign data, and pipeline metrics to prioritize markets and investments. Based in Sydney, it requires regular APJ travel and periodic trips to San Francisco.

Own the machine-learning signals and decisions governing web crawling and indexing at massive scale. Develop classifiers, rankers, and calibrated models to assess page quality, credibility, freshness, parsing success, duplicate content, and indexing value. Define ground truth for ambiguous problems, validate measurable search improvements, and collaborate across crawling, indexing, and retrieval teams to improve the quality and coverage of search results.

Develop large-scale machine learning systems for web content understanding and search quality. Responsibilities include parsing web pages, classifying content, extracting metadata, assessing quality and credibility, detecting misinformation and AI-generated content, deduplicating documents, designing supervision for unlabeled problems, and building efficient models that operate across the web in every language and format.