{"id":262770,"url":"https://alion.io/job/lancedb-senior-customer-success-engineer","title":"Senior Customer Success Engineer","company":{"id":4364,"name":"Lancedb","domain":"lancedb.com","url":"https://alion.io/company/lancedb","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":89,"open_postings":8,"ghost_share":0,"stale_share":0.25,"repost_share":0,"time_to_fill_p50_days":73,"computed_at":"2026-10-30T05:48:23Z"}},"role":"Customer Success","role_family":"Customer Success","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":180000,"max":250000,"currency":"USD","period":"year","gross":null,"usd_annual":250000},"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"GCP","optional":false},{"name":"Kubernetes","optional":false},{"name":"LanceDB","optional":false},{"name":"Midjourney","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"Runway","optional":false},{"name":"Rust","optional":false},{"name":"World Models","optional":false},{"name":"Feature Store","optional":true},{"name":"Grafana","optional":true},{"name":"Incident Management","optional":true},{"name":"OpenTelemetry","optional":true},{"name":"Prometheus","optional":true}],"status":"live","first_seen_at":"2026-07-10T15:40:32Z","employer_posted_date":"2026-07-10","last_verified_at":"2026-10-30T19:10:36Z","board_verified":true,"closed_at":null,"days_open":112,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":110},"description":"About LanceDB\nAI advances at the speed of its research, and research moves at the speed of its data. LanceDB is the AI-native Multimodal Lakehouse: one system where a researcher curates petabytes of video, audio, and every signal derived from them with a few lines of Python, and the next training run starts as fast as the next idea. Customers like Runway, Midjourney, and Netflix build the future of AI on LanceDB, from frontier and world models to robots and autonomous vehicles.\nAbout the Role\nAs the Senior Customer Success Engineer, you will be a trusted advisor to our most strategic customers. You'll combine deep technical expertise with outstanding communication and relationship-building skills to ensure customers achieve success in deploying and scaling LanceDB across production workloads.\nYou'll guide customers from onboarding through adoption and expansion - while flexing seamlessly into pre-sales or technical support capacities as business needs require. You'll serve as the connective tissue between our customers, product, and engineering teams, driving continuous improvement in both the customer experience and the product itself.\nWhat You’ll Do\nPartner closely with customers to design, deploy, and optimize LanceDB in production environments, ensuring reliability, scalability, and performance for distributed, cloud-native workloads.\n\nLead technical onboarding and architecture reviews; provide best-practice guidance on system configuration, query optimization, and integration patterns.\n\nProactively identify adoption barriers, troubleshoot complex distributed-system issues, and coordinate with product and engineering teams to drive timely resolutions.\n\nOwn customer success metrics: deployment time, usage growth, retention, and satisfaction. Build dashboards and track health across accounts.\n\nDevelop and deliver technical enablement: create sample code, automation tools, and documentation to accelerate customer outcomes.\n\nServe as the customer's technical advocate internally - communicating feature requests, influencing roadmap priorities, and improving developer experience.\n\nCollaborate cross-functionally with sales engineering (for technical evaluations, proofs-of-concept, and demos) and support engineering (for escalations and issue triage).\n\nContribute to internal tooling, runbooks, and playbooks that will form the foundation of LanceDB's future customer success organization.\n\nHelp to shape processes, tooling, and team culture as we scale customer success and post-sales engineering.\n\nWhat We’re Looking For\n10+ years of professional experience in technical roles such as post-sales engineering, customer success, solutions architecture, or technical support, ideally within the data infrastructure or distributed systems space.\n\nProven track record supporting or deploying distributed database systems or large-scale cloud-native data platforms (e.g., high-availability, multi-region, and horizontally scalable environments).\n\nStrong proficiency in Rust and Python - able to read, debug, and write production-grade code in both languages.\n\nDeep understanding of distributed systems concepts: sharding, replication, consensus, partitioning, failure recovery, and performance tuning.\n\nExperience deploying and managing workloads on Kubernetes or other container orchestration frameworks, and familiarity with cloud environments (AWS, GCP, Azure).\n\nExceptional communication and presentation skills: able to engage directly with customers' engineering leaders, architects, and executives with credibility and empathy.\n\nStrong problem-solving ability, coupled with a customer-first mindset and the ability to operate autonomously in fast-moving, ambiguous environments.\n\nWillingness and ability to flex across functions - including pre-sales engineering, technical support, and post-sales enablement - as needed by the business.\n\nNice to Have\nPrevious experience as a founding or early member of a customer success or solutions engineering function at a high-growth startup.\n\nHands-on experience with vector search, feature stores, or AI-native data systems.\n\nContributions to open-source projects (especially in Rust or Python) or experience authoring developer-facing technical content.\n\nFamiliarity with modern observability stacks (Prometheus, Grafana, OpenTelemetry) and incident management best practices.\n\nExperience designing or leading enterprise architecture workshops or technical 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