{"id":14114,"url":"https://alion.io/job/lancedb-senior-support-engineer","title":"Senior Support 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":"B","score":75,"open_postings":5,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-09-30T05:45:00Z"}},"role":"Support","role_family":"Support","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":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"Grafana","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":"OpenTelemetry","optional":true},{"name":"Prometheus","optional":true}],"status":"live","first_seen_at":"2026-03-17T04:48:17Z","employer_posted_date":"2026-03-17","last_verified_at":"2026-10-01T03:59:16Z","board_verified":true,"closed_at":null,"days_open":198,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":198},"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\nWe're looking for a hands-on, technically strong Support Engineer who will be the bridge between our engineering team and enterprise users of LanceDB, helping our customers debug distributed databases built in Rust.\nWhat You’ll Do\nAs one of the early team members, build our support infrastructure and practices while handling customer cases:\n\nDevelop and maintain knowledge-base articles, runbooks, and support tooling that document common issues, best practices, deployment patterns, and performance tuning.\n\nContribute to metrics around support response-times, resolution times, customer satisfaction, and help build a scalable support organization as we grow.\n\nWork proactively: identify recurring issues, escalate product bugs or UX gaps, propose improvements in the support process, and advocate for the customer in the roadmap.\n\nServe as one of the primary technical points of contact for our customers: troubleshoot issues, respond to escalations, and guide customers through full lifecycle support for large-scale deployments of LanceDB.\n\nWork in close collaboration with our engineering and product teams to reproduce issues, debug root causes, propose remediation, and drive fixes or enhancements.\n\nDive deeply into distributed database internals: query execution, storage engine, indexing, sharding, replication, fail-over, and cloud orchestration (Kubernetes, serverless-style deployments).\n\nUse and contribute to Rust codebases: reproduce customer environments, inspect logs, build diagnostic tools, run instrumentation, apply patches and configuration changes.\n\nWhat We’re Looking For\nPlease do not apply unless you meet all of the criteria in this section.\n8+ years of professional experience in a support / operations / troubleshooting role in a distributed database or data infrastructure environment.\n\nDemonstrated experience with distributed database systems, cloud-native data platforms (AWS, GCP, or Azure), and Kubernetes or serverless deployment models.\n\nStrong knowledge of distributed systems concepts: sharding, replication, consensus, failure modes, resource contention, performance bottlenecks, and cloud-native orchestration (Kubernetes, containerization, autoscaling).\n\nDemonstrated experience with at least one of the following: vector/feature stores, analytics engines or big data systems.\n\nVery comfortable with reading logs and correlating them with source code, working with Grafana dashboards, and creating shell scripts or Python code to assist in debugging.\n\nExcellent customer-facing communication skills: you'll be working directly with high-value customers, so you must be comfortable explaining complex technical issues clearly, managing expectations, and advocating for the customer.\n\nStrong sense of ownership, urgency, correct prioritization under pressure, and ability to work closely with engineering teams to drive resolution.\n\nComfortable working in a fast-moving startup environment with high autonomy and evolving responsibilities.\n\nNice to Have\nProficiency in Rust: you should be comfortable reading, navigating, and debugging code; ideally you've built or debugged production-quality systems written in Rust.\n\nFamiliarity with storage engine internals, indexing/data layout, performance tuning, and profiling tools.\n\nContributions to open-source projects (especially Rust), or experience writing diagnostic tools, debuggers, or instrumentation.\n\nExperience deploying and monitoring systems in large-scale production environments: logging/observability (e.g., Prometheus, Grafana, OpenTelemetry), alerting, SLOs/SLAs.\n\nPrevious experience creating new runbooks, selecting and configuring support ticketing systems, and defining incident response processes.","description_format":"text","description_chars":4207,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Data Warehouses & Lakehouses","Databases & DBMS","Vector Search & RAG"],"lifecycle":[{"event":"open","at":"2026-03-17T04:48:17Z"}],"liveness":{"score":8,"band":"cold","label":"Long shot","p_open":1,"p_active":0.284,"p_room":0.28,"age_days":197,"expected_fill_days":42,"reasons":["conf:3","win:tail","crowd:"],"computed_at":"2026-09-30T05:45:00Z"},"pay":{"stated_usd_annual":250000,"is_top_pay":true},"html_url":"https://alion.io/job/lancedb-senior-support-engineer","json_url":"https://alion.io/job/lancedb-senior-support-engineer.json","meta":{"generated_at":"2026-10-01T04:56:30Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":4521,"day_limit":5000,"remaining_today":479,"minute_limit":60,"resets_at":"2026-10-02T00:00:00Z"}}}