Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Senior Software Developer, Search
- Remote from
- Canada
- Salary
- CAD 107k–165k / yr
- Department
- Software Engineering
- Employment
- Full Time
- Experience
- Director
- Published
- Apply before
- 12 Dec 2026
- Listing views
- 23
- Application actions
- 0
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About this role.
MaintainX is a leading mobile-first work execution platform for industrial and frontline teams. More than 15,000 customers, including McDonalds, Michaels, Cintas, Redimix, and U.S. LBM, use MaintainX to cut unplanned downtime and run better operations, across 16 million managed assets and 92 million completed work orders.
In August 2026 MaintainX became part of Autodesk, joining Autodesk Operations Solutions, the organization unifying Autodesk’s operations platform alongside Tandem, FlexSim and Fusion Operations. Autodesk’s strategy is to converge design, make and operate into one continuous lifecycle: design an asset, build it, run it, then feed what you learn running it back into the next design. Autodesk had design and make. Operate is the phase that tells you what actually happened, and it is ours.
About the Role
We’re starting a new Search team to make it easier for users find exactly what they need across MaintainX. You’ll play a critical role in designing, building, and scaling search infrastructure that reflects the unique shape of the MaintainX domain. This is a zero-to-one role. You’ll be scoping ambiguity, shipping usable search early, and evolving it with fast feedback loops. If you enjoy building systems from scratch, obsess over performance and relevance, and want your work to power daily decisions across industries, we’d love to talk.
What You’ll Do:
Design and build MaintainX’s end-to-end search and retrieval platform — from indexing pipelines and ranking systems to AI-facing query layers.
Partner with product and domain experts to model search across core entities like work orders, assets, and procedures, ensuring high-relevance results.
Ship fast, iterate fast, launch early versions, gather feedback, and improve rapidly.
Operate and optimize distributed systems, ensuring reliability, performance, and scalability under real-world load.
Drive architectural decisions, balancing correctness, performance, and long-term maintainability.
Own integrations between search systems and upstream data sources.
Explore semantic search, vector retrieval, and AI-enhanced relevance using modern ML tooling.
On-call duties (currently business hours)
About You:
Hands-on experience designing, building, and operating production-grade search systems end-to-end — including indexing, query execution, relevance tuning, and infrastructure — using technologies such as Elasticsearch, OpenSearch, Lucene, Solr, or similar.
Understanding of embeddings, chunking, and retrieval strategies.
Familiarity with search-specific tuning: analyzers, BM25, shard sizing, cluster health, and relevance optimization.
Comfort taking systems from implementation to deployment and production operation, working across backend services, data pipelines, and integration points as needed.
An expert level of English, both spoken and written, is required, as the individual will need to lead platform engineering managers across multiple teams, present technical priorities to executive stakeholders, and align with engineering leaders outside Québec on a daily basis.
Nice to Have:
Experience evaluating and improving search relevance using both qualitative analysis and quantitative metrics.
Experience shipping AI-powered or LLM-integrated backend systems
Comfortable using modern AI tools like GPT, Copilot, or Cursor to boost productivity and streamline development.
Understanding of multi-tenancy and data security, including ACLs, field-level access controls, and secure index partitioning.
Experience building observability and success metrics into systems to monitor performance, usage, and impact.
Our mission is to keep the physical world running. Factories, fleets, hospitals and campuses stay up because the people who maintain them have tools worth using. That is what we build.
Compensation and benefits. Base pay is one part of the package. Depending on the role, compensation may also include commission, an annual bonus and equity. Benefits differ by country. For roles in the United States, Autodesk’s benefits are described at benefits.autodesk.com. For roles in Canada and other countries, the plan differs on health coverage, retirement and leave, and your recruiter will walk you through it.
Belonging. We take pride in a culture where everyone can thrive. More at autodesk.com/company/global-belonging. More on where this is going: Autodesk CEO Andrew Anagnost on building the future of connected operations, and AOS SVP Stephen Hooper on welcoming MaintainX to Autodesk.
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Sample interview questions
I would begin by defining the entity schemas, search use cases, access-control model, and relevance metrics. I would build resilient ingestion pipelines that normalize source data into purpose-built indexes, then expose a query service with filtering, ranking, highlighting, and monitoring. I would launch a focused lexical-search version first and evolve it through observed user behavior and feedback.
I combine qualitative review of representative queries with quantitative measures such as click-through, reformulation, zero-result rates, and task completion. I tune analyzers, field boosts, synonyms, filters, and BM25 parameters against an evaluated query set. Changes should be tested safely, measured against a baseline, and monitored after release.
I monitor indexing throughput, query latency, heap pressure, shard balance, disk growth, replication health, and error rates. Shard sizing and lifecycle policies should match data volume and query patterns, while capacity planning must account for growth and recovery scenarios. I also establish alerting, backup and restore procedures, and runbooks for common incidents.
I would treat vector retrieval as an incremental capability rather than a replacement for proven lexical search. After validating embedding quality, security boundaries, latency, and evaluation data, I would test hybrid retrieval that combines lexical and semantic signals. Results should be measured against real user tasks, with a fallback path to maintain predictable behavior.
Authorization must be enforced before results are returned, using tenant isolation and document- or field-level access controls aligned with the upstream source of truth. I would ensure indexing pipelines propagate ACL changes reliably and test for cross-tenant leakage. Auditing, least-privilege service access, and security-focused monitoring would be part of the operational design.
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