Stratus

Account Executive

• Own full-cycle sales in your territory
• Self-source pipeline through multi-channel outbound efforts
• Run sharp discovery with operational buyers
• Partner with Solutions Engineers for demos
• Keep Salesforce clean and forecast-ready

Commercial Account Executive

Product Manager

• Own the electrical portfolio across Works and Stratus, spanning BIM/CAD interoperability through to prefab and fabrication workflows.
• Support model, assembly, and package interoperability between Stratus and BIM authoring tools.
• Manage electrical prefabrication and fabrication workflows - how assemblies, packages, and shop work move from the model into production and out to the field.
• Write PRDs and specs precise enough that engineering can build against them without back-and-forth on CAD-specific behavior or on fabrication-specific behavior.
• Conduct frequent direct customer conversations and site visits to validate that "the model is right" and "the shop can build it".
• Coordinate with engineering on integration and data-model architecture decisions where Stratus's data model meets Autodesk's.
• Track launch and adoption of features in this scope, partnering with PMM and CS to ensure integrations and fabrication workflows are utilized.
• Develop a point of view on where AI tooling can accelerate product work.

Product Manager – Electrical BIM/CAD, Fabrication

Performance Marketing

• Own the demand generation strategy and roadmap across paid, organic, ABM, email/lifecycle, and events channels
• Build and manage a multi-channel pipeline engine, from top-of-funnel awareness through MQL/SQL handoff
• Partner with sales leadership to define and refine ideal customer profile, account scoring, and lead routing
• Design and run account-based marketing programs targeting a defined list of high-value target accounts
• Manage and optimize paid media spend (search, social, retargeting) with a clear eye on CAC and pipeline efficiency, not just clicks
• Build lifecycle and nurture programs that move leads through the funnel with minimal manual lift
• Establish and own the demand gen reporting cadence: pipeline sourced/influenced, funnel conversion rates, channel efficiency, and forecast contribution
• Collaborate with content and product marketing to ensure campaigns are grounded in a strong narrative and speak to the actual buyer, not generic B2B messaging
• Continuously test, measure, and iterate; this role has a bias toward shipping and learning over perfecting a plan in a doc
• Help build the systems and processes (not just the campaigns) that let demand gen scale as the team grows

Senior Demand Generation Marketer

AI Engineer

• Build the customer-facing AI layer for Stratus — the production agent systems, tool integrations, and evaluation infrastructure that let our products reason over MEP fabrication data and act on a contractor's behalf.
• Design and ship multi-agent workflows, build the tool and context layer that connects agents to Stratus data, and own the guardrails, evals, and observability that make those systems safe and trustworthy in front of customers.
• Build and maintain the Revit-side integrations that feed the AI layer — the add-ins and publishing paths that move design and fabrication data out of our customers' Autodesk Revit environment and into Stratus.
• Use AI-assisted development tooling (Claude Code, Cursor, Copilot, etc.) as a first-class part of the dev loop — writing tests for AI-generated changes and exercising clear judgment about when AI output is ready to ship.
• Collaborate with product managers, designers, and customer-facing teams to scope, design, and ship — grounding technical decisions in real design and fabrication workflows.
• Contribute to agile workflows, ensuring flexibility and responsiveness to evolving project needs.
• Share knowledge and raise the engineering bar through code review and pragmatic best practices.

Senior Software Engineer – AI, Revit

Customer Success Manager

• Own a portfolio of commercial mid-market accounts end-to-end, serving as the primary point of contact and trusted advisor.
• Build and maintain multi-threaded relationships across champion, economic buyer, and executive levels.
• Develop and execute tailored success plans aligned to each customer’s strategic goals and key business outcomes.
• Proactively monitor account health, engagement signals, and product usage to identify risk and opportunity early.
• Drive on-time renewals across your book of business, managing the full renewal cycle from 90-day forecast through execution.
• Maintain a rolling renewal forecast with high accuracy and visibility in Salesforce or equivalent CRM.
• Identify and mitigate churn risk through proactive outreach, business reviews, and escalation management.
• Achieve gross revenue retention (GRR) targets and net revenue retention (NRR) targets.
• Identify and qualify expansion opportunities (upsell, cross-sell, seat expansion) within your existing customer base.
• Lead commercial conversations with customers, partnering with your aligned Account Executive to close expansion deals.
• Develop account growth plans that outline the path to expansion over a 12–24 month horizon.
• Conduct structured Quarterly Business Reviews (QBRs) with key stakeholders, presenting ROI, product usage insights, roadmap alignment, and forward-looking success plans.
• Build executive sponsorship programs and escalate appropriately when executive relationships need reinforcement.
• Monitor product adoption milestones and proactively intervene when customers fall behind adoption benchmarks.
• Serve as the voice of the customer internally, advocating for product enhancements, escalating support issues, and sharing market feedback with Product and Engineering.
• Collaborate with Sales, Marketing, and Implementation on account strategy, case studies, and customer advocacy programs.

Customer Success Manager, Commercial Mid-Market

Data Scientist

• Owner of AI and data product initiatives end to end.
• Build agentic AI and LLM-powered features, from concept to shipped product.
• Turn large construction data sets into commercial products.
• Create crisp specs and PRDs for engineering execution.
• Maintain a working feedback loop with design partners.
• Partner with PMM, sales, and CS for feature adoption.

Product Manager – AI and Data Products

Artificial Intelligence

• Owns the Stratus Intelligence Platform: the data, machine learning, and reasoning capabilities that make compounding intelligence real.
• Accountable for delivering in the AI roadmap, ensuring that AI workloads scale on the platform, and the ML lifecycle that trains and serves models.
• Set the Intelligence strategy in close partnership with the CTO, build the team that delivers it, and stay close enough to the technical work to make hard architectural calls when they arise.
• Stand up the canonical data layer, the ML platform, and the learning and reasoning capabilities.
• Hire and develop a high-leverage Intelligence team (Data, AI/ML, AVE).
• Set the bar for craft and shape the operating cadence.
• Stand up the trust patterns needed to ship AI workloads to customers with confidence: evaluation frameworks, observability for AI, drift detection, confidence scoring.

Director, Data – AI/ML

Backend Engineer

• Design, build, and ship features across our Revit add-ins and Revit-integrated desktop applications that connect our customers' Autodesk design environment to the Stratus platform.
• Use AI-assisted development tooling (Claude Code, Cursor, Copilot, etc.) as a first-class part of the dev loop — writing tests for AI-generated changes and exercising clear judgment about when AI output is ready to ship.
• Write production C#/.NET code against the Revit API: custom commands, panels, external events, document and transaction management, and integration with Autodesk model data.
• Own and improve the publishing add-in that exports data from Revit into the Stratus Core application — including data mapping, fidelity, validation, and the error handling that keeps exports trustworthy.
• Interface with the Stratus open API and understand our data export pipeline — including how data flows through our Azure ServiceBus and other backend services — to build and troubleshoot the integrations that move data out of Revit.
• Solve the hard problems of desktop add-in development — Revit version compatibility, performance inside large models, the API threading model, and graceful degradation when the host environment misbehaves.
• Build and manage the deployment of these desktop applications — installers (MSIs), versioning, and update mechanisms across customer environments.
• Investigate and fix customer-reported bugs across the desktop applications, including the failure modes specific to Revit add-ins.
• Collaborate with product leadership, engineering teams, and customer-facing teams to scope, design, and ship — grounding technical decisions in real design and fabrication workflows.
• Implement and maintain automated testing for the desktop applications, and advocate for incremental, reliable delivery.
• Stay current on the Autodesk Revit API and the broader Autodesk Platform Services ecosystem, and bring that judgment to how we build.

Senior Revit API Developer

Data Engineer

• Own our canonical data architecture — the schema, contracts, tenancy, and governance.
• Make production data AI-ready: well-modeled, contract-enforced, lineage-tracked, and drift-detectable.
• Own the canonical data model — the normalized definition of the core business objects shared across our products.
• Define the multi-tenant data architecture: tenancy isolation, data residency posture, and per-tenant cost attribution.
• Lead staged modernization toward the right mix of stores and patterns.
• Own the architectural direction of the data pipeline and lake / lakehouse layer.
• Drive hands-on prototypes, reference implementations, and in-repo guardrails.
• Partner with database engineering on production data health while owning long-term architectural direction.

Senior Data Architect – Hands on

• Own the architectural vision and technical roadmap for Intelligence (data, learning, reasoning) and Agentic interfaces, workflows
• Design and evolve agent-based workflows, enabling reasoning-capable, adaptive, multi-step systems with strong trust patterns (grounding, evals, confidence scoring)
• Develop and drive evaluation frameworks measuring model quality, agent reliability, drift, and platform effectiveness
• Make AI workloads observable to engineering and product
• Architect for simplify the core, customize at the edge
• AI/ML capabilities are opinionated at the center, with foundations for marketplace or platform-exchange patterns at the edge
• Architect agentic UX and enable just-in-time interfaces
• Build evaluation frameworks: model quality, agent reliability, drift detection, eval harnesses, observability for AI workloads
• Set engineering standards through working reference implementations, in-repo templates, and patterns AI/ML teams can copy
• Lead the polyglot AI stack: vector search, embeddings pipelines, RAG architectures, agent frameworks, model lifecycle, MLOps
• Drive AI-augmented engineering practice
• Personally set the bar
• Engage directly with customer-facing teams and customers to ground AI architectural decisions in real workflows
• Mentor engineers; raise the technical bar through code review, design review, and technical coaching.

Principal/Senior AI/ML Architect

• Build foundation models and generative AI tools alongside a team of technologists.
• Design and build agentic workflows — multi-agent orchestration (e.g., CrewAI, LangGraph, AutoGen), tool use, multi-step planning, and human-in-the-loop checkpoints — to automate complex engineering tasks.
• Establish evaluation, guardrails, and failure-mode analysis for agent systems to ensure they are safe, reliable, and production-ready.
• Develop scalable data pipelines for diverse data sources used in production ML systems, including BIM, CAD, and infrastructure design data.
• Work with large-scale, multi-modal datasets — including text and geometric data — to design novel preprocessing, augmentation, analysis, and content understanding.
• Transform unstructured infrastructure and design data into representations suitable for machine learning.
• Lead cross-functional collaboration with ML Research Scientists and Engineers to align data formats with downstream training and fine-tuning of LLMs.
• Apply deduplication, normalization, and validation techniques to ensure high-quality data in production environments.
• Architect and optimize pipelines for scalability, reproducibility, and cloud deployment.
• Mentor junior engineers and provide technical guidance on complex data challenges.
• Drive technical decision-making and influence best practices across the team.
• Perform requirements analysis with senior stakeholders, ensuring technical solutions meet both immediate project goals and long-term research objectives.
• Communicate findings and technical insights through quantitative analysis, visualizations, and clear documentation.
• Contribute to agile workflows, ensuring flexibility and responsiveness to evolving project needs.
• Participate in technical planning and roadmap development.

Principal AI Engineer

• Own product strategy and roadmap for an assigned pillar
• Manage the full product lifecycle from define to measure
• Coordinate with peer PMs for cross-pillar features
• Partner with PMM, sales, and CS for successful launch and adoption
• Communicate effectively with executives and customers