Stratus, deriving from the Latin term meaning 'layer', offers an advanced set of MEP-specific solutions that seamlessly layer across a contractor's entire workflow from design to fabrication to installation.Our team of seasoned industry experts, skilled technology leaders, innovators, and entrepreneurs understands that fabrication does not occur in isolation, and increasingly, it may not happen within your own fabrication shop.
Through close relationships with our customers - who include some of the most innovative and largest MEP contractors - we have developed a suite of Stratus tools to digitize, automate, and optimize piping, plumbing, sheet metal, and electrical contracting.Stratus provides the software layer an MEP contractor needs to optimize profits with true "Data-Driven Contracting." GENERAL DESCRIPTION The Director of Data and AI/ML owns the Stratus Intelligence Platform: the data, machine learning, and reasoning capabilities that make compounding intelligence real.
This role is accountable for delivering in the AI roadmap, ensure that AI workloads scale on the platform, the ML lifecycle that trains and serves models on it, and the learning and reasoning capabilities that turn data into customer value over time.This is a player-coach leadership role with both team-build and execution mandates.You 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.
The organizing principle is compounding intelligence: every solution, data points, deepens the data substrate, the models that learn from it, and the reasoning capabilities customers come to rely on.The Intelligence area is where Stratus builds its defensible AI moat.Three primary responsibilities
- Data and Intelligence platform. Stand up the canonical data layer, the ML platform (training, serving, MLOps), and the learning and reasoning capabilities (model lifecycle, feature stores, vector search, RAG architectures, eval frameworks). End of 2026 milestone: foundational data substrate baselined and ML workloads running on it in production.
- Team build-out. Hire and develop a high-leverage Intelligence team (Data, AI/ML, AVE. Build the team's craft, culture, operating rhythm and collaboration with other teams.
- Production-grade ML and AI. Stand up the trust patterns Stratus needs to ship AI workloads to customers with confidence: evaluation frameworks, observability for AI, drift detection, confidence scoring, and the operating posture that lets the platform team and customer success trust what we ship.
KEY RESPONSIBILITIES - Partner with the CTO, leadership to set the intelligence strategy and roadmap. Lead and own the execution.
- Build, hire, and develop the Intelligence team. Set the bar for craft and shape the operating cadence. Comfortable manage people and agents.
- Stand up the canonical data substrate that AI/ML workloads run cleanly against. Schema discipline, tenancy isolation, data contracts, lineage, governance.
- Stand up the ML and AI platform: model lifecycle, feature store, vector store, training and serving infrastructure, MLOps practice.
- Lead the learning and reasoning capabilities of the platform. RAG architectures, agentic data systems, knowledge graphs, and the patterns that let Stratus's data compound into platform intelligence.
- Develop and drive evaluation frameworks measuring model quality, agent reliability, drift, and platform effectiveness. Make AI workloads observable to engineering, product, and customer success.
- Partner with product on the AI use case portfolio. Translate business needs into ML and AI capabilities; translate ML and AI possibilities into product opportunities.
- Drive the build-versus-buy posture for the AI/ML stack. Favor proven solutions; build only what is differentiating.
- Set production readiness standards for AI workloads in close collaboration with the platform team.
- Engage directly with customer-facing teams and customers when needed to ground Intelligence decisions in real workflow problems.
- Mentor engineers and data scientists. Raise the technical bar through design review, code review, and technical coaching. Personally set the bar for AI-augmented practice.
QUALIFICATIONS - 10+ years of professional experience in AI/ML, data engineering, or data science, with 4+ years in formal leadership roles (Senior Manager, Director, or Head of) at a B2B SaaS or AI/ML platform company.
- Demonstrated track record of building and leading AI/ML, data engineering, or data science teams of 5-15 people from a small base.
- Deep technical credibility across the modern AI/ML stack: data platforms (Postgres, pgvector, MongoDB or equivalent), ML platforms (training, serving, MLOps), and generative AI (LLMs, embeddings, RAG, fine-tuning).
- Experience shipping production ML and AI workloads to enterprise customers with the trust patterns (evals, observability, drift, confidence) that come with it.
- Hands-on player-coach posture. Comfortable reviewing technical designs, joining architecture debates, and writing reference implementations when the work warrants it.
- Strong hiring track record. Has built a team in the AI/ML market within the last two to three years and knows what good looks like.
- Excellent written and verbal communication. Capable of explaining AI/ML strategy to engineers, product, executives, and customers.
- Strong cross-functional partnership instincts. Has worked closely with product, engineering, and customer-facing teams as peers.
- Experience with multi-tenant data architecture and the operational realities of serving ML and AI workloads to enterprise customers.
NICE TO HAVE - Experience in construction tech, MEP, BIM, AEC, or other CAD and engineering workflow domains (or strong willingness to ramp on the domain).
- Background in AI security and threat modeling (prompt injection, data exfiltration, agent abuse, tenant isolation for AI workloads).
- Experience with Azure-native AI architecture (Azure ML, Azure AI Foundry, AKS).
- Experience standing up a data platform or ML platform from early-stage to scale.
- Prior experience in a Series B or growth-stage company navigating the transition from product-market fit to scale.
- Background in regulated or enterprise sales motions where compliance, security, and SLA discipline are non-negotiable.
Benefits- Comprehensive and competitive health benefits plan
- Matching 401k contributions
- 20 days annual PTO
- Primarily remote work with occasional annual team onsites.
This is a remote role, but candidates must be based in the U.S.
Stratus, deriving from the Latin term meaning 'layer', offers an advanced set of MEP-specific solutions that seamlessly layer across a contractor's entire workflow from design to fabrication to installation.Our team of seasoned industry experts, skilled technology leaders, innovators, and entrepreneurs understands that fabrication does not occur in isolation, and increasingly, it may not happen within your own fabrication shop.
Through close relationships with our customers - who include some of the most innovative and largest MEP contractors - we have developed a suite of Stratus tools to digitize, automate, and optimize piping, plumbing, sheet metal, and electrical contracting.Stratus provides the software layer an MEP contractor needs to optimize profits with true "Data-Driven Contracting." GENERAL DESCRIPTION The Director of Data and AI/ML owns the Stratus Intelligence Platform: the data, machine learning, and reasoning capabilities that make compounding intelligence real.
This role is accountable for delivering in the AI roadmap, ensure that AI workloads scale on the platform, the ML lifecycle that trains and serves models on it, and the learning and reasoning capabilities that turn data into customer value over time.This is a player-coach leadership role with both team-build and execution mandates.You 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.
The organizing principle is compounding intelligence: every solution, data points, deepens the data substrate, the models that learn from it, and the reasoning capabilities customers come to rely on.The Intelligence area is where Stratus builds its defensible AI moat.Three primary responsibilities
- Data and Intelligence platform. Stand up the canonical data layer, the ML platform (training, serving, MLOps), and the learning and reasoning capabilities (model lifecycle, feature stores, vector search, RAG architectures, eval frameworks). End of 2026 milestone: foundational data substrate baselined and ML workloads running on it in production.
- Team build-out. Hire and develop a high-leverage Intelligence team (Data, AI/ML, AVE. Build the team's craft, culture, operating rhythm and collaboration with other teams.
- Production-grade ML and AI. Stand up the trust patterns Stratus needs to ship AI workloads to customers with confidence: evaluation frameworks, observability for AI, drift detection, confidence scoring, and the operating posture that lets the platform team and customer success trust what we ship.
KEY RESPONSIBILITIES - Partner with the CTO, leadership to set the intelligence strategy and roadmap. Lead and own the execution.
- Build, hire, and develop the Intelligence team. Set the bar for craft and shape the operating cadence. Comfortable manage people and agents.
- Stand up the canonical data substrate that AI/ML workloads run cleanly against. Schema discipline, tenancy isolation, data contracts, lineage, governance.
- Stand up the ML and AI platform: model lifecycle, feature store, vector store, training and serving infrastructure, MLOps practice.
- Lead the learning and reasoning capabilities of the platform. RAG architectures, agentic data systems, knowledge graphs, and the patterns that let Stratus's data compound into platform intelligence.
- Develop and drive evaluation frameworks measuring model quality, agent reliability, drift, and platform effectiveness. Make AI workloads observable to engineering, product, and customer success.
- Partner with product on the AI use case portfolio. Translate business needs into ML and AI capabilities; translate ML and AI possibilities into product opportunities.
- Drive the build-versus-buy posture for the AI/ML stack. Favor proven solutions; build only what is differentiating.
- Set production readiness standards for AI workloads in close collaboration with the platform team.
- Engage directly with customer-facing teams and customers when needed to ground Intelligence decisions in real workflow problems.
- Mentor engineers and data scientists. Raise the technical bar through design review, code review, and technical coaching. Personally set the bar for AI-augmented practice.
QUALIFICATIONS - 10+ years of professional experience in AI/ML, data engineering, or data science, with 4+ years in formal leadership roles (Senior Manager, Director, or Head of) at a B2B SaaS or AI/ML platform company.
- Demonstrated track record of building and leading AI/ML, data engineering, or data science teams of 5-15 people from a small base.
- Deep technical credibility across the modern AI/ML stack: data platforms (Postgres, pgvector, MongoDB or equivalent), ML platforms (training, serving, MLOps), and generative AI (LLMs, embeddings, RAG, fine-tuning).
- Experience shipping production ML and AI workloads to enterprise customers with the trust patterns (evals, observability, drift, confidence) that come with it.
- Hands-on player-coach posture. Comfortable reviewing technical designs, joining architecture debates, and writing reference implementations when the work warrants it.
- Strong hiring track record. Has built a team in the AI/ML market within the last two to three years and knows what good looks like.
- Excellent written and verbal communication. Capable of explaining AI/ML strategy to engineers, product, executives, and customers.
- Strong cross-functional partnership instincts. Has worked closely with product, engineering, and customer-facing teams as peers.
- Experience with multi-tenant data architecture and the operational realities of serving ML and AI workloads to enterprise customers.
NICE TO HAVE - Experience in construction tech, MEP, BIM, AEC, or other CAD and engineering workflow domains (or strong willingness to ramp on the domain).
- Background in AI security and threat modeling (prompt injection, data exfiltration, agent abuse, tenant isolation for AI workloads).
- Experience with Azure-native AI architecture (Azure ML, Azure AI Foundry, AKS).
- Experience standing up a data platform or ML platform from early-stage to scale.
- Prior experience in a Series B or growth-stage company navigating the transition from product-market fit to scale.
- Background in regulated or enterprise sales motions where compliance, security, and SLA discipline are non-negotiable.
Benefits- Comprehensive and competitive health benefits plan
- Matching 401k contributions
- 20 days annual PTO
- Primarily remote work with occasional annual team onsites.
This is a remote role, but candidates must be based in the U.S.
