Product Lead

Job description:

As a Product Lead, you will work where Slate's AI meets real construction projects. You will spend a significant share of your time with customers, understanding how their teams work, designing solutions on the Slate platform, and implementing them alongside Slate engineers. You will also own the product roadmap and outcomes for your area, turning what you learn in the field into platform capabilities that benefit every customer.

This is a senior individual contributor role in technical product leadership. It suits someone who can lead a technical workshop with a customer's project team in the morning, build a working prototype with an AI coding assistant in the afternoon, and explain the cost and latency tradeoffs of a model choice to an executive the next day. You will report directly to the CTO.

Role & Responsibilities:

• Embed with customers and deliver solutions.
• Serve as the trusted technical and product partner for customer stakeholders, from project teams to executive sponsors.
• Lead technical discovery on site and remotely to understand customer workflows, data landscapes, systems and the outcomes that matter to them.
• Work hands-on with Slate engineering team members to design, configure and implement customer solutions, including data onboarding and mapping, integrations with customer systems (such as Procore, Autodesk Construction Cloud, Oracle Primavera P6 and ERP platforms), and tuning of AI workflows to the customer's context.
• Scope and run pilots and proofs of concept with clear success criteria, timelines and executive readouts.
• Stay engaged after go-live to drive adoption, resolve issues and measure business impact against agreed outcomes.
• Own the product.
• Own the vision, roadmap and outcomes for AI-powered capabilities in your area of the Slate platform, including agentic workflows, project intelligence, document and drawing understanding, and predictive insights.
• Turn patterns across customer deployments into platform capabilities, so one-off solutions become repeatable product.
• Decide where AI adds real value and where deterministic rules, better UX or data fixes are the better answer.
• Write precise specs covering expected model behavior, edge cases, failure modes and acceptance criteria.
• Build, prototype and validate.
• Use AI coding assistants and LLMs to build working prototypes, demo ideas to customers and validate feasibility before committing engineering capacity.
• Analyze product data, model outputs and customer data and build relatable solution.
• AI quality, evaluation and trust.
• Define and own the evaluation framework for your area with data science, including ground-truth datasets built from real construction artifacts (schedules, RFIs, submittals, specs, drawings, site imagery), quality metrics and release thresholds.
• Partner with engineering on prompt and agent design, retrieval and grounding quality, tool and API integrations (including MCP-style connectors), model selection, and cost, latency and accuracy tradeoffs.
• Build Intelligence with Accountability into every deployment through provenance and citations, human-in-the-loop review, permissions, audit trails, escalation paths and clear user control.
• Go-to-market and impact.
• Partner with customer success, solutions and sales teams on commercial pursuits, launches and enablement.
• Define and report on outcome metrics such as adoption, task completion, answer quality, automation success rate, time saved and customer business impact.
• Stay ahead of developments in foundation models, agent frameworks and construction technology, and bring them into customer conversations and the roadmap.

Desired Profile of the Candidate:

• 10+ years in technical product management, solutions architecture or forward deployed roles, with at least 3 years on commercially shipped B2B SaaS or enterprise products.
• Proven experience leading customer-facing technical work: discovery, solution design and implementation with enterprise customers, alongside engineering teams.
• At least 2 years building and deploying AI or ML-powered solutions, ideally including LLM-based features in production.
• Working knowledge of modern AI systems.
• Daily, hands-on use of AI coding assistants / LLM tools in your own work. You should be able to show prototypes, scripts or tools you have built with them.
• Strong technical fluency with APIs, data pipelines, cloud architecture (AWS or Azure), integrations and system design.
• Ability to turn ambiguous customer problems into clear solutions, requirements and measurable outcomes.
• Excellent written and verbal communication, with the presence to earn trust from customer executives and senior engineers alike.
• Willingness to travel internationally to customer sites.
• Bachelor's or Master's Degree in Computer Science, Engineering or a related technical field.

Strongly preferred:

• Prior experience as a forward deployed engineer, solutions architect, implementation lead or software engineer.
• Experience in AEC or construction technology, or a strong understanding of construction workflows such as scheduling, RFIs, submittals, change management, field execution, and cost and progress tracking.
• Familiarity with AEC data and platforms such as Procore, Autodesk Construction Cloud, Oracle Primavera P6, Revit, IFC/BIM and DWG. Experience with AEC data ontologies or knowledge graphs is a plus.
• Experience building evaluation frameworks, ground-truth datasets or benchmarks for AI systems.
• Exposure to computer vision products (for example site imagery or safety detection), time-series and IoT data, or digital twins.
• Experience delivering enterprise AI with security, privacy, permissions and governance requirements across multiple regions.

Qualitative Skills:

• Passion for developing robust software and writing maintainable code.
• Proven ability to work in a fast-paced, highly responsive agile team with rapidly evolving requirements and architectures.
• Excellent verbal and written communication skills.

How We Will Assess This:

Candidates in the final stages will complete a practical exercise based on a real construction workflow problem. You will prototype a solution using an AI coding assistant of your choice, then present it to us as you would to a customer, covering the solution design, evaluation approach, implementation plan, and what you would turn into product.

Skills

AI, LLM, Large Language Models, ML, Machine Learning, Product