Director/Senior Director of AI Innovation, DoubleVerify

• Proven experience managing a small engineering team (and desire/ability to grow managers)
• Track record delivering AI/ML or LLM-powered systems from concept to production (POCs → pilots → GA) in partnership with product and platform teams
• Strong grasp of modern AI stacks: Python; model orchestration, vector stores, retrieval patterns, evals; MLOps/LLMOps concepts (observability, drift, prompt/version management)
• Familiarity with security, privacy, and governance considerations for AI (data retention, PII controls, model/content safety, vendor risk)
• Ability to design experimentation & evaluation plans (offline metrics, synthetic and human evals, A/B tests) and make decisions from evidence
• Outstanding written & verbal communication; ability to create exec-ready narratives and technical docs

Desirable

• DoubleVerify is investing in practical, business-driving AI. We’re hiring a Director / Senior Director to lead our Skunkworks R&D team and drive cross-company AI adoption
• This leader will manage a small team of senior engineers, coordinate delivery of back-office AI initiatives across many stakeholders, and advise senior executives on strategy, governance, and transformation
• Lead the Skunkworks team to de-risk high-impact product/engineering workstreams through fast, rigorous R&D: proofs of concept, technical spikes, evaluation pilots, and recommendations (build/buy/partner)
• Own the AI roadmap for internal enablement, orchestrating initiatives across Data, Security, Legal/Privacy, IT, Product, Finance, and Operations (e.g., agentic workflows, knowledge retrieval, internal copilots, automation)
• Establish disciplined AI practices: experimentation frameworks, offline/online evals, A/B testing, prompt/memory/version control, guardrails, safety & red-teaming, cost/perf tracking, and observability
• Be an executive advisor on AI strategy: opportunity sizing, risk/controls, vendor landscape, TCO, and change-management; present clear recommendations and tradeoffs
• Scale the team and function: hire, mentor, and grow senior engineers and future managers; set goals, operating cadences, and SLAs
• Ship impact quickly: move from concept to pilot to production hand-off with clear success criteria and documentation
• Partner deeply with product & platform teams to accelerate the integration of AI components into existing systems at scale
• Champion compliance & trust: data governance, privacy-by-design, IP/PII handling, model/content safety, and vendor risk management
• Structured & data-driven: translates ambiguous problems into crisp hypotheses, milestones, metrics, and decision trees; builds execution plans with dependencies, owners, risks, and communication cadences across many stakeholders
• Excellent communicator: adapts to audiences from principal engineers to the C-suite; drives consensus among many stakeholders
• Business-savvy: connects technical choices to customer value and P&L; MBA or equivalent experience is a plus
• Builder-manager: leads senior engineers, grows the managerial track, and stays hands-on enough to unblock the team
• Lead a team of senior engineers with clarity and empathy; set crisp goals and hold the bar on quality
• Demonstrate previous success leading a small engineering team and growing the managerial track (hiring, mentoring, career paths, performance)
• Foster a culture of structure, measurement, and writing: clear docs, design reviews, and post-mortems
• We’re open to either level; we’ll calibrate scope, autonomy, and compensation accordingly
• Director, AI:
• Leads the Skunkworks team and portfolio of internal AI initiatives
• Influences cross-functional priorities; directly manages senior ICs
• Typical background: 8+ years in software/ML, 3+ years people leadership
• Senior Director, AI:
• Owns a broader multi-team portfolio and AI strategy for internal enablement; heavier exec interface
• Builds and scales a leadership bench; drives multi-quarter roadmaps and budgets
• Typical background: 12+ years in software/ML, 5+ years people leadership, including managers
• First 30-90 days
• Audit and rationalize the current Skunkworks portfolio; establish a single intake/prioritization funnel and shared evaluation rubric
• Land the AI delivery playbook (evals, guardrails, observability, cost tracking) and the cross-functional operating cadence (weekly standups; monthly QBRs)
• Ship 1-2 quick-win pilots with measurable value; produce a roadmap with business cases and alignment
• 6 months
• 4 internal AI workflows in production across at least two functions (e.g., Support, Sales Ops, Finance, People Ops) with clear SLAs and ownership in the destination teams
• Baseline + early ROI: e.g., 10-15% reduction in cycle times or hours saved for target processes; vendor TCO plan in place
• Executive reporting live for adoption, quality, risk, and cost
• 12 months
• 10 production-grade AI workflows operated by business owners, with Skunkworks owning the innovation pipeline and standards
• Company-wide evaluation & guardrail framework adopted (prompt/model versioning, safety tests, red-team results, incident playbooks)
• Material business impact: aggregate $X.XM annualized savings or revenue lift tied to shipped initiatives; measurable quality improvements (e.g., 20-30% ticket deflection, 15-25% faster close rates, 20% faster onboarding) depending on domains selected
• Org scaling: Skunkworks team grown, with a repeatable idea → pilot → production pipeline and well-defined handoffs
• Vendor & platform posture: consolidated contracts, right-sized model choices (open vs. proprietary), and documented migration paths to control cost/latency
• Leading & lagging indicators
• Leading: number of qualified opportunities in the pipeline, cycle time from idea → pilot, eval coverage, guardrail test pass rates, stakeholder NPS
• Lagging: time saved, error reduction, quality scores, ticket deflection, win rates, revenue/expense impact vs. baseline