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Director / Senior Director, AI

New York

Who we are

Doubleverify is the industry's leading media effectiveness platform that leverages AI to drive superior outcomes for global brands. By creating more effective, transparent ad transactions, DV strengthens the digital advertising ecosystem, ensuring a fair value exchange between buyers and sellers of digital media. Hundreds of Fortune 500 advertisers employ our unbiased data and analytics to drive campaign quality and effectiveness, and to maximize return on their digital advertising investments globally.

About the role

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.

What you’ll do

  • 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.

Your operating style

  • 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.

Our manager expectations (explicit)

  • 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.

Your qualifications

Required

  • 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.

Preferred

  • Experience in ad tech, marketing tech, or other high-scale data domains.

  • Hands-on background with cloud platforms (AWS/Azure/GCP), data platforms (e.g., Snowflake/Databricks/BigQuery), orchestration (Airflow/Kubernetes), and modern app stacks.

  • Exposure to agents, tool-use, and workflow automation; grounding in cost/performance tradeoffs for inference (latency, throughput, caching).

  • MBA or demonstrated business/financial acumen (TCO modeling, vendor contracts, ROI).

  • Experience running an internal “skunkworks” or innovation program.

Leveling guidelines

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.

What success looks like

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.

The successful candidate’s starting salary will be determined based on a number of non-discriminating factors, including qualifications for the role, level, skills, experience, location, and balancing internal equity relative to peers at DV. The estimated salary range for this role based on the qualifications set forth in the job description is between [$210,000.00 - $320,000.00]. This role will also be eligible for bonus/commission (as applicable), equity, and benefits. The range above is for the expectations as laid out in the job description; however, we are often open to a wide variety of profiles, and recognize that the person we hire may be more or less experienced than this job description as posted.

Not-so-fun fact: Research shows that while men apply to jobs when they meet an average of 60% of job criteria, women and other marginalized groups tend to only apply when they check every box. So if you think you have what it takes but you’re not sure that you check every box, apply anyway!

 

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