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New York, New York, United States
Hybrid
DoubleVerify is the leading independent provider of marketing measurement software, data and analytics that authenticates the quality and effectiveness of digital media for the world's largest brands and media platforms. DV provides media transparency and accountability to deliver the highest level of impression quality for maximum advertising performance. Since 2008, DV has helped hundreds of Fortune 500 companies gain the most from their media spend by delivering best in class solutions across the digital advertising ecosystem, helping to build a better industry. Learn more at www.doubleverify.com.
We are seeking an exceptional Director of Engineering to lead our PinAI team, DoubleVerify's strategic initiative in conversational AI and agentic systems. In this role, you will spearhead the development of an AI-powered conversational interface for Pinnacle, transforming how our Fortune 500 clients interact with and derive insights from their advertising data.
The PinAI platform leverages cutting-edge AI technologies including agentic AI architectures, Model Context Protocol (MCP), and advanced LLM capabilities to deliver intelligent, context-aware analytics through natural language interactions. This role is ideal for a visionary technical leader who is passionate about AI innovation, has deep experience in conversational AI systems, and possesses exceptional architectural, engineering, and people leadership skills.
Location: New York, NY (Hybrid)
Define and execute the technical vision and roadmap for PinAI/Pin Chat, aligning AI initiatives with DoubleVerify's strategic objectives
Lead the design and architecture of agentic AI systems that leverage Model Context Protocol (MCP) for scalable, extensible AI agent frameworks
Drive innovation in conversational AI experiences that transform complex analytics into intuitive, natural language interactions
Establish technical standards for AI/ML engineering, including evaluation frameworks, prompt engineering, and model performance optimization
Partner with Product, Data Science, and Architecture teams to translate business requirements into scalable AI solutions
Build, mentor, and scale a world-class team of AI/ML engineers, software engineers, and technical leads
Attract top-tier talent with expertise in LLMs, agent architectures, conversational AI, and distributed systems
Foster a culture of innovation, experimentation, and continuous learning in AI technologies
Provide hands-on technical guidance through architecture reviews, design discussions, and code reviews
Develop career paths and growth opportunities for team members across AI and engineering disciplines
Oversee the development of agentic AI systems using frameworks like LangChain, LangGraph, and custom agent architectures
Implement robust evaluation (evals) frameworks to measure and improve AI agent performance, accuracy, and user satisfaction
Design and build conversational AI interfaces that understand context, maintain conversation history, and provide intelligent responses
Architect solutions leveraging MCP (Model Context Protocol) to create modular, reusable AI components and tools
Integrate with Pinnacle's existing analytics infrastructure including Looker, data APIs, and authentication systems
Ensure production-grade quality through comprehensive testing, monitoring, observability, and reliability engineering
Collaborate with Product Management to define AI-powered features and user experiences
Partner with Data Science teams on model selection, fine-tuning, and prompt optimization strategies
Work with Platform Engineering to ensure scalable infrastructure for LLM deployments and agent orchestration
Engage with Security and Compliance teams to implement responsible AI practices and data governance
Coordinate with UX/UI teams to create seamless conversational interfaces
Define and track KPIs for AI system performance, including response accuracy, latency, user satisfaction, and engagement metrics
Implement MLOps best practices for model deployment, versioning, monitoring, and continuous improvement
Optimize costs associated with LLM API usage and infrastructure while maintaining quality
Establish incident response protocols and SLAs for AI-powered features
Drive continuous improvement through retrospectives, evals analysis, and user feedback loops
Experience:
10+ years of software engineering experience building production systems at scale
3+ years in engineering leadership roles, managing teams of 5-10+ engineers
Hands-on experience with AI/ML systems, particularly LLMs and conversational AI
Proven track record of delivering AI-powered products from concept to production
Technical Expertise:
Agentic AI & LLM Frameworks: Deep expertise in building agentic systems using frameworks like LangChain, LangGraph, AutoGPT, or similar agent architectures
Model Context Protocol (MCP): Experience with MCP or similar protocols for building modular AI agent components and tool integrations
Conversational AI: Strong background in designing and implementing conversational AI systems, dialogue management, and natural language understanding
Evaluation Frameworks: Expertise in building comprehensive evaluation (evals) systems for LLM applications, including accuracy metrics, hallucination detection, and human-in-the-loop feedback
LLM Technologies: Hands-on experience with major LLM providers (OpenAI, Anthropic, Google, etc.) and open-source models
Backend Engineering: Proficiency in Python, Java, Scala, or similar languages for building scalable backend systems
Cloud & Infrastructure: Experience with cloud platforms (GCP, AWS, Azure) and containerization (Kubernetes, Docker)
Leadership & Soft Skills:
Exceptional ability to architect complex systems while adhering to engineering best practices
Strong technical communication skills; ability to explain AI concepts to non-technical stakeholders
Experience building and scaling high-performing, diverse engineering teams
Proven track record of delivering ambitious projects on time with cross-functional teams
Strategic thinker with the ability to balance innovation with practical execution
Strong stakeholder management and influencing skills
Education:
BS/MS in Computer Science, Machine Learning, AI, or related technical field (PhD preferred)
Experience with Prompt Engineering, RAG (Retrieval-Augmented Generation), and fine-tuning techniques
Familiarity with agent evaluation tools like DeepEval, LangSmith, Weights & Biases, or custom eval frameworks
Knowledge of responsible AI practices, bias mitigation, and AI safety
Experience in AdTech, Analytics, or Business Intelligence domains
Contributions to open-source AI projects or published research in AI/ML
Experience with modern frontend frameworks (React, Angular) for AI interfaces
Familiarity with Looker, Snowflake, or similar analytics platforms
Track record of innovation and patents in AI/ML space
Opportunity to lead a cutting-edge AI initiative at a publicly-traded, industry-leading company
Work with Fortune 500 clients on transformative AI solutions
Collaborative, innovative culture with a focus on continuous learning
Competitive compensation including base salary, bonus, and equity
Comprehensive benefits package
Professional development and conference opportunities
Hybrid work flexibility
At DoubleVerify, we live by our core values of Innovation, Collaboration, Excellence, and Integrity. We're looking for a leader who embodies these values and will help our PinAI team deliver world-class AI solutions that transform how clients interact with advertising analytics.
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 $153,000 - $260,000 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!