Apollo.io is the AI-native GTM platform used by millions of professionals worldwide, helping revenue teams automate workflows, personalize outreach, and convert prospects into customers. Backed by Sequoia and Bain Capital and valued at $1.6B+, Apollo is one of the fastest-growing SaaS companies in the world, shipping AI-first capabilities across assistants, agentic automation, search, and generation at massive scale.
We are entering a hyper-growth phase of AI innovation and are hiring a Director of AI Engineering to lead the strategy, engineering execution, and cross-functional integration of Apollo’s applied AI systems.
Your Role & Mission
As the Director of AI Engineering, you will own the end-to-end delivery of Apollo’s AI roadmap spanning autonomous agentic systems, LLM-powered workflows, search & relevance, personalization, and production-grade ML infrastructure. You will lead and scale a high-impact AI Engineering organization responsible for turning cutting-edge AI techniques into real, revenue-driving product capabilities used by millions.
This role sits at the intersection of Engineering, Product, Machine Learning, Growth, and GTM, requiring deep technical leadership, exceptional product intuition, and a track record of shipping AI systems that create measurable business outcomes.
You will define the long-term architecture for agentic AI at Apollo, elevate engineering excellence, mentor senior ICs and managers, and accelerate Apollo’s position as the AI-native leader in the go-to-market ecosystem.
What You’ll Own & Deliver
AI Systems Strategy & Roadmap
- Define the multi-year technical vision for Apollo’s AI stack, spanning agents, orchestration, inference, retrieval, and platformization.
- Prioritize high-impact AI investments by partnering with Product, Design, Research, and Data leaders to align engineering outcomes with business goals.
- Establish technical standards, evaluation criteria, and success metrics for every AI-powered feature shipped.
Agentic Systems & Applied AI Delivery
- Lead the architecture and deployment of long-horizon autonomous agents, multi-agent workflows, and API-driven orchestration frameworks.
- Build reusable, scalable agentic components that power GTM workflows like research, enrichment, sequencing, lead scoring, routing, and personalization.
- Own the evolution of Apollo’s internal LLM platform for high-scale, low-latency, cost-optimized inference.
AI Assistants, Search & Personalization
- Oversee model-driven experiences for natural-language interfaces, RAG pipelines, semantic search, personalized recommendations, and email intelligence.
- Partner with Product & Design to build intuitive conversational UX that hides underlying complexity while elevating user productivity.
Experimentation, Evaluation & Observability
- Implement rigorous evaluation frameworks, including offline benchmarking, human-in-the-loop review, and online A/B experimentation.
- Ensure robust observability, monitoring, and safety guardrails for all AI systems in production.
- Drive continuous model improvement through telemetry, feedback loops, and data-driven performance tuning.
Technical Leadership & Team Building
- Build and lead a world-class AI Engineering team of senior ICs, managers, and cross-functional collaborators.
- Foster a culture of experimentation, velocity, and pragmatic engineering, where speed and safety coexist.
- Mentor engineers on system design, LLM architectures, distributed systems, and applied ML best practices.
- Upskill the broader engineering org in AI-native development, tooling, and automation.
Cross-Functional Impact & Execution Excellence
- Collaborate closely with Data Science, Product, Design, and Research teams to ensure AI capabilities integrate seamlessly into the GTM user journey.
- Accelerate learning cycles by combining LLM experimentation, product iteration, telemetry insights, and engineering automation.
What We’re Looking For
Technical & Production Expertise
- 10–15+ years in software engineering, with significant leadership experience owning AI/ML or applied LLM systems at scale.
- Proven history shipping LLM-powered features, agentic workflows, or AI assistants used by real customers in production.
- Deep understanding of LLM orchestration frameworks (LangChain, LlamaIndex), RAG pipelines, vector search, embeddings, and prompt engineering.
- Expert in backend & distributed systems (Python strongly preferred) and cloud infrastructure (AWS/GCP).
- Strong experience with telemetry, observability, and cost-aware real-time inference optimizations.
Leadership, Strategy & Delivery
- Demonstrated ability to lead senior engineers, define technical roadmaps, and deliver outcomes aligned to business metrics.
- Experience building or scaling teams working on experimentation, optimization, personalization, or ML-powered growth systems.
- Exceptional ability to simplify complex problems, set clear standards, and drive alignment across Product, Data, Design, and Engineering.
- Strong product sense, ability to weigh novelty vs. impact, focus on user value, and prioritize speed with guardrails.
AI-Native Engineering Mindset
- Fluent in integrating AI tools into engineering workflows for code generation, debugging, delivery velocity, and operational efficiency.
- Comfortable balancing rapid experimentation with production rigor, ensuring features are safe, reliable, and measurable.
- Thrives in an environment where learning velocity, automation, and continuous improvement are core values.
What Will Make You Successful at Apollo
- You raise the bar on engineering leadership — setting standards that elevate execution, quality, and innovation across the org.
- You operate with ownership and urgency. Apollo ships fast, iterates fast, and expects leaders to turn ambiguity into scalable systems.
- You connect AI research to real product outcomes. Novelty matters less than moving business metrics materially.
- You excel cross-functionally. You influence Product, Desig, and Data with clarity, technical depth, and pragmatic decision-making.
- You thrive in AI-native environments. You use AI daily to accelerate engineering, improve workflows, and teach your teams to do the same.
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