Staff AI Engineer

Your Role & Mission

As a Staff AI Engineer on our AI Engineering team you will be responsible for building and productionizing advanced AI systems powered by Large Language Models (LLMs) and intelligent agents. You'll work on critical Apollo capabilities including our AI Assistant Autonomous AI Agents Deep Research Agents Conversational Assistant Semantic Search Search Personalization and AI Power Automation features that directly impact millions of users' productivity.

The mission of our AI teams is to leverage Apollo's massive scale data and cutting-edge AI to understand and predict user behaviors personalize experiences and optimize every stage of the customer journey through intelligent automation.

What You'll Be Working On

AI Assistant & Agent Systems

• Agent Architecture & Implementation : Build sophisticated multi-agent systems that can reason plan and execute complex sales workflows

• Context Management : Develop systems that maintain conversational context across complex multi-turn interactions

• LLM and Agentic Platforms : Build scalable large language model and agentic platforms that enable widespread adoption and viability of agent development within the Apollo ecosystem

• Backend Systems: Build back-end systems necessary to support the agents.

• AI features: Conversational AI Natural Language Search Personalized Email Generation and similar AI features

Classical AI/ML (Optional Focus)

• Search Scoring & Ranking : Develop and improve recommendation systems and search relevance algorithms

• Entity Extraction : Build models for automatic company keywords people keywords and industry classification

• Lookalike & Recommendation Systems : Create intelligent matching and suggestion engines

Key Responsibilities

• Design and Deploy Production LLM Systems : Build scalable reliable AI systems that serve millions of users with high availability and performance requirements

• Agent Development : Create sophisticated AI agents that can chain multiple LLM calls integrate with external APIs and maintain state across complex workflows

• Prompt Engineering Excellence : Develop and optimize prompting strategies understand trade-offs between prompt engineering vs fine-tuning and implement advanced prompting techniques

• System Integration : Build robust APIs and integrate AI capabilities with existing Apollo infrastructure and external services

• Evaluation & Quality Assurance : Implement comprehensive evaluation frameworks A/B testing and monitoring systems to ensure AI systems meet accuracy safety and reliability standards

• Performance Optimization : Optimize for cost latency and scalability across different LLM providers and deployment scenarios

• Cross-functional Collaboration : Work closely with product teams backend engineers and stakeholders to translate business requirements into technical AI solutions

Required Qualifications

Core AI/LLM Experience (Must-Have)

• 10+ years of software engineering experience with a focus on production systems

• 1.5+ years of hands-on LLM experience (2023-present) building real applications with GPT Claude Llama or other modern LLMs

• Production LLM Applications : Demonstrated experience building customer-facing scalable LLM-powered products with real user usage (not just POCs or internal tools)

• Agent Development : Experience building multi-step AI agents LLM chaining and complex workflow automation

• Prompt Engineering Expertise : Deep understanding of prompting strategies few-shot learning chain-of-thought reasoning and prompt optimization techniques

Technical Engineering Skills

• Python Proficiency : Expert-level Python skills for production AI systems

• Backend Engineering : Strong experience building scalable backend systems APIs and distributed architectures

• LangChain or Similar Frameworks : Experience with LangChain LlamaIndex or other LLM application frameworks

• API Integration : Proven ability to integrate multiple APIs and services to create advanced AI capabilities

• Production Deployment : Experience deploying and managing AI models in cloud environments (AWS GCP Azure)

Quality & Evaluation Focus

• Testing & Evaluation : Experience implementing rigorous evaluation frameworks for LLM systems including accuracy safety and performance metrics

• A/B Testing : Understanding of experimental design for AI system optimization

• Monitoring & Reliability : Experience with production monitoring alerting and debugging complex AI systems

• Data Pipeline Management : Experience building and maintaining scalable data pipelines that power AI systems

What Makes a Great Candidate

Production-First Mindset

• You've built AI systems that real users depend on not just demos or research projects

• You understand the difference between a working prototype and a production-ready system

• You have experience with user feedback iterative improvements and feedback systems

Technical Depth with Business Impact

• You can design end-to-end systems including back-end systems asynchronous workflows LLMs and agentic systems

• You understand the cost-benefit trade-offs of different AI approaches

• You've made decisions about when to use different LLM providers fine-tuning vs prompting and architecture choices

Evaluation & Quality Excellence

• You implement repeatable quantifiable evaluation methodologies

• You track performance across iterations and can explain what makes systems successful

• You prioritize safety reliability and user experience alongside capability

Adaptability & Learning

• You stay current with the rapidly evolving LLM landscape

• You can quickly adapt to new models frameworks and techniques

• You're comfortable working in ambiguous problem spaces and breaking down complex challenges

Our AI Impact at Apollo

Join a team that's already making significant impact:

• Our AI Assistant helps sales teams automate research scoring and outreach processes

• Assisted Prompting Mode allows users to leverage AI power-ups without being prompt engineering experts

• Our AI email assistant processes hundreds of thousands of words monthly for Professional plan user

• We help users 'book more meetings in less time by automating research scoring outreach & more with embedded AI sales assistants'

If you're looking for a place where your AI engineering work directly impacts millions of users where you can push the boundaries of what's possible with LLMs and agents and where your career can thrive in the AI-native future—Apollo is the place for you.

Application Instructions

To help us identify candidates with strong real-world AI engineering experience please answer the following five short screening questions directly in your application (2–5 sentences per response). Applications without answers to these questions will not be reviewed.