Job Description
Staff AI Engineer
Apollo.io
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.
Apollo.io
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.
About Apollo.io
Apollo.io combines a buyer database of over 210M contacts and powerful sales engagement and automation tools in one, easy to use platform. Trusted by over 160,000 companies including Autodesk, Rippling, Deel, Jasper.ai, Divvy, and Heap, Apollo has more than one million users globally. By helping sales professionals find their ideal buyers and intelligently automate outreach, Apollo helps go-to-market teams sell anything. Celebrating a $100M Series D Funding Round 🦄
Industry: Software Development