Staff Machine Learning Engineer
About Apollo
Apollo.io is the leading go-to-market platform for revenue teams, trusted by over 500,000 companies and millions of users globally. We provide sales and marketing teams with easy access to verified contact data for over 210 million B2B contacts and 35 million companies—plus the tools to engage and convert them all in one place.
As an AI-native company, we are redefining how businesses drive pipeline and revenue. Backed by top-tier investors like Sequoia Capital and Bain Capital Ventures, and recently valued at $1.6B, Apollo is one of the fastest-growing SaaS companies in the world.
Your Role & Mission
We are looking for a Staff Machine Learning Engineer to join our growing Intelligence team. You will lead mission-critical initiatives that power ML-driven user experiences across search, recommendations, content generation, scoring, and more. Your role is to push Apollo forward as an AI-native product, helping us create intelligent, personalized, and highly automated features that scale.
You will collaborate closely with engineers, product managers, and data scientists to build machine learning systems that enhance Apollo’s ability to guide our users to value—and drive the future of our AI platform.
Responsibilities:
Design, build, evaluate, deploy and iterate on scalable Machine Learning systems
Drive end-to-end ML initiatives—problem definition, data exploration, modeling, productionization, and monitoring.
Understand the Machine Learning stack at Apollo and continuously improve it
Lead development of intelligent features powered by LLMs, embeddings, ranking models, and semantic search.
Guide platform architecture decisions and contribute to foundational ML infrastructure (e.g., feature stores, MLOps).
Work cross-functionally to define AI-first product experiences and rapidly iterate toward user impact.
Mentor and uplevel engineers across the org, influencing engineering best practices and technical direction.
Champion the use of AI internally to drive engineering and operational efficiency.
Required Qualifications:
8+ years of experience building and scaling machine learning systems in production environments.
Strong experience with LLMs and embeddings (e.g., fine-tuning, prompt engineering, vector databases).
Hands-on experience with Python and modern ML libraries such as PyTorch, TensorFlow, HuggingFace, or Scikit-learn.
Experience with cloud infrastructure (e.g., GCP), orchestration (Airflow), and experimentation platforms (e.g., mlflow, Databricks).
Excellent collaboration and communication skills—can influence across product and engineering teams.
Proven impact shipping ML-driven features in B2B SaaS products or enterprise platforms.
Preferred Qualifications:
Bachelors, Masters, or a PhD in Computer Science, Mathematics, Statistics, or other quantitative fields or related work experience
Experience with retrieval-augmented generation (RAG), search infrastructure, or recommendations at scale.
Exposure to GTM, marketing tech, or sales enablement domains in a B2B setting.
Staff Machine Learning Engineer
About Apollo
Apollo.io is the leading go-to-market platform for revenue teams, trusted by over 500,000 companies and millions of users globally. We provide sales and marketing teams with easy access to verified contact data for over 210 million B2B contacts and 35 million companies—plus the tools to engage and convert them all in one place.
As an AI-native company, we are redefining how businesses drive pipeline and revenue. Backed by top-tier investors like Sequoia Capital and Bain Capital Ventures, and recently valued at $1.6B, Apollo is one of the fastest-growing SaaS companies in the world.
Your Role & Mission
We are looking for a Staff Machine Learning Engineer to join our growing Intelligence team. You will lead mission-critical initiatives that power ML-driven user experiences across search, recommendations, content generation, scoring, and more. Your role is to push Apollo forward as an AI-native product, helping us create intelligent, personalized, and highly automated features that scale.
You will collaborate closely with engineers, product managers, and data scientists to build machine learning systems that enhance Apollo’s ability to guide our users to value—and drive the future of our AI platform.
Responsibilities:
Design, build, evaluate, deploy and iterate on scalable Machine Learning systems
Drive end-to-end ML initiatives—problem definition, data exploration, modeling, productionization, and monitoring.
Understand the Machine Learning stack at Apollo and continuously improve it
Lead development of intelligent features powered by LLMs, embeddings, ranking models, and semantic search.
Guide platform architecture decisions and contribute to foundational ML infrastructure (e.g., feature stores, MLOps).
Work cross-functionally to define AI-first product experiences and rapidly iterate toward user impact.
Mentor and uplevel engineers across the org, influencing engineering best practices and technical direction.
Champion the use of AI internally to drive engineering and operational efficiency.
Required Qualifications:
8+ years of experience building and scaling machine learning systems in production environments.
Strong experience with LLMs and embeddings (e.g., fine-tuning, prompt engineering, vector databases).
Hands-on experience with Python and modern ML libraries such as PyTorch, TensorFlow, HuggingFace, or Scikit-learn.
Experience with cloud infrastructure (e.g., GCP), orchestration (Airflow), and experimentation platforms (e.g., mlflow, Databricks).
Excellent collaboration and communication skills—can influence across product and engineering teams.
Proven impact shipping ML-driven features in B2B SaaS products or enterprise platforms.
Preferred Qualifications:
Bachelors, Masters, or a PhD in Computer Science, Mathematics, Statistics, or other quantitative fields or related work experience
Experience with retrieval-augmented generation (RAG), search infrastructure, or recommendations at scale.
Exposure to GTM, marketing tech, or sales enablement domains in a B2B setting.