Staff Machine Learning Engineer

Summary

Join Apollo.io's growing Intelligence team as a Staff Machine Learning Engineer to lead mission-critical initiatives powering ML-driven user experiences. You will drive end-to-end ML projects, from problem definition to production and monitoring, focusing on LLMs, embeddings, ranking models, and semantic search. Collaborate with engineers, product managers, and data scientists to build and improve machine learning systems. Guide platform architecture decisions and contribute to foundational ML infrastructure. Mentor and guide other engineers, championing AI use for efficiency. This role requires a strong background in building and scaling machine learning systems and experience with LLMs and embeddings. Apollo.io is an AI-native company, offering a fast-paced and collaborative environment.

Requirements

• 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

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

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