About Apollo

Founded in 2015, Apollo is a leading sales intelligence and engagement platform trusted by over 15,000 paying customers, from rapidly growing startups to the largest global enterprises. Our platform unifies a database of 200 million business contacts with advanced intelligence and engagement tools, to help over 500,000 sales, marketing, and recruiting professionals to connect with the right person at the right time with the right message, at speed and scale.

In the last year, we’ve grown ARR 3x, quadrupled our active users, maintained profitability 18 out of the past 20 months, and recently closed a $110M Series C led by Sequoia Capital to fuel the next phase of our growth.

Working at Apollo

We are a remote-first inclusive organization focused on operational excellence.  Our way of working ensures clear expectations and an environment to do your best work with ample reward.

Your Role & Mission:

As a Senior Machine Learning Engineer you will be responsible for building and productionizing Machine Learning (ML) models and other smart algorithms for various Apollo products. These products may include Search, Recommendations, Conversations or similar.


  • Design, build, evaluate, deploy and iterate on scalable Machine Learning systems
  • Understand the Machine Learning stack at Apollo and continuously improve it
  • Build systems that help Apollo personalize their users’ experience
  • Evaluate the performance of machine learning systems against business objectives
  • Develop and maintain scalable data pipelines that power our algorithms
  • Implement automated monitoring, alerting, self-healing (restartable/graceful failures) features while productionizing data & ML workflows
  • Write unit/integration tests and contribute to engineering wiki


  • Documentation first approach; loves to scale up by writing things down to share knowledge asynchronously
  • Excellent communication skills; be able to work with stakeholders to develop and define key business questions and build data sets that answer those questions.
  • Excellent ambiguity resolution skills; be able to break down ambiguous problems into simpler milestones and delegate to junior engineers
  • Self-motivated and self-directed
  • Inquisitive, able to ask questions and dig deeper
  • Organized, diligent, and great attention to detail
  • Acts with the utmost integrity
  • Genuinely curious and open; loves learning
  • Critical thinking and proven problem-solving skills required

Required Qualifications: 

  • Bachelors, Masters, or a PhD in Computer Science, Mathematics, Statistics, or other quantitative fields or related work experience
  • 4+ years of experience building Machine Learning or AI systems
  • Experience deploying and managing machine learning models in the cloud
  • Strong analytical and problem-solving skills
  • Proven software engineering skills in production environment, primarily using Python
  • Experience with Machine Learning software tools and libraries (e.g., Scikit-learn, TensorFlow, Keras, PyTorch, etc.)

Preferred Qualifications:

  • PhD in Computer Science or related field with a focus on machine learning
  • Experience with Databricks, Google Cloud Platform, Snowflake, mlflow, and Airflow
  • Experience with one or more of the following: natural language processing, deep learning, recommendation systems, search relevance & ranking, and speech-to-text conversion.

What You’ll Love About Apollo

Besides the great compensation package and culture that thrives in openness and excellence, we invest tremendous effort into developing our remote employees’ careers. The team embraces that we have a sole purpose: to help customers maximize their full revenue potential on the Apollo platform. This mindset opens us up to a lot of creative approaches to making customers successful at scale. You’ll be a significant part of a lean, remote team, empowered to really own your role as a proactive educator. We’re very collaborative at Apollo, so you’ll be able to lean on your teammates, even in adjacent departments, to help you achieve lofty goals. You’ll be supported and encouraged to experiment and take educated risks that lead to big wins. And, you’ll have a whole team remotely by your side to help you do it!

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