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Build and productionize Machine Learning models for Apollo products like Search and Recommendations. Leverage data to predict user behaviors and optimize their experience. Improve ML stack and maintain scalable data pipelines.
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Apollo.io
At Apollo, we are a remote-first inclusive organization focused on operational excellence. As a Senior Machine Learning Engineer on the Intelligence team, you will build and productionize ML models for products like Search, Recommendations, Content generation, and Conversations. The mission is to leverage Apollo's data to predict user behaviors and optimize their experience. Responsibilities include designing scalable ML systems, improving the ML stack, personalizing user experiences, evaluating performance against business goals, maintaining data pipelines, implementing monitoring and self-healing features, writing tests, and contributing to engineering documentation. Required qualifications include a degree in Computer Science or related field with 6+ years of ML/AI experience, cloud deployment, fine-tuning LLMs, prompt engineering, software engineering skills in Python, and experience with ML libraries. Preferred qualifications include a PhD in machine learning and experience with NLP, deep learning, recommendation systems, search relevance, and speech-to-text conversion.
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 on the Intelligence team, 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, Content generation, Conversations or similar. The mission of the Intelligence team is to leverage Apollo’s massive scale data to understand and predict Apollo users’ behaviors and optimize their experience at all stages of their product journey.
Responsibilities:
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
Competencies:
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
6+ years of experience building Machine Learning or AI systems
Experience deploying and managing machine learning models in the cloud
Experience working with fine tuning LLMs and prompt engineering
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.
