Engineering Manager, Machine Learning

Posted 2024-10-15

💎 Seniority level: Middle, 3-5 years' experience in people management, preferably leading senior engineers and staff engineers; 6+ years of experience building Machine Learning or AI systems

📍 Location: United States

🔍 Industry: Go-to-market solution for revenue teams

🏢 Company: Apollo.io👥 501-1000💰 $100.0m Series D on 2023-08-29Software Development

⏳ Experience: 3-5 years' experience in people management, preferably leading senior engineers and staff engineers; 6+ years of experience building Machine Learning or AI systems

🪄 Skills: LeadershipPythonKerasMachine LearningMLFlowPeople ManagementPyTorchSnowflakeAirflowTensorflowCollaboration

Requirements:

3-5 years' experience in people management, preferably leading senior engineers and staff engineers.
6+ 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: Experience with Databricks, Google Cloud Platform, Snowflake, mlflow, and Airflow.
Experience with Large Language Models (LLMs) or similar technologies.
Experience with one or more of the following: natural language processing, deep learning, recommendation systems, search relevance & ranking, and speech-to-text conversion.

Responsibilities:

Help the Machine Learning team define, set, and adhere to goals and expectations.
Drive a culture around excellence, learning, and fun.
Critically observe and improve development and management processes within Apollo engineering.
Work closely with key stakeholders such as the CTO, Product Managers, and Designers to deliver high quality Machine Learning products (like lead scoring, recommendations, LLM based products, AI agents, etc.) at a rapid pace for an ever-increasing user base.
Change, and be a part of key decision-making processes within the engineering organization.
Participation in key technical decision-making discussions such as sprint planning, software design, and occasionally code reviews.
Help build and scale a world-class engineering team by sourcing and hiring candidates.
Hold systematic weekly 1:1 touch points with engineers to deliver and receive quality feedback.
Work with engineers to develop and execute on personalized goals.
Conduct objective and helpful quarterly performance reviews for engineers.

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