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Senior Machine Learning Engineer job description

A Senior Machine Learning Engineer is an expert in developing and implementing machine learning algorithms and models, focusing on solving complex problems and enhancing technological solutions within an organization.

Alexandros Pantelakis
Alexandros Pantelakis

HR content specialist at Workable, delivering in-depth, data-driven articles to offer insights into industry and tech trends.

Use this Senior Machine Learning Engineer job description template to advertise open roles for your company. Be sure to modify requirements and duties based on the unique needs of the role you’re hiring for.

What is a Senior Machine Learning Engineer?

A Senior Machine Learning Engineer specializes in creating, deploying, and maintaining advanced machine learning models that drive innovation and efficiency within software applications. They leverage their deep understanding of machine learning, natural language processing, and data science to develop algorithms that can learn from and make decisions based on data.

This role involves a blend of technical expertise, creativity, and problem-solving skills to tackle complex challenges in various domains, including search and recommendation systems.

What does a Senior Machine Learning Engineer do?

A Senior Machine Learning Engineer designs and implements machine learning solutions to improve and automate decision-making processes within an organization. Their work spans the full machine learning lifecycle, from data preparation and model development to deployment and monitoring.

They utilize NLP and ML algorithms to power semantic search and recommendation engines, ensuring the models are scalable, efficient, and integrated seamlessly into the product ecosystem. Additionally, they write and optimize code for production environments, ensuring the robustness and reliability of ML services.

Staying at the forefront of ML advancements, they continuously explore new technologies and methodologies to enhance model performance and functionality.

Senior Machine Learning Engineer responsibilities include:

  • Applying deep learning NLP and ML models to enhance search and recommendation engines
  • Managing the ML lifecycle from data collection to deployment and monitoring
  • Writing production-quality code for ML models as services and APIs
  • Keeping up with the latest ML tooling and communities

Job brief

We’re seeking a highly skilled Senior Machine Learning Engineer to join our dynamic Data Science Team.

In this role, you’ll tackle complex NLP and machine learning challenges, driving forward our in-house ML engine’s capabilities.

Your expertise will be pivotal in developing production-quality ML/AI solutions, enhancing our semantic search and recommendation engines.

You’ll own projects from concept to deployment, ensuring our ML systems are scalable, efficient, and cutting-edge. Join us in revolutionizing recruiting software with your deep learning and ML insights.


  • Apply deep learning NLP and ML models to power semantic search and recommendation engines
  • Contribute to all processes of the ML lifecycle: data collection, annotation, modeling, evaluation, deployment, and monitoring
  • Write production-quality code for ML models as online services and APIs
  • Take ownership of solutions from analysis to implementation
  • Stay updated with the latest in Data Science and ML tooling & communities
  • Present complex analyses clearly and concisely

Requirements and skills

  • BSc/MSc in Electrical & Computer Engineering, Machine Learning, Computer Science, or related fields, with 3+ years of ML implementation experience or a PhD with 1+ years of hands-on ML project experience
  • Solid knowledge of ML principles applied to recommendation/search systems
  • Proficiency in NLP, including language models and text processing
  • Experience with Python in Linux-based environments, Git, and ML frameworks (PyTorch/TensorFlow)
  • Familiarity with relational databases (Postgres, MySQL)
  • Extra credit for cloud deployment experience (GCP, AWS), containerization (Docker), vector search engines, knowledge graphs, PyTorch ecosystem projects, ML publications, or competition participation

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