Machine Learning Engineer, Recommendation (Junior & New Grad)

About the Role

As a Machine Learning Engineer on our Recommendation team, you will contribute to building and improving the machine learning systems that power personalized content delivery to millions of users. You’ll work closely with senior engineers and data scientists to develop scalable algorithms, data pipelines, and experimentation tools that enhance user experience and engagement.

This is an ideal opportunity for a new graduate or early-career engineer who is passionate about large-scale machine learning, recommendation systems, and solving real-world problems through data-driven innovation.

Responsibilities

• Model Development:
  Design and implement scalable machine learning models to address problems such as classification, clustering, topic modeling, natural language processing, and recommendation.
• Tooling & Experimentation:
  Develop in-house machine learning tools and pipelines to support fast experimentation, model training, and deployment.
• Collaboration & Optimization:
  Partner with other engineers and data scientists to identify and solve ML-related challenges, improve algorithm performance, and optimize recommendation quality.
• Continuous Learning:
  Stay current with advances in machine learning, recommendation algorithms, and large-scale data processing frameworks.

Qualifications

• MS degree in Computer Science, Machine Learning, or a related quantitative field.
• Academic or project experience in one or more of the following areas: machine learning, recommendation systems, deep learning, relevance modeling, feed ranking, or data mining.
• Proficiency in Java or Scala.
• Familiarity with scripting languages such as Python, Perl, or shell scripting.
• Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, or MXNet.
• Strong analytical, problem-solving, and communication skills, with enthusiasm for collaboration and learning.

Preferred Qualifications (Nice to Have)

• Internship or research experience related to recommendation systems or large-scale ML model deployment.
• Familiarity with data processing frameworks (e.g., Spark, Flink, or Hadoop).
• Understanding of feature engineering, A/B testing, or model evaluation metrics such as CTR, retention, or relevance.

Why You’ll Love Working Here

• Opportunity to work on high-impact ML projects that influence millions of users.
• A collaborative, learning-driven environment where mentorship and growth are prioritized.
• Exposure to state-of-the-art ML technologies and end-to-end system development.