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NewsBreak

Machine Learning Engineer, Recommendation (Junior & New Grad)

NewsBreak, Mountain View, California, us, 94039

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About NewsBreak

Founded in 2015, NewsBreak is the Content Intelligence platform shaping the future content economy. With over 40 million monthly active users, our flagship platform delivers highly personalized local news and information powered by advanced AI, recommendation systems, and adtech.

Recognized by Fast Company as #32 on the Top Workplaces for Innovators, we're proud to be Great Place to Work® certified and home to a dynamic team of technologists, product innovators, and business leaders who are passionate about solving meaningful challenges at scale.

Together, we reached unicorn status in 2021, and we remain committed to continuing this high-growth trajectory with the right team to fulfill our mission: building the infrastructure layer for content intelligence.

If you're inspired to dream big, innovate fast, and make a difference, we'd love to hear from you! For more information, visit www.newsbreak.com/about

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.

The US base salary range for this full-time position is listed below. Pay may vary based on a number of factors including job-related skills, level, experience, geographic location and relevant education or training. At NewsBreak, we design our overall rewards package to attract top talents. Depending on the position, the role may also be eligible for discretionary bonus and options. Your recruiter can share more details during the hiring process.

Annual Base Pay Range

$145,000-$185,000 USD

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