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Lead Machine Learning Engineer, Recommendation Systems at Jobgether. This position is posted by Jobgether on behalf of a partner company. We are currently looking for a . Lead Machine Learning Engineer, Recommendation Systems. in . California (USA). .. This role offers the opportunity to lead the design, development, and deployment of large-scale recommendation systems that personalize experiences for millions of users daily. You will work closely with cross-functional teams to build ML models, optimize data pipelines, and deliver real-time predictions that directly impact engagement, retention, and revenue. The position emphasizes both technical depth and business impact, requiring expertise in ranking algorithms, distributed computing, and experimentation frameworks. You will operate in a fast-paced, data-driven environment, taking ownership of the end-to-end ML lifecycle while continuously innovating to improve personalization and scalability. The role combines hands-on development with strategic guidance to shape the company’s recommendation engine and user experience.. Accountabilities:. Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale.. Design and implement ranking algorithms balancing relevance, diversity, and revenue impact.. Develop and optimize data processing pipelines using Spark, Beam, Dask, or similar frameworks.. Conduct rigorous A/B and multivariate tests to measure the business impact of ML models.. Ensure production systems meet latency, throughput, and cost efficiency requirements.. Collaborate with product, engineering, and analytics teams to launch high-impact personalization features.. Implement monitoring systems and maintain clear ownership for model reliability and performance.. 7+ years of experience building and scaling production ML systems with measurable business impact.. Strong expertise in recommendation systems, ranking algorithms, or related personalization approaches.. Proficiency with Python, ML frameworks (TensorFlow, PyTorch), and SQL.. Experience with distributed data processing (Spark, Ray) and cloud infrastructure (AWS/GCP).. Familiarity with experimentation platforms and best practices for A/B testing.. Track record of improving KPIs via ML-powered personalization at scale.. Excellent communication, collaboration, and problem-solving skills.. Company Location: United States.