Machine Learning Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Engineer based in United States. This role offers the opportunity to build and scale machine learning systems that directly influence production decisions and measurable business outcomes. You will develop sophisticated models, optimize ML pipelines, and help advance the technologies used to train and serve models at scale. The position combines deep technical work with applied research, allowing you to evaluate emerging ML techniques and translate promising ideas into production systems. You will work closely with experienced engineers in a collaborative environment focused on engineering excellence, reliability, and explainability. Your work will span real-time and batch processing, model monitoring, performance optimization, and automated retraining. This is a remote-first opportunity with access to a global engineering community and regular opportunities for in-person collaboration.

Develop, deploy, and maintain machine learning models that support large-scale production decision-making and directly influence business outcomes. Build or adopt technologies that improve the training and serving of machine learning models, including solutions that support larger models and increase engineering productivity. Research emerging machine learning techniques and evaluate opportunities to apply relevant advances to production systems. Design, optimize, and scale efficient machine learning pipelines for both real-time and batch processing. Monitor model performance, identify data or model drift, and implement automated retraining and improvement processes. Apply strong software engineering practices to create reliable, maintainable, and scalable machine learning systems. Collaborate with engineers and cross-functional stakeholders to translate technical and statistical concepts into practical solutions. Contribute to an engineering excellence culture through rigorous testing, code reviews, explainable systems, and modern development tools. Communicate complex machine learning concepts clearly to both technical and non-technical audiences. Requirements: 6+ years of industry experience applying machine learning, including neural networks, to large-scale production problems. Strong experience with recommendation systems and their application in production environments. Hands-on experience developing and deploying deep neural networks at scale. Solid software engineering and coding skills with the ability to build robust production systems. Demonstrated ability to execute complex projects effectively and deliver results on time. Strong collaborative mindset and willingness to evaluate ideas based on their technical merit rather than individual ownership. Bachelor’s degree or higher in Machine Learning, Mathematics, Physics, Computer Science, or a related technical field; a PhD is a plus. Experience in AdTech or mobile advertising technology is highly desirable. Strong written and verbal communication skills, with the ability to explain statistical and machine learning concepts to diverse audiences. Benefits: Base salary of $225,000–$275,000 USD for eligible locations including the San Francisco Bay Area, Los Angeles/Orange County, New York City, and Seattle. Base salary of $210,000–$255,000 USD for other approved locations, with compensation determined by location and experience. Equity as part of the overall compensation package. Health, vision, and dental benefits. Additional benefits and perks based on location and country of residence. Remote-first working environment with full-time remote eligibility in approved U.S. states. Access to U.S. hubs in Redwood City, Los Angeles, and New York City. Opportunities for in-person project meetings, regional meetups, and company-wide gatherings at least once per quarter. Collaborative and innovative environment focused on engineering excellence and professional growth. Opportunities to work on advanced machine learning systems and technologies shaping the mobile app ecosystem.

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