Machine Learning Engineer

Accountabilities:

- Develop, deploy, and maintain machine learning models that support production decision-making systems and directly influence business outcomes.

- Build or adopt technologies that improve the training and serving of machine learning models, including solutions capable of supporting large models and increasing developer productivity.

- Monitor current machine learning research and identify promising techniques that can be evaluated and applied to practical business challenges.

- Design, build, and scale efficient machine learning pipelines for both real-time and batch processing.

- Monitor model performance, identify data or model drift, and develop automated retraining and improvement processes.

- Apply deep learning and neural network techniques to large-scale production systems, including recommendation systems.

- Collaborate with engineers from diverse technical backgrounds to design reliable and scalable solutions.

- Communicate statistical and machine learning concepts clearly to both technical and non-technical stakeholders.

- Contribute to engineering excellence through state-of-the-art development tools, risk-driven testing, explainable systems, and rigorous code review.

- Drive projects from technical planning through execution and timely delivery while continuously improving engineering and machine learning practices.

Requirements:

- 6+ years of industry experience applying machine learning, including neural networks, to large-scale problems.

- Strong hands-on experience developing and operating deep neural networks in production at scale.

- Experience designing or working with recommendation systems.

- Solid software engineering and coding capabilities with an emphasis on building reliable production systems.

- Demonstrated ability to execute projects effectively and deliver high-quality results within expected timelines.

- Strong analytical and problem-solving skills, with an ability to evaluate multiple approaches and focus on the most effective solution.

- Collaborative mindset and willingness to set aside personal preferences in pursuit of the strongest technical outcome.

- Bachelor’s degree or higher in Machine Learning, Mathematics, Physics, or a related technical discipline.

- PhD in a relevant field is a plus.

- Experience in AdTech or related advertising technology environments is a strong plus.

- Strong written and verbal communication skills, particularly when explaining complex machine learning and statistical concepts.

Benefits:

- Base salary of $225,000–$275,000 for the San Francisco Bay Area, Los Angeles/Orange County, NYC, and Seattle.

- Base salary of $210,000–$255,000 for Olympia, Austin, San Diego, Santa Barbara, Boston, and other approved locations.

- Full-time remote work available from approved locations in CA, CO, ID, IL, FL, GA, MA, MI, MN, MO, NJ, NV, NY, OR, PA, TX, UT, and WA.

- Remote-first work environment with U.S. hubs in Redwood City, Los Angeles, and New York City.

- Equity as part of the overall compensation package.

- Health, vision, and dental benefits.

- Wellness stipends and additional benefits or perks depending on location.

- Opportunities for in-person project meetings, regional gatherings, and company-wide events at least once per quarter.

- Collaborative, fast-paced, and innovative engineering environment.

- Opportunities to work on large-scale machine learning, recommendation systems, deep learning, and emerging ML technologies.

- Culture focused on engineering excellence, explainability, rigorous testing, and continuous technical improvement.

- Compensation benchmarked according to role, level, experience, and geographic location.

How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
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