Senior Machine Learning Engineer - AdTech
To see similar active jobs please follow this link: Remote Development jobs
We are seeking a Senior Machine Learning Engineer with strong expertise in data analysis, online experimentation techniques, and large-scale engineering systems. This role will lead multiple strategic initiatives within our Supply Personalization team.
Supply Personalization focuses on optimizing the volume, timing, and types of ad loads a user receives. By leveraging data and experimentation, we aim to uncover and learn the most effective strategies for enhancing user experiences and driving business outcomes using machine learning techniques combined with large scale experimentation.
We are looking for someone who is motivated by user and business problems as much as they are by technical problems, and who enjoys ambiguity, brainstorming, experimentation, and iteration. You will work in close collaboration with key stakeholders across engineering, product, business, and leadership teams to build the most impactful solutions for our Spotify listeners and business.
What You'll Do
Design and implement machine learning systems to optimize various targets continuously.
Research and apply optimization strategies to balance multiple objectives effectively.
Partner with multiple teams to shape and enhance systems while exploring untapped collaboration opportunities.
Analyze data and use machine learning techniques to understand user behavior and improve ad experiences.
Collaborate with backend engineers, data scientists, data engineers, and product managers to establish baselines, inform product decisions, and develop new technologies.
Who You Are
You have professional experience in applied machine learning.
You have strong technical expertise in software engineering, data analysis, and machine learning.
You are proficient in programming languages such as Python, Java, C++, or Scala.
You are skilled in SQL for data transformation and analysis.
You are experienced in designing online experiments for hypothesis testing and validation.
You have expertise in developing data pipelines using tools like Apache Beam or Spark
As a plus, you may have experience with adtech, Recommender Systems, or Optimization Problems, as well as Bayesian optimization, Online learning, and Causal inference
Where You'll Be
We offer you the flexibility to work where you work best! For this role, you can be within the Americas region as long as we have a work location.
This team operates within the Eastern time zone for collaboration.
The United States base range for this position is $176,166.00 - $251,666.00, plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. This range encompasses multiple levels. Leveling is determined during the interview process. Placement in a level depends on relevant work history and interview performance. These ranges may be modified in the future
About the job
Senior Machine Learning Engineer - AdTech
To see similar active jobs please follow this link: Remote Development jobs
We are seeking a Senior Machine Learning Engineer with strong expertise in data analysis, online experimentation techniques, and large-scale engineering systems. This role will lead multiple strategic initiatives within our Supply Personalization team.
Supply Personalization focuses on optimizing the volume, timing, and types of ad loads a user receives. By leveraging data and experimentation, we aim to uncover and learn the most effective strategies for enhancing user experiences and driving business outcomes using machine learning techniques combined with large scale experimentation.
We are looking for someone who is motivated by user and business problems as much as they are by technical problems, and who enjoys ambiguity, brainstorming, experimentation, and iteration. You will work in close collaboration with key stakeholders across engineering, product, business, and leadership teams to build the most impactful solutions for our Spotify listeners and business.
What You'll Do
Design and implement machine learning systems to optimize various targets continuously.
Research and apply optimization strategies to balance multiple objectives effectively.
Partner with multiple teams to shape and enhance systems while exploring untapped collaboration opportunities.
Analyze data and use machine learning techniques to understand user behavior and improve ad experiences.
Collaborate with backend engineers, data scientists, data engineers, and product managers to establish baselines, inform product decisions, and develop new technologies.
Who You Are
You have professional experience in applied machine learning.
You have strong technical expertise in software engineering, data analysis, and machine learning.
You are proficient in programming languages such as Python, Java, C++, or Scala.
You are skilled in SQL for data transformation and analysis.
You are experienced in designing online experiments for hypothesis testing and validation.
You have expertise in developing data pipelines using tools like Apache Beam or Spark
As a plus, you may have experience with adtech, Recommender Systems, or Optimization Problems, as well as Bayesian optimization, Online learning, and Causal inference
Where You'll Be
We offer you the flexibility to work where you work best! For this role, you can be within the Americas region as long as we have a work location.
This team operates within the Eastern time zone for collaboration.
The United States base range for this position is $176,166.00 - $251,666.00, plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. This range encompasses multiple levels. Leveling is determined during the interview process. Placement in a level depends on relevant work history and interview performance. These ranges may be modified in the future