Machine Learning Engineer - 2-year Contract - Hybrid, Seattle Washington - W2 ONLY, NO C2C

We are looking for a Machine Learning Engineer to lead innovation in advertising, focusing on inventory forecasting, planning, pricing, targeting, and ad delivery optimization. Collaborate with cross-functional teams to invent and implement solutions to complex ad-related challenges, driving continuous improvement in business effectiveness.

Responsibilities:

• Drive innovation in advertising through the application of cutting-edge machine learning and statistical techniques.
• Develop scalable methods for large-scale data analysis and model development.
• Invent and iterate solutions to challenging ad-related problems with quick turnaround times.
• Collaborate closely with engineering, program, and product teams in an open and creative environment.
• Mentor team members, guiding their technical development and fostering growth.

Qualifications:

• MS or PhD in computer science or EE.
• 4 years of experience in machine learning and statistics, preferably in leading internet companies.
• Solid understanding of ML technologies, mathematics, and statistics.
• Proficiency in Java, Python, Scala, Spark, SQL, and large-scale ML/DL platforms.
• Passion for understanding the ad business and seeking innovation opportunities.
• Experience thriving in a fast-paced, data-driven, collaborative, and iterative environment.

Mainz Brady Group is a technology staffing firm with offices in California, Oregon and Washington. We specialize in Information Technology and Engineering placements on a Contract, Contract-to-hire and Direct Hire basis. Mainz Brady Group is the recipient of multiple annual Excellence Awards from the Techserve Alliance, the leading association for IT and engineering staffing firms in the U.S.

Mainz Brady Group is an Equal Opportunity Employer. We are committed to Diversity & Inclusion and incorporate non-discrimination best practices in all of our staffing processes. Mainz Brady Group does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, gender expression, age, disability or any other protected class.