Harnham · 1 day ago

Senior Machine Learning Engineer

United States

Full-time

Remote

Senior Level

$180K/yr - $200K/yr

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AnalyticsConsulting

Hiring Manager

Cassie Wandell

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Responsibilities

Design, develop, and implement machine learning models and algorithms to solve complex problems across various domains, including but not limited to recommendation systems, natural language processing, and predictive analytics.

Deploy machine learning models into production environments, collaborating with software engineering teams to ensure scalability, reliability, and maintainability.

Deploy and refine production-grade models on large-scale datasets to enhance ad performance and relevancy.

Use data-driven insights to address key challenges such as cross-channel optimization and A/B testing.

Collaborate with cross-functional teams, including marketing, product, and engineering, to drive data-driven solutions.

Leverage large-scale data processing technologies like Spark and Hive, and machine learning platforms to handle complex datasets.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Ad Tech ExperienceAd AuctioningMachine Learning EngineeringPythonModel Deployment ToolsData PreprocessingFeature EngineeringModel Evaluation TechniquesCloud ComputingDockerKubernetesGoLangPerlTensorFlowAgile/ScrumCollaboration Skills

Required

MUST be coming from an ad tech background

ad auctioning and bidding is a MUST

Bachelor’s degree in Computer Science, Engineering, Mathematics, or related field; Master’s or Ph.D. preferred.

Experience in machine learning engineering or related roles, with a track record of developing and deploying models in production environments.

Proficiency in Python for development and debugging, familiarity with GoLang, Perl, and TensorFlow.

Experience with model deployment tools like Airflow, Databricks, AWS, Docker.

Strong understanding of data preprocessing, feature engineering, and model evaluation techniques.

Familiarity with cloud computing platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).

Excellent problem-solving skills with attention to detail, effective communication, and collaboration skills across technical and non-technical teams.