Staff Machine Learning Engineer

Key Skills

Amazon Simple Storage Service (S3), AWS, AWS Sagemaker, Data science techniques, Effective communication skills, Git, Lambda, Machine learning techniques, MLOps tools, Python Programming, SQL

Job Description

The Opportunity

The Team: The Machine Learning Operations (MLOps) team at Kargo bridges the gap between data science, engineering, and production deployment. Our mission is to design, deploy, monitor, and maintain scalable machine learning and optimization systems that directly contribute to the business’s revenue objectives. Collaborating closely with Data Science, Product Management, and Business stakeholders, we focus on delivering robust solutions that optimize auction dynamics (e.g., bid pricing, pacing), ensure accurate predictions of advertising outcomes (CTR, viewability, etc.), and enable advanced recommendations for advertising content (audience targeting and contextual matching).

The Role: The Staff Machine Learning (ML) Engineer will play a crucial role in designing, developing, deploying, and maintaining machine learning models. This individual will work closely with cross-functional teams to ensure the seamless integration of ML solutions into our advertising technology platform. The position requires strong hands-on experience, technical skills, and a deep understanding of machine learning principles and best practices.

The Daily To-Do

• Design, develop, and deploy machine learning models to meet business objectives.
• Implement CI/CD pipelines for seamless model versioning, updates, and deployment.
• Ensure models are scalable, reliable, and optimized for production environments.
• Collaborate with Data Science, Engineering, and Product teams to deliver end-to-end ML solutions.
• Work with stakeholders to integrate models into the AdTech platform.
• Set up monitoring and alerting systems to track model health and identify data/model drift.
• Continuously optimize models for improved efficiency, accuracy, and performance.
• Leverage AWS (EMR, EC2, SageMaker), Snowflake, Databricks, and other cloud tools for ML workflows.
• Optimize data pipelines, incorporating feature stores for enhanced model performance.
• Stay current with industry trends and emerging technologies.
• Contribute to knowledge sharing, code reviews, and process improvements.

Qualifications :

• BS/MS in Computer Science, Statistics, or a related field preferred.
• In-depth understanding of machine learning principles and best practices.
• 3+ years of experience in building and deploying machine learning models in production environments.
• Experience building both offline and online training and inference pipelines for real-time systems.
• Strong experience with AWS (S3, EC2, Lambda, SageMaker), Snowflake, and other cloud-based tools for machine learning and data engineering.
• Familiarity with the MLOps stack, including Databricks, Feature Stores, Kubernetes, Kubeflow, MLflow etc
• Expertise in Spark for large-scale data processing and distributed workflows.
• Proficient in Git and version control best practices.
• Highly skilled in SQL and Python; experience with Go is a plus.
• Hands-on experience in automating the provisioning and management of cloud infrastructure.
• Strong interest in advertising, media, analytics, and marketing, with AdTech or digital advertising experience preferred.
• Highly organized, detail-oriented, and able to manage multiple tasks effectively.
• Excellent communication skills, able to convey complex technical concepts to both technical and non-technical audiences.
• Able to work independently and collaboratively within a team environment.

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