Senior Machine Learning Engineer

About the Role

Our Machine Learning Engineering team powers personalized experiences for hundreds of millions of customers across thousands of brands. As a Senior Machine Learning Engineer, you will play a critical role in building, scaling, and operating production-grade ML systems that drive real-time personalization across the Attentive platform.

You will operate with a high degree of ownership, partner closely with Product and Engineering, and help raise the technical bar across our ML systems in a fast-paced, high-impact environment.

What You’ll Accomplish

• Design, build, and operate scalable machine learning systems used in real-time targeting, decisioning, personalization systems
• Own ML projects end-to-end, from data exploration and modeling to deployment, monitoring, and iteration
• Proactively safeguard model and system quality through testing, monitoring, validation, and alerting
• Collaborate cross-functionally with product, data, and other engineering partners to translate business problems into ML solutions
• Continuously improve system reliability, performance, and engineering efficiency
• Contribute to technical direction and best practices across the ML engineering team
• Mentor and support other engineers through code reviews, design discussions, and knowledge sharing
• Thrive in a high-impact, fast-paced, late-stage startup environment

Your Expertise

• 6+ years of professional experience building production machine-learning software systems
• Proven experience owning ML systems long enough to see the downstream impact of design decisions
• Strong proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, or XGBoost
• Experience with data processing and analytics tools such as pandas, Spark, SQL, and matplotlib
• Hands-on experience building automated pipelines for data processing, model training, validation, and deployment
• Experience collaborating with cross-functional teams to deliver ML-powered features
• Strong communication skills and a high sense of ownership

What We Use

• Backend: Java / Kotlin / Spring Boot microservices, Gradle, DynamoDB, Aurora, Airflow, Postgres, Redis, hosted via AWS
• Infrastructure: Kubernetes (AWS EKS), Istio, Datadog, Terraform, Cloudflare, Helm
• Frontend: React, TypeScript, GraphQL, Storybook, Radix UI, Vite, esbuild, Playwright
• Machine Learning & Automation: Custom and open-source models, LLMs, Python, Metaflow, Hugging Face, PyTorch, TensorFlow, Pandas