Machine Learning Systems Engineer, Ads ML Platform
WFA Digital Insight
The demand for skilled machine learning engineers has skyrocketed, with a 25% growth in job openings over the past year. Reddit's commitment to innovation and community-driven approach makes this role particularly appealing. With the rise of AI, professionals with expertise in building reliable infrastructure and data pipelines are in high demand. Before applying, candidates should be prepared to showcase their experience with distributed data systems and ML workflows. Reddit's flexible work environment and cutting-edge projects make this an attractive opportunity for those looking to make a real impact.
Job Description
About the Role
Reddit is a community of communities, built on shared interests, passion, and trust. As a Machine Learning Systems Engineer, you will play a crucial role in developing and maintaining the infrastructure that powers our Ads ML platform. You will work closely with our team of engineers to design, build, and deploy scalable data infrastructure and ML platforms that support large-scale feature and training set computation, transformation, and storage.Our team is focused on building a scalable feature platform that makes high-quality features and training datasets easy to build, share, and maintain. We are looking for an engineer with experience in building high-scale data infrastructure and exposure to ML platforms to help evolve and scale our feature management systems.
What You Will Do
- Design and build data infrastructure that supports large-scale feature and training set computation, transformation, and storage.
- Develop frameworks for batch and real-time features with a focus on reliability, scalability, and ease of use.
- Build platform capabilities for feature governance, including lineage tracking, validation, drift detection, anomaly monitoring, reproducibility, and versioning.
- Partner with ML engineers to ensure smooth integration of feature engineering workflows into ML production systems.
- Build systems that support agentic ML workflows, including automated feature discovery, feature quality evaluation, and feature lifecycle management.
- Contribute to operational excellence through observability, performance tuning, reliability engineering, and cost optimization initiatives.
- Collaborate with cross-functional teams to identify and prioritize project requirements.
- Develop and maintain technical documentation for our systems and infrastructure.
What We Are Looking For
- 3+ years of experience in data infrastructure/platform engineering or ML infrastructure platforms.
- Hands-on experience building production services, data pipelines, APIs, workflow systems, or developer tools.
- Experience with at least one distributed data or compute system such as Spark, PySpark, Flink, Kafka, Ray, Airflow, Kubernetes, BigQuery, or similar technologies.
- Familiarity with ML data workflows such as feature generation, training dataset creation, batch processing, real-time data processing, model training, experimentation, or online serving.
- Strong coding skills and ability to write clean, maintainable, well-tested code.
- Experience building intelligent automation or agentic workflows for ML systems is a strong plus.
- Experience with ML infrastructure and MLOps workflows spanning feature engineering, training pipelines, experimentation, model deployment, and online serving is a plus.
Nice to Have
- Experience with Excel and data analysis tools.
- Knowledge of cloud-based technologies and containerization.
- Familiarity with agile development methodologies and version control systems.
Benefits and Perks
- Flexible work environment with remote options.
- Competitive salary and benefits package.
- Opportunities for professional growth and development.
- Access to cutting-edge technologies and tools.
- Collaborative and dynamic work environment.
- Recognition and rewards for outstanding performance.
How to Stand Out
- Showcase your experience with distributed data systems and ML workflows in your resume and cover letter.
- Be prepared to discuss your approach to building scalable infrastructure and data pipelines in your interview.
- Highlight your ability to collaborate with cross-functional teams and communicate complex technical concepts effectively.
- Demonstrate your knowledge of ML infrastructure and MLOps workflows, including feature engineering and model deployment.
- Research Reddit's company culture and values to understand how you can contribute to the team's mission.
- Prepare to discuss your experience with agile development methodologies and version control systems.
- Be ready to provide examples of your experience with automation and workflow optimization.
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