Machine Learning Systems Engineer
Job Description
Substack is revolutionizing the creator economy by building a new economic engine for culture through their publishing platform. As a Machine Learning Systems Engineer, you'll be at the forefront of building and maintaining the infrastructure that powers their machine learning capabilities. This role combines the challenges of large-scale distributed systems with cutting-edge ML technologies.
The position offers an opportunity to architect foundational ML infrastructure supporting Substack's growing AI/ML initiatives. You'll be responsible for designing and implementing scalable systems for model serving, feature stores, and training pipelines. The role requires expertise in both software engineering and machine learning operations, with a focus on building robust, production-grade systems.
Working at Substack means joining a company that's transforming how creators connect with their audiences. The compensation package is competitive, ranging from $200,000 to $250,000, plus equity and benefits. The company values independence and ownership, expecting engineers to lead their work without micromanagement.
The ideal candidate will have 5+ years of experience in building ML infrastructure, strong programming skills in Python or TypeScript, and deep knowledge of distributed systems and cloud platforms. You'll work with cutting-edge technologies including Kubernetes, various ML serving frameworks, and modern MLOps tools. The role offers a unique blend of technical challenges, from optimizing real-time inference systems to establishing best practices for ML operations.
Substack's mission of empowering creators with economic autonomy and creative ownership makes this an exciting opportunity for engineers who want to make a meaningful impact on the future of digital publishing and content creation. The hybrid work environment offers flexibility while maintaining collaborative opportunities at their San Francisco headquarters.
Responsibilities For Machine Learning Systems Engineer
- Design and build scalable ML infrastructure including model serving systems, feature stores, and training pipelines
- Develop and maintain robust data pipelines for ML workflows
- Implement MLOps best practices including CI/CD for ML models
- Build and optimize model serving infrastructure
- Collaborate with ML engineers and data scientists
- Monitor and optimize ML system performance
- Establish infrastructure standards and tooling
- Own the deployment and operational aspects of ML models in production
Requirements For Machine Learning Systems Engineer
- 5+ years of relevant experience building and maintaining data/ML infrastructure systems
- Strong programming skills in Python or TypeScript and experience with infrastructure-as-code tools
- Experience with ML infrastructure components
- Solid understanding of distributed systems, containerization, and cloud platforms
- Experience with data pipeline orchestration tools
- Independent and autonomous
- Hold yourself and others to a high standard when working on production systems
- Enjoy collaboration with diverse stakeholders
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