Machine Learning Systems Engineer

Substack is building a new economic engine for culture, giving the brightest, most interesting, and most creative people on the internet the power of their own publishing platform. The terms of our culture should not be set by gate-keeping legacy media or chaos-fueling social media, but by the people who make and participate in that culture. Substack's model, based on direct subscriptions, has fueled an explosion of independent publishing. It empowers creators with economic autonomy, creative ownership, and a direct connection to their most engaged audiences.

As a Systems Engineer on Substack's ML Team, you will be responsible for building and maintaining the infrastructure that powers our machine learning capabilities. You will focus on the systems, pipelines, and platforms that enable our ML team to develop, deploy, and scale machine learning solutions effectively. This role offers an exciting opportunity to architect the foundational ML infrastructure that will support Substack's growing AI/ML initiatives.

Responsibilities

• Design and build scalable ML infrastructure including model serving systems, feature stores, and training pipelines

• Develop and maintain robust data pipelines for ML workflows, from data ingestion to model deployment

• Implement MLOps best practices including CI/CD for ML models, monitoring, and automated retraining pipelines

• Build and optimize model serving infrastructure to support real-time and batch inference at scale

• Collaborate with ML engineers and data scientists to understand infrastructure requirements and translate them into reliable systems

• Monitor and optimize ML system performance, reliability, and cost efficiency

• Establish infrastructure standards and tooling to accelerate ML development workflows

• Own the deployment and operational aspects of ML models in production

Requirements

• 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 such as feature stores, model registries, and serving systems

• Solid understanding of distributed systems, containerization (Docker/Kubernetes), and cloud platforms

• Experience with data pipeline orchestration tools (e.g., Airflow, Prefect, or similar)

• Independent and autonomous. We're too small to micromanage, and expect that every person at the company owns their work and can be a leader

• Hold yourself and others to a high standard when working on production systems

• Enjoy collaboration with a diverse group of stakeholders while bringing your own unique experience and background to the team

Nice to have

• Experience with ML serving frameworks (e.g., TensorFlow Serving, MLflow, Ray Serve)

• Knowledge of monitoring and observability tools for ML systems

• Experience with cloud ML platforms (AWS SageMaker, Modal, or similar)

• Understanding of ML model lifecycle management and MLOps practices

• Experience with consumer web applications at scale

• Familiarity with ML frameworks like PyTorch and Tensorflow

• Familiarity with JavaScript/TypeScript

Substack’s compensation package includes a market-competitive salary, equity, and exceptional benefits. Our cash compensation salary range for this role is $200,00-250,000. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above.

Substack is an equal opportunity employer. All applicants will be considered for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, gender identity or transgender status), age, national origin, veteran or disability status. We’re seeking people passionate about enabling independent expression and building a better business model for creators. If you want to see what media, communities, and content can become when unmoored from advertising models, and you have the skills and experience to contribute, we’d love to meet you.

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