Senior Machine Learning Engineer

What you'd actually do

1. Design the CI/CD pipelines and deployment architecture for the ML systems your team owns, making them reliable, repeatable, and easy to operate.
2. Build automated retraining pipelines triggered by performance degradation, and architect monitoring solutions with drift detection and alerting.
3. Design real-time and batch feature pipelines that power identity resolution, customer segmentation, and predictive models at scale.
4. Improve model inference latency to deliver predictions that meet strict Service level agreements while keeping infrastructure costs in check.
5. Establish SLOs and operational standards for your team's production ML.

Skills

What the JD emphasized

• lead complex, ambiguous ML projects
• own technically deep pieces of its ML architecture
• AI-first company
• embrace AI assistance tools like Claude Code as a core part of their daily workflow
• building production ML systems
• ML deployment patterns, model serving, feature engineering, and monitoring/observability for ML systems
• cloud-native ML infrastructure, containerization, and orchestration (Kubernetes, Docker)
• AI-first development practices
• mentoring teammates

Other signals

• ML Engineers work in small, collaborative, and accountable teams.
• lead complex, ambiguous ML projects
• own technically deep pieces of its ML architecture
• deliver production ML systems that create measurable customer impact
• AI is at the core of our platform and the way we work
• AI-first company
• embrace AI assistance tools like Claude Code as a core part of their daily workflow
• keep our processes lightweight, our experimentation rigorous, and our focus on delivering value to our customers through machine learning products and features.
• Design the CI/CD pipelines and deployment architecture for the ML systems your team owns, making them reliable, repeatable, and easy to operate.
• Build automated retraining pipelines triggered by performance degradation, and architect monitoring solutions with drift detection and alerting.
• Design real-time and batch feature pipelines that power identity resolution, customer segmentation, and predictive models at scale.
• Improve model inference latency to deliver predictions that meet strict Service level agreements while keeping infrastructure costs in check.
• Establish SLOs and operational standards for your team's production ML.
• Lead incident response and blameless post-mortems.
• Evaluate MLOps tooling that raises the bar for the team, including experiment tracking, model registry, and serving.
• pairs deep technical judgment with the ability to build and operate production systems end-to-end.
• own technically complex pieces of your team's ML architecture.
• Your teammates seek you out for advice in your space.
• ramp quickly in unfamiliar areas—often leaning on AI tools to do it
• embrace AI-first practices, helping establish how your team works with tools like Claude Code.
• value simplicity, mentorship, and well-reasoned decisions.
• 5+ years building production ML systems, including hands-on experience designing ML pipelines and infrastructure.
• Experience leading complex or ambiguous ML projects within a team as the directly responsible individual.
• Expertise in ML deployment patterns, model serving, feature engineering, and monitoring/observability for ML systems.
• Software engineering skills with experience in Python and familiarity with ML frameworks (e.g. XGBoost, PyTorch, PySpark).
• Experience with cloud-native ML infrastructure, containerization, and orchestration (Kubernetes, Docker).
• Enthusiastic about AI-first development practices, with experience using AI coding assistants to accelerate engineering workflows.
• A habit of mentoring teammates, reviewing others' work, and elevating how your team builds and operates ML systems.