Associate ML Engineer - Builders Program

Role overview

• check_circle Build and ship ML systems Develop and deploy ML models and services that solve real business problems. Write production-quality code, tests, and documentation.
• check_circle Develop and deploy ML models and services that solve real business problems.
• check_circle Write production-quality code, tests, and documentation.
• check_circle Own the full model lifecycle Help with data understanding, feature creation, training, evaluation, and iteration. Set up offline and online evaluation, and monitor performance after release.
• check_circle Help with data understanding, feature creation, training, evaluation, and iteration.
• check_circle Set up offline and online evaluation, and monitor performance after release.
• check_circle Make ML reliable in production Improve model serving reliability, latency, and cost. Implement monitoring for data drift, model drift, and key quality metrics. Participate in incident response and postmortems when needed.
• check_circle Improve model serving reliability, latency, and cost.
• check_circle Implement monitoring for data drift, model drift, and key quality metrics.
• check_circle Participate in incident response and postmortems when needed.
• check_circle Work with data and platform teams Partner with data engineers and platform engineers on pipelines, event-driven signals, and data quality. Use event streaming patterns when appropriate (near-real-time features, online scoring, CDC signals).
• check_circle Partner with data engineers and platform engineers on pipelines, event-driven signals, and data quality.
• check_circle Use event streaming patterns when appropriate (near-real-time features, online scoring, CDC signals).
• check_circle Build tools that help others move faster Contribute to reusable training and serving components (templates, libraries, CI/CD, feature pipelines). Help enable safe self-serve ML and AI usage across teams.
• check_circle Contribute to reusable training and serving components (templates, libraries, CI/CD, feature pipelines).
• check_circle Help enable safe self-serve ML and AI usage across teams.
• check_circle Use AI tools thoughtfully Use AI to accelerate prototyping, debugging, documentation, and test generation. Validate outputs, document assumptions, and protect sensitive data.
• check_circle Use AI to accelerate prototyping, debugging, documentation, and test generation.
• check_circle Validate outputs, document assumptions, and protect sensitive data.
• check_circle Fresh graduate or < 1 year of relevant experience (internships and projects count).
• check_circle Solid programming fundamentals in Python (preferred) or another language used for ML systems.
• check_circle Strong fundamentals in: Data structures and algorithms (enough to write efficient, reliable code) Probability and statistics basics ML fundamentals (supervised learning, overfitting, validation, metrics)
• check_circle Data structures and algorithms (enough to write efficient, reliable code)
• check_circle Probability and statistics basics
• check_circle ML fundamentals (supervised learning, overfitting, validation, metrics)
• check_circle Comfort working with data using SQL and/or Python.
• check_circle A problem-solving mindset and curiosity to learn quickly.
• check_circle Clear communication and a collaborative approach.

Preferred qualifications

• Experience with common ML libraries (scikit-learn, PyTorch, TensorFlow) through coursework or projects.
• Familiarity with model deployment patterns (APIs, batch scoring, streaming/online scoring).
• Exposure to MLOps concepts (experiment tracking, model registry, CI/CD, monitoring).
• Familiarity with cloud (GCP/AWS) and containers (Docker) is a plus.
• Understanding of responsible AI and privacy (PII handling, access control, evaluation).
• Experience using AI assistants responsibly for coding and analysis.
• You ship at least one end-to-end ML improvement (model, feature, or service) that runs reliably.
• You can explain model performance clearly: what improved, what did not, and why.
• Monitoring is in place for your models, and you react quickly when metrics drift.
• You reduce manual effort for the team by adding a reusable component, template, or automation.

About the company