Role overview
We’re looking for a fresh graduate or early-career Machine Learning Engineer on a builder track.
This role is for someone who wants to take ML from “cool idea” to “running in production”. You will work across data, modeling, and software engineering to build, deploy, and operate models that improve customer experience, reduce risk, and unlock operational efficiency.
With the advancement of AI, we care more about fundamentals than buzzwords. Use AI assistants to move faster, but always own correctness, safety, privacy, and performance.
What you'll work on
- Build and ship ML systems
- Develop and deploy ML models and services that solve real business problems.
- Write production-quality code, tests, and documentation.
- 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.
- 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.
- 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).
- 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.
- Use AI tools thoughtfully
- Use AI to accelerate prototyping, debugging, documentation, and test generation.
- Validate outputs, document assumptions, and protect sensitive data.
What we're looking for
- 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.
Tags & focus areas
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