Associate ML Engineer - Builders Program Tamara 

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.

Your responsibilities

• 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.

Your expertise (must have)

• Fresh graduate or of relevant experience (internships and projects count).
• Solid programming fundamentals in Python (preferred) or another language used for ML systems.
• Strong fundamentals in:
  
  • Data structures and algorithms (enough to write efficient, reliable code)
  • Probability and statistics basics
  • ML fundamentals (supervised learning, overfitting, validation, metrics)
• Comfort working with data using SQL and/or Python.
• A problem-solving mindset and curiosity to learn quickly.
• Clear communication and a collaborative approach.

Nice to have

• 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.

What success looks like

• 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.