Machine Learning Engineer

The Role

We're looking for a Machine Learning Engineer to design and build feedback-driven learning systems that improve our AI agent over time.

This is not a traditional RL research role. We're focused on practical systems that learn from real user behavior and improve production outcomes.

You'll work at the intersection of a live conversational agent and real shopping behavior, where the feedback signal quality is unusually rich.

What You'll Do

• Build and productionize feedback loops that improve agent performance over time.
• Build evaluation infrastructure including offline metrics, regression suites, and experiment analysis.
• Own signal pipelines end-to-end: instrument events, build labeled datasets, and convert user behaviors into reliable learning signals.
• Design lightweight reinforcement learning and bandit-style approaches where appropriate.
• Partner with product and engineering to define success metrics and optimize for them.
• Design and analyze experiments to validate whether learning system changes improve real outcomes.
• Improve ranking, recommendations, and decision-making within the agent.
• Iterate quickly: ship, measure, learn, improve.

What Success Looks Like

• You ship quickly and drive measurable improvements in core product metrics.
• You turn noisy user behavior into reliable learning signals that improve the agent over time.
• You own systems end-to-end and operate comfortably in production.

Ideal Background

• 5-8 years of hands-on experience building and shipping ML systems.
• Bachelor's or Master's degree in Computer Science.
• Experience shipping recommendation systems, ranking, personalization, or optimization systems in production.
• Deep knowledge of Python and modern ML tooling.
• Pragmatic mindset: choose simple, effective solutions over theoretically perfect ones.