{{Company name}} is hiring a Machine Learning Engineer to take models out of notebooks and into production. You will own the full lifecycle — data preparation, training, deployment, and monitoring — building ML systems that keep working after launch day.

What you'll do

• Build and productionize models for {{Primary ML use case}}, from baseline to deployed service.
• Design training and feature pipelines that are reproducible, versioned, and testable.
• Deploy models behind reliable, low-latency serving infrastructure with sensible fallbacks.
• Monitor models in production for drift, degradation, and cost — and retrain before users notice.
• Evaluate honestly: offline metrics, online experiments, and the gap between them.
• Work with product teams to frame problems where ML actually beats a simpler rule.
• Contribute to shared ML tooling and raise the engineering standard of the team's model code.

What we're looking for

• {{Years of experience}} years building machine learning systems that ran in production.
• Strong Python and software engineering fundamentals — models are code, and yours is reviewable.
• Hands-on experience with a modern ML framework and the surrounding training toolchain.
• Practical understanding of the deployment side: APIs, containers, batch vs real-time serving.
• Experience with data pipelines and the discipline to version data as carefully as code.
• Judgment about model complexity: the simplest model that meets the metric wins.

Nice to have

• Experience with large language models: fine-tuning, retrieval pipelines, or evaluation harnesses.
• Familiarity with an ML platform or experiment-tracking stack.
• Experience with {{Cloud provider}} ML infrastructure and GPU workloads.
• Publications, competition results, or open-source ML work.

Compensation and benefits

• {{Salary range}}
• {{Benefits summary}}
• {{Location / remote policy}}

How to apply

{{Application instructions}} Tell us about one model you shipped: the metric it moved, how it failed in production at least once, and what you changed.