Machine Learning Engineer

This is an applied AI role focused on building and deploying machine learning systems that operate in real-world conditions-not just experiments. You'll work closely with external teams to break down ambiguous problems, design solutions, and deliver models that can handle edge cases, uncertainty, and scale.

The work sits at the intersection of data, modeling, and system design, with a strong emphasis on getting solutions into production and driving measurable outcomes.

What You'll Own

• Build and productionize machine learning models that identify patterns, anomalies, and system-level risks
• Work with stakeholders to define problem spaces, success metrics, and deployment strategies
• Develop pipelines for data ingestion, labeling, training, and evaluation
• Apply modern techniques (including deep learning and large language models) to improve system performance
• Iterate on models based on real-world usage, feedback, and failure modes
• Contribute to system architecture decisions that support scalable, reliable ML deployment

• 8+ years of experience in machine learning or applied AI roles
• Strong Python skills and hands-on experience with ML frameworks (e.g., PyTorch, TensorFlow)
• Experience deploying models into production environments and maintaining them over time
• Ability to work through loosely defined problems and deliver practical solutions
• Solid foundation in data analysis, experimentation, and model evaluation

This organization builds applied AI systems for companies operating complex, high-risk platforms. Their focus is on helping teams better understand system behavior, anticipate failure scenarios, and improve how automated decisions are made in production environments.