ML Systems Engineer - Model Training and Infrastructure

About the role

Cosine is building autonomous AI engineers that plan, write and ship code within real development workflows. As an ML Systems Engineer, you will work at the intersection of machine learning, software engineering, and infrastructure to train the next generation of Lumen models. You will build the environments where models learn to code, develop data pipelines, and run fine-tuning and reinforcement-learning workloads.

Responsibilities

• Contribute to the end-to-end training of software-engineering models.
• Implement supervised fine-tuning pipelines using curated code and conversation datasets.
• Build reinforcement-learning loops in which models write code, run tests and use development tools.
• Develop custom PyTorch dataloaders, training objectives and evaluation workflows.
• Develop synthetic data-generation pipelines for future RL and fine-tuning runs.
• Design, build and deploy containerised services that support model training and evaluation.
• Maintain and extend evaluation suites for code models.

Required skills

• Python
• Go
• PyTorch
• Docker
• Kubernetes
• Machine Learning
• Data Engineering

Nice to have

• Synthetic data generation
• Reinforcement learning
• SQL
• Apache Iceberg
• DuckDB
• TensorFlow
• JAX

Qualifications

• Experience in software engineering, ML infrastructure, data engineering, applied machine learning or a closely related field
• Equivalent evidence of strong technical ability

About the Company

Cosine is building autonomous AI engineers that plan, write and ship code inside real development workflows. Their models are designed for on-premise, VPC and fully air-gapped environments, focusing on reliability and enterprise-grade coding performance.