Machine Learning Engineer

About This Role

Design, implement, and optimize scalable Machine Learning pipelines and deep learning models for reasoning, code generation, and real-world decision-making. Collaborate with data scientists and systems engineers to ensure model quality and efficient deployment.

Responsibilities

• Design and implement scalable ML pipelines for model training, evaluation, and continuous improvement
• Build and fine-tune deep learning models for reasoning, code generation, and real-world decision-making
• Collaborate with data scientists to collect and preprocess training data, ensuring quality and representativeness
• Develop benchmarking tools that test models across reasoning, accuracy, and speed dimensions
• Implement reinforcement learning loops and self-improvement mechanisms for agent training
• Work with systems engineers to optimize inference speed, memory efficiency, and hardware utilization

Requirements

• Strong background in machine learning, deep learning, or reinforcement learning
• Proficiency in Python and familiarity with PyTorch, TensorFlow, or JAX
• Understanding of training infrastructure, including distributed training, GPUs/TPUs, and data pipeline optimization
• Experience with end-to-end ML systems, from preprocessing and feature extraction to training, evaluation, and deployment
• Comfort with MLOps tools (e.g., Weights & Biases, MLflow, Docker, Kubernetes, or Airflow)
• Experience designing custom architectures or adapting LLMs, diffusion models, or transformer-based systems

Skills

* Required skills