Machine Learning Engineer

Role Responsibilities

• Design, train, and deploy scalable machine learning systems supporting autonomous AI agents.
• Build and fine-tune deep learning models for reasoning, code generation, and real-world decision-making tasks.
• Develop end-to-end ML pipelines covering data preprocessing, feature extraction, training, evaluation, and deployment.
• Implement reinforcement learning workflows and self-improving training loops to enhance agent adaptability.
• Create benchmarking and evaluation tools to measure model accuracy, reasoning ability, and performance.
• Collaborate with data scientists and systems engineers to optimize data quality, inference speed, and hardware utilization.
• Maintain model reproducibility, version control, and experiment tracking using MLOps best practices.
• Contribute to research efforts focused on improving learning efficiency, generalization, and model robustness.

Requirements

• Strong foundation in machine learning, deep learning, or reinforcement learning.
• Proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, or JAX.
• Understanding of training infrastructure, including GPUs/TPUs, distributed training, and data pipelines.
• Experience building end-to-end ML systems from experimentation through deployment.
• Familiarity with MLOps tools such as Weights & Biases, MLflow, Docker, Kubernetes, or Airflow.
• Experience designing custom model architectures or working with transformer-based systems, LLMs, or diffusion models.
• Ability to evaluate model performance critically using data-driven experimentation.
• Interest in AI agents, autonomous reasoning, and collaborative model behavior.