Senior Staff Engineer (Machine Learning)

About This Role

This role will lead cross-functional teams to build and deploy cutting-edge LLM and ML systems, driving the full lifecycle of AI development from research to production. The Senior Staff Engineer will combine technical depth in deep learning and MLOps with leadership, mentoring engineers and collaborating with research and infrastructure leaders to deliver reliable, high-performance AI systems.

Responsibilities

• Lead and mentor a cross-functional team of ML engineers, data scientists, and MLOps professionals.
• Oversee the full lifecycle of LLM and ML projects — from data collection to training, evaluation, and deployment.
• Collaborate with Research, Product, and Infrastructure teams to define goals, milestones, and success metrics.
• Provide technical direction on large-scale model training, fine-tuning, and distributed systems design.
• Implement best practices in MLOps, model governance, experiment tracking, and CI/CD for ML.
• Manage compute resources, budgets, and ensure compliance with data security and responsible AI standards.
• Communicate progress, risks, and results to stakeholders and executives effectively.

Requirements

• Strong background in Machine Learning, NLP, and modern deep learning architectures (Transformers, LLMs).
• Hands-on experience with frameworks such as PyTorch, TensorFlow, Hugging Face, or DeepSpeed
• Hands-on experience in Docker for Production deployment.
• Proven experience managing teams delivering ML/LLM models in production environments.
• Knowledge of distributed training, GPU/TPU optimization, and cloud platforms (AWS, GCP, Azure).
• Familiarity with MLOps tools like MLflow, Kubeflow, or Vertex AI for scalable ML pipelines.
• Excellent leadership, communication, and cross-functional collaboration skills.

Qualifications

• Bachelor’s or Master’s in Computer Science, Engineering, or related field (PhD preferred)

Nice to Have

• Experience building Agentic applications
• Experience training or fine-tuning foundation models.
• Contributions to open-source ML or LLM frameworks.
• Understanding of Responsible AI, bias mitigation, and model interpretability.

Skills

* Required skills

Benefits