Machine Learning Leadership Role
As a hands-on Machine Learning Engineering Manager, you will lead cross-functional teams in designing, training, and deploying large-scale ML and LLM systems. You will drive the full lifecycle of AI development, from research and experimentation to distributed training and production deployment, while mentoring top-tier engineers and partnering closely with product, research, and infrastructure leaders.
Key Responsibilities:
* Lead and mentor ML engineers, data scientists, and MLOps professionals.
* Manage end-to-end ML/LLM project lifecycle: data pipelines, training, evaluation, deployment, and monitoring.
* Provide technical direction for distributed training, large-scale model optimization, and system architecture.
* Collaborate with Research, Product, and Infrastructure teams to define objectives, milestones, and KPIs.
* Implement MLOps best practices: experiment tracking, CI/CD, model governance, observability.
* Manage compute resources and enforce responsible AI + data security standards.
* Communicate technical progress, blockers, and results clearly to leadership and stakeholders.
This role requires strong proficiency in Python and frameworks like PyTorch, TensorFlow, Hugging Face, DeepSpeed, as well as experience with distributed training, GPU/TPU optimization, and cloud platforms (AWS, GCP, Azure). Knowledge of MLOps tools is also necessary. Excellent leadership, communication, and cross-functional collaboration skills are essential for success.
Required Skills:
* Proficiency in Python and popular frameworks.
* Experience with distributed training, GPU/TPU optimization, and cloud platforms.
* Knowledge of MLOps tools.
Benefits:
This is an excellent opportunity to work on cutting-edge AI projects and develop your skills in machine learning engineering.
Others:
We value diversity and inclusion in the workplace and encourage applicants from all backgrounds to apply.