Machine Learning Engineering Manager

Há 6 dias

Brazil Vintti Tempo inteiro

Location: Remote - LATAM Schedule: Full-time (8 hrs/day) — must have 4 hrs overlap with PST About the Role We're looking for a hands-on Machine Learning Engineering Manager to lead cross-functional teams in designing, training, and deploying large-scale ML and LLM systems. You'll 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 infra leaders. This role blends deep ML/MLOps expertise with strong leadership and execution, ensuring all AI initiatives translate into measurable business impact. 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, cloud budgets, and enforce Responsible AI + data security standards. Communicate technical progress, blockers, and results clearly to leadership and stakeholders. Required Skills & Qualifications 5+ years of experience in Machine Learning, NLP, and Deep Learning (Transformers, LLMs). 2+ years leading teams delivering ML/LLM systems in production. Strong proficiency in Python and frameworks like PyTorch, TensorFlow, Hugging Face, DeepSpeed. Experience with distributed training, GPU/TPU optimization, and cloud platforms (AWS, GCP, Azure). Knowledge of MLOps tools (MLflow, Kubeflow, Vertex AI, etc.). Excellent leadership, communication, and cross-functional collaboration skills. Bachelor's/Master's in Computer Science, Engineering, or related field (PhD preferred). Nice to Have Experience training or fine-tuning foundation models. Contributions to open-source ML/LLM frameworks. Knowledge of Responsible AI practices, bias mitigation, and model interpretability.

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