Machine Learning Engineering Manager
Há 6 dias
?? Location:Remote - LATAM?? Schedule:Full-time (8 hrs/day) —must have 4 hrs overlap with PST? About the RoleWe're looking for ahands-on Machine Learning Engineering Managerto 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 blendsdeep ML/MLOps expertisewithstrong leadership and execution, ensuring all AI initiatives translate into measurable business impact.?? Key ResponsibilitiesLead 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 & Qualifications5+ 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 HaveExperience 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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