🌎 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.