job specification

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.