Machine Learning Engineering Manager

Vintti; Teletrabalho; BRL 541.000 - 704.000; Tempo integral

Resumo da oferta

A leading tech company is seeking a Machine Learning Engineering Manager to lead efforts in designing and deploying large-scale ML systems. The ideal candidate will have over 5 years of experience in Machine Learning, strong leadership skills, and proficiency in Python and MLOps practices. This role involves overseeing the full AI development lifecycle while collaborating with cross-functional teams to ensure AI initiatives bring measurable business impact. This is a remote position focused on LATAM working hours.

Qualificações

• 5+ years of experience in Machine Learning, NLP, and Deep Learning.
• 2+ years leading teams delivering ML/LLM systems in production.
• Strong proficiency in Python and related frameworks.

Responsabilidades

• Lead and mentor ML engineers and data scientists.
• Manage end-to-end ML/LLM project lifecycle.
• Implement MLOps best practices and manage compute resources.

Conhecimentos

Machine Learning

Deep Learning

NLP

Leadership

Python

MLOps

Formação académica

Bachelor's/Master’s in Computer Science or related field

Ferramentas

PyTorch

TensorFlow

MLflow

Kubeflow

Descrição da oferta de emprego

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.