Machine Learning Engineering Manager

Vintti; Teletrabalho; BRL 433.000 - 650.000; Tempo integral

Resumo da oferta

A tech-focused organization is seeking a Machine Learning Engineering Manager to lead teams in the design and deployment of ML and LLM systems. The ideal candidate will have over 5 years of experience in machine learning and proven leadership in production environments. Responsibilities include managing the complete ML project lifecycle, optimizing models, and collaborating with various teams. This role requires strong proficiency in Python and MLOps tools. The position is fully remote for LATAM candidates.

Qualificações

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

Responsabilidades

• Lead and mentor ML engineers, data scientists, and MLOps professionals.
• Manage end-to-end ML/LLM project lifecycle from data pipelines to deployment.
• Provide technical direction on distributed training and model optimization.

Conhecimentos

Machine Learning

NLP

Deep Learning

Python

MLOps

Leadership

Formação académica

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

Ferramentas

PyTorch

TensorFlow

AWS

GCP

Azure

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.