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Role Overview
We are seeking a hands-on Machine Learning Engineering Manager to lead cross-functional teams building and deploying cutting-edge LLM and ML systems. In this role, you'll drive the full lifecycle of AI development — from research and large-scale model training to production deployment — while mentoring top engineers and collaborating closely with research and infrastructure leaders.
You'll combine technical depth in deep learning and MLOps with leadership in execution and strategy, high-performance systems that translate research breakthroughs into measurable business impact.
This position is ideal for leaders who are still comfortable coding, optimizing large-scale training pipelines, and navigating the intersection of research, engineering, and product delivery.
Commitments Required: 8 hours per day with an overlap of 4 hours with PST.
Employment type: Contractor assignment (no medical/paid leave)
Duration of contract: 3 months, possible extension
Location: LATAM, Europe
Two rounds of interviews (60 min technical + 30 min technical & cultural discussion)
Roles & Responsibilities
- Lead and mentor a cross-functional team of ML engineers, data scientists, and MLOps professionals
- Oversee the full lifecycle of LLM and ML projects — from data collection to training, evaluation, and deployment
- Collaborate with Research, Product, and Infrastructure teams to define goals, milestones, and success metrics
- Provide technical direction on large-scale model training, fine-tuning, and distributed systems design
- Implement best practices in MLOps, model governance, experiment tracking, and CI/CD for ML
- Manage compute resources, budgets, and ensure compliance with data security and responsible AI standards
- Communicate progress, risks, and results to stakeholders and executives effectively
Required Skills & Qualifications
- 9+ yrs of strong background in Machine Learning, NLP, and modern deep learning architectures (Transformers, LLMs)
- Hands-on experience with frameworks such as PyTorch, TensorFlow, Hugging Face, or DeepSpeed
- 2+ yrs of proven experience managing teams delivering ML/LLM models in production environments
- Knowledge of distributed training, GPU/TPU optimization, and cloud platforms (AWS, GCP, Azure)
- Familiarity with MLOps tools like MLflow, Kubeflow, or Vertex AI for scalable ML pipelines
- Excellent leadership, communication, and cross-functional collaboration skills
- Bachelor's or Master's in Computer Science, Engineering, or related field (PhD preferred)
- Experience training or fine-tuning foundation models
- Contributions to open-source ML or LLM frameworks
- Understanding of Responsible AI, bias mitigation, and model interpretability
- Work in a fully remote environment
- Opportunity to work on cutting-edge AI projects with leading LLM companies
Key Skills
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