Delivery Manager - Machine Learning - US

Delivery Manager – Machine Learning

 

Location: San Francisco, California, United States
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Position Overview

We are seeking a Delivery Manager (ML) with a strong background in Machine Learning, MLOps, and LLM engineering to lead and scale the execution of complex AI projects across multiple global teams. This individual will oversee the end-to-end delivery of high-impact ML and LLM initiatives — from large-scale data preparation and model training to deployment and performance optimization.

The ideal candidate is a strategic operator and technical leader — someone who can bridge research, engineering, and business delivery, ensuring excellence in execution, model quality, and stakeholder alignment. You will lead projects with 100+ team members, collaborate with AI researchers, ML engineers, and product leads, and drive innovation in tooling, automation, and process optimization.

Primary Responsibilities

1. Team Leadership and Execution

• Lead and manage large, cross-functional ML delivery teams (ML engineers, data scientists, MLOps, and annotators).
  
  
• Build scalable organizational structures and delivery mechanisms to support multiple concurrent ML/LLM programs.
  
  
• Define clear KPIs, quality goals, and success metrics for every project stage.
  
  
• Drive a culture of ownership, learning, and technical excellence through coaching, recognition, and gamified performance systems.
  
  

2. Project Management and Coordination

• Own end-to-end delivery of large-scale ML projects — from planning and training through deployment and monitoring.
  
  
• Translate research and product goals into executable delivery roadmaps.
  
  
• Identify and resolve risks, blockers, and dependencies proactively.
  
  
• Provide transparent reporting to senior leadership and research partners on delivery status, risks, and quality.
  
  

3. Technical Oversight and ML Quality

• Ensure delivery excellence in data pipelines, model development, and evaluation frameworks.
  
  
• Review ML architectures and promote best practices in reproducibility, experiment tracking, and model governance.
  
  
• Analyze performance data and error trends to improve training efficiency and output quality.
  
  
• Champion continuous improvement and innovation in ML delivery workflows.
  
  

4. Cross-Functional and Research Collaboration

• Partner closely with AI researchers, data infrastructure, and product teams to translate research insights into production-grade ML systems.
  
  
• Collaborate with engineering leaders to streamline compute usage, tooling, and resource allocation.
  
  
• Contribute to design discussions around distributed training, fine-tuning, and scalable ML infrastructure.
  
  
• Document and disseminate learnings to accelerate organizational ML maturity.
  
  

Required Skills & Qualifications

• 10+ years of experience in engineering, program management, or AI delivery, including 2+ years in ML/LLM project leadership.
  
  
• Proven ability to manage large-scale delivery programs (100+ team members) across data, research, and engineering streams.
  
  
• Strong technical grounding in machine learning, NLP, and modern deep learning frameworks (PyTorch, TensorFlow, Hugging Face, etc.).
  
  
• Working knowledge of MLOps pipelines (MLflow, Kubeflow, or Vertex AI) and cloud platforms (AWS, GCP, or Azure).
  
  
• Experience with data curation, training pipelines, and large-scale annotation processes.
  
  
• Excellent communication, stakeholder management, and risk mitigation capabilities.
  
  
• Demonstrated success driving ML systems from research to production under tight timelines.
  
  

Preferred Skills & Qualifications

• Advanced degree in Computer Science, Machine Learning, or related field (PhD preferred).
  
  
• Hands-on experience training or fine-tuning foundation or LLM models.
  
  
• Experience managing compute-heavy ML operations, including GPU scheduling and distributed model training.
  
  
• Understanding of Responsible AI, bias mitigation, and model interpretability.
  
  
• Background in gamification, performance motivation, or workforce engagement for large-scale technical teams.
  
  

Why Join

• Operate at the intersection of frontier AI research and large-scale execution.
  
  
• Lead mission-critical initiatives that accelerate AI innovation for global clients.
  
  
• Mentor and scale a world-class ML delivery organization.
  
  
• Influence technical strategy, process design, and AI innovation in one of the fastest-evolving spaces.

 

 

Compensation: (in USD)

 

OTE:

Emerging: 220,000 Target - 259,000  + RSUs