Senior Software Engineer - Machine Learning (f/m/d)

Contentful · Enterprise · London, United Kingdom · Engineering

Senior Machine Learning Engineer at Contentful, focusing on building and optimizing generative AI solutions for enterprise customers. The role involves designing, building, and measuring production ML workloads, optimizing generative AI products for accuracy, speed, and scalability, integrating ML cloud solutions, and applying prompt engineering and fine-tuning. It requires technical leadership in product software development, experience deploying LLM-based models, understanding customer needs, and familiarity with distributed systems and cloud services. The role also emphasizes staying up-to-date on LLM developments like RAGs, open-source models, and agent architectures.

What you'd actually do

  1. Measure and ensure the high-quality output of ML workloads for our enterprise customers.
  2. Design, build, and measure production software in the Contentful Platform.
  3. Conduct focused research and testing using tools like Jupyter Notebook.
  4. Optimize generative AI products for accuracy, speed, and scalability.
  5. Integrate ML cloud solutions and adapt them to our large-scale use cases.

Skills

Required

  • Proven experience as a technical leader in a product software development environment
  • Proven Track Record in Model Development, including successfully developing and deploying LLM-based models for various NLP tasks in real-world applications with production workloads
  • Proven ability to work backwards from customer needs to deliver ML-based features that meet those needs
  • Ability to organize and prioritize competing workloads
  • Familiarity with distributed systems and cloud services (e.g., AWS, Azure, GCP)
  • Solid understanding of machine learning principles
  • Experience with container frameworks such as Docker or Kubernetes
  • Constructive Problem-Solving
  • Up-To-Date on The Latest Updates on LLM Development & Research – From RAGs to Open-Source models over Agent Architectures

Nice to have

  • Background includes machine learning and AI implementation
  • You can translate a research paper into a PoC if the code does not exist

What the JD emphasized

  • production workloads
  • customer needs
  • large-scale use cases
  • high-load customer workloads
  • LLM-based models
  • production workloads
  • customer needs
  • RAGs
  • Open-Source models
  • Agent Architectures

Other signals

  • customer impact
  • production software
  • generative AI
  • LLM-based models
  • customer needs
  • large-scale use cases
  • prompt engineering
  • fine-tuning
  • high-load customer workloads
  • distributed systems
  • cloud services
  • RAGs
  • Open-Source models
  • Agent Architectures