Machine Learning Engineer, Enterprise GenAI

1 Day ago • 1-3 Years • $176,000 PA - $300,000 PA

Job Summary

Job Description

As an ML Engineer at Scale, you will be at the forefront of the AI revolution, working with clients to train ML models. Your responsibilities include training state-of-the-art models, identifying opportunities for new services, exploring approaches that integrate human feedback, creating techniques to integrate tool-calling, and working with customers to build new deep learning models. You will be working on cutting-edge projects, from AI cybersecurity to genomic models. You'll be shaping the future of AI by building complex agents for enterprises. The role involves direct interaction with clients, utilizing proprietary research and resources developed at Scale.
Must have:
  • Model training, deployment and maintenance experience
  • Strong skills in NLP, LLMs and deep learning
  • Solid background in algorithms and data structures
  • Experience working with a cloud technology stack
Good to have:
  • Experience in large scale AI problems, ideally in generative-AI field
  • Expertise in vision-language models for real-world applications
  • Published research in machine learning
  • Strong programming skills (e.g., Python)
  • Strong written and verbal communication skills

Job Details

AI is becoming vitally important in every function of our society. At Scale, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the usage of frontier data and models by building complex agents for enterprises around the world through our Scale Generative AI Platform (SGP).

The SGP ML team works on the front lines of this AI revolution. We interface directly with clients to build cutting edge products using the arsenal of proprietary research and resources developed at Scale. As an ML Engineer, you’ll work with clients to train ML models to satisfy their business needs. Your work will range from training next-generation AI cybersecurity firewall LLMs to training foundation genomic models making predictions about life-saving drug proteins. Having a deep curiosity about the hardest questions about LLMs will also motivate various research opportunities on how to apply ML to the forefront of enterprise data. If you are excited about shaping the future of the modern AI movement, we would love to hear from you!

You will: 

  • Train state of the art models, developed both internally and from the community, in production to solve problems for our enterprise customers. 
  • Work with product and research teams to identify opportunities for ongoing and upcoming services.
  • Explore approaches that integrate human feedback and assisted evaluation into existing product lines. 
  • Create state of the art techniques to integrate tool-calling into production-serving LLMs.
  • Work closely with customers - some of the most sophisticated ML organizations in the world - to quickly prototype and build new deep learning models targeted at multi-modal content understanding problems.

Ideally you’d have:

  • At least 1-3 years of model training, deployment and maintenance experience in a production environment
  • Strong skills in NLP, LLMs and deep learning 
  • Solid background in algorithms, data structures, and object-oriented programming
  • Experience working with a cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment
  • PhD or Masters in Computer Science or a related field

Nice to haves:

  • Experience in dealing with large scale AI problems, ideally in the generative-AI field
  • Demonstrated expertise in large vision-language models for diverse real-world applications, e.g. classification, detection, question-answering, etc. 
  • Published research in areas of machine learning at major conferences (NeurIPS, ICML, EMNLP, CVPR, etc.) and/or journals
  • Strong high-level programming skills (e.g., Python), frameworks and tools such as DeepSpeed, Pytorch lightning, kubeflow, TensorFlow, etc. 
  • Strong written and verbal communication skills to operate in a cross functional team environment

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