About the Role
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 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 Applied AI Engineer, you’ll work with clients to create ML solutions to satisfy their business needs. Your work will range from building next-generation AI cybersecurity firewalls to creating transformative AI experiences in journalism to applying foundation genomic models making predictions about life-saving drug proteins. Daily data-driven experiments will provide key insights around model strengths and inefficiencies which you’ll use to improve your product’s performance. If you are excited about shaping the future of the modern AI movement, we would love to hear from you!
Responsibilities
- Own, plan, and optimize the AI behind our Enterprise customer’s deepest technical problems
- Leverage SGP to build the most advanced AI agents across the industry including multimodal functionality, tool-calling, and more
- Have experience gathering business requirements and translating them into technical solutions
- Meet regularly with customer teams onsite and virtually, collaborating cross-functionally with all teams responsible for their data and ML needs
- Push production code in multiple development environments, writing and debugging code directly in both our customer’s and Scale’s codebases.
- Be able and willing to multi-task and learn new technologies quickly
Requirements
- A love for solving deeply complex technical problems with ambiguity using state of the art research and AI to accomplish your client’s business goals
- Strong engineering background: a Bachelor’s degree in Computer Science, Mathematics, or another quantitative field or equivalent strong engineering background.
- Deep familiarity with a data-driven approach when iterating on machine learning models and how changes in datasets can influence model results
- Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment
- Proficiency in Python to write, test and debug code using common libraries (ie numpy, pandas)
Qualifications
- Strong knowledge of software engineering best practices
- Have built applications taking advantage of Generative AI in real, production use cases
- Familiarity with state of the art LLMs and their strengths/weaknesses