We're looking for a Staff Applied AI Engineer to join our Enterprise Engineering team. 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!
You will:
- 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.
Ideally you'd have:
- 7+ years of full-time engineering experience, post-graduation
- 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)
Nice to haves:
- 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
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