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Join the team shaping the future of AI at Scale. 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.
Job Responsibility:
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)
Nice to have:
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