We’re looking for an Applied AI Scientist with strong machine learning experience to join our AI Technologies team.
In this role, you’ll help build practical machine learning and generative AI solutions that can move from experimentation into production. You’ll work closely with product owners, data engineers, software engineers, and other ML practitioners to design, test, improve, and deploy AI/ML capabilities at scale.
This is a hands-on role for someone who enjoys solving technical problems, writing code, experimenting with new approaches, and turning ideas into reliable systems.
Job Responsibilities:
- Define problems, success measures, and technical approaches with product and engineering teams.
- Design experiments, establish baselines, and benchmark models and agent systems. Investigate failures and use the results to guide improvements.
- Own Python development from experimental prototypes to tested production code, working with engineers on deployment and monitoring.
- Make and explain trade-offs between model quality, reliability, latency, and cost.
- Lead technical design reviews and mentor other ML practitioners.
- Work with engineering and control teams to meet data, model governance, and security requirements.
Required Qualifications, Capabilities, and Skills:
- At least five years of applied ML experience, including leading complex technical work and taking models into production.
- Strong knowledge of machine learning and deep learning, with experience selecting methods, designing experiments, and evaluating results.
- Strong Python skills and experience writing tested, maintainable software.
- Hands-on experience building and evaluating LLM-based systems, including retrieval, tool use, or agent orchestration.
- The ability to turn unclear requirements into testable problems, drive work independently, and explain technical decisions clearly.
- A STEM degree or equivalent practical experience.
Preferred Qualifications, Capabilities, and Skills:
- Experience fine-tuning models or optimizing inference, using frameworks such as PyTorch or TensorFlow.
- Experience deploying and operating ML systems on cloud platforms, particularly AWS services such as SageMaker or EKS.
- Programming experience in Java, C/C++, or another production language.