Own well-scoped ML components or models: You will take ownership of specific components, executing on defined problems with guidance
Train and deploy models using established patterns, and ship models to production following existing tooling and standards
Monitor and debug models with help from senior engineers, and work to improve model performance incrementally
Implement AI governance, privacy, and security requirements as defined
Collaborate within the team and with partner functions, while communicating progress, risks, and blockers clearly

Requirements

Degree or recent experience relating to Machine Learning
Familiarity with implementing safe, ethical, and compliant ML systems (familiarity with ISO 42001/NIST AI RMF and the associated common controls)
Experience deploying, monitoring, and improving ML models at a technology company
A strong grasp of Python
Experience training and experimenting with deep learning models as well as serving them in production
Experience with transformers and other HuggingFace libraries
Experience building and consuming APIs
An ability to build consensus while creating space for others
Excellent prioritization and time management skills
Experience with NLP and large language models, a plus
Experience with machine learning models which are not deep learning (e.g. decision trees), a plus
Experience using Docker and AWS (SageMaker endpoints, SageMaker notebooks, S3, IAM), a plus

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

Machine LearningPythonDeep LearningTransformersHuggingFaceAPIsNLPDecision TreesDockerAWS

Soft Skills

Consensus BuildingCommunicationPrioritizationTime Management

Certifications

ISO 42001NIST AI RMF