Founding Senior Machine Learning Engineer

SentiLink provides innovative identity and risk solutions, empowering institutions and individuals to transact with confidence. They are seeking a founding Senior Machine Learning Engineer to help scale and operationalize their ML systems end-to-end, focusing on building the infrastructure and processes for efficient model development and deployment.

Responsibilities:

• Own SentiLink’s real-time ML model monitoring domain, leading the design, implementation, and ongoing improvement of monitoring systems and workflows
• Own our ML experimentation, model tracking, and versioning infrastructure, ensuring strong reproducibility and visibility across the model lifecycle
• Drive improvements to the model development process, reducing inefficiencies, improving code quality, resolving DS tooling gaps, and enabling faster iteration
• Serve as the primary technical owner of key touchpoints and interfaces between Data Science and Engineering/Infrastructure, defining standards and workflows
• Support efforts to optimize model behavior in production, including latency, reliability, maintainability, and operational best practices
• Investigate and diagnose model performance issues on an ad-hoc basis, including partner escalations and analysis of model behavior in real-world scenarios
• Evaluate, prototype, and recommend new ML infrastructure, tools, and data capabilities, partnering with DS to validate impact and support adoption

Requirements:

• 5+ years of relevant experience, with a degree in Computer Science, Engineering, Mathematics, or a related technical field
• Strong software engineering fundamentals, with proficiency in Python and SQL, and strong working knowledge of Git and modern CI/CD workflows
• Hands-on experience with ML experimentation and model tracking tools
• Strong proficiency with model monitoring and observability tooling
• Experience with ML infrastructure and orchestration technologies, such as Docker, Kubernetes, and workflow orchestration frameworks
• Familiarity with model serving and deployment frameworks
• Proven experience deploying and operating machine learning models as production services, with an emphasis on reliability and performance
• Demonstrated ability to build 0-to-1 prototypes and proof-of-concepts, rapidly standing up ML services and experimentation environments
• Experience designing, building, and optimizing ML pipelines for training, evaluation, and deployment
• Highly adaptable and able to learn quickly in fast-moving environments with evolving technical requirements
• Candidates must be legally authorized to work in the United States and must live in the United States