Senior Machine Learning Engineer

Requirements

• Design, train, validate, and deploy ML models (classification, regression, anomaly detection, time-series forecasting) on structured and unstructured sensor data.
• Develop and maintain feature engineering pipelines that extract meaningful signals from noisy, high-frequency biomechanical and environmental sensor data.
• Evaluate and integrate emerging ML techniques relevant to wearable and embedded sensor applications.
• Design and implement agentic AI architectures that leverage large language models (LLMs) to automate multi-step analytical workflows.
• Develop prompt engineering strategies, few-shot learning pipelines, and feedback loops that continuously improve agent output quality.
• Evaluate cost, latency, and accuracy trade-offs across LLM providers and deployment configurations.
• Contribute to the project codebases, primarily in Python, ensuring production-quality, well-tested, and documented code.
• Work within an AWS-native stack (Lambda, RDS, S3, Bedrock, SageMaker) to build and maintain scalable ML and analytics infrastructure.
• Participate in architecture reviews, code reviews, and sprint planning within an Agile/SAFe framework.
• Strong proficiency in Python, including ML/data libraries (scikit-learn, pandas, NumPy, SciPy) and production frameworks.
• Demonstrated experience building and deploying ML models on sensor, time-series, or IoT data in production environments.
• Solid foundation in statistics and experimental design, with the ability to select and apply appropriate methods independently.
• Experience with AWS services (EC2, Lambda, S3, RDS) or equivalent cloud platforms.
• Effective communicator who can explain technical concepts to non-technical stakeholders.

Benefits

• Paid time off
• 401k matching
• Health insurance
• Dental insurance
• Vision insurance
• Retirement plan
• Stock options
• Professional development opportunities