Senior Machine Learning Engineer

Simbex is a company focused on transforming sensor data into actionable insights through advanced analytics. They are seeking a Senior Machine Learning Engineer to design and deploy machine learning models, build analytics pipelines, and develop AI solutions that automate complex workflows.

Responsibilities:

• 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, including data summarization, insight generation, and report authoring
• 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

Requirements:

• Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field
• 5+ years of professional experience in machine learning engineering, data science, or a closely related discipline
• 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
• Advanced degree in a relevant field
• Experience with Amazon Bedrock, SageMaker, or similar managed ML/AI services
• Familiarity with sports-tech, med-tech, biomechanics, or wearable sensor domains
• Experience with deep learning frameworks (PyTorch, TensorFlow) for signal processing or sequence modeling tasks
• Knowledge of data pipeline orchestration tools
• Hands-on experience with agentic AI or LLM-based systems (e.g., Bedrock Agents, LangChain, OpenAI function calling, custom tool-using agents)
• Experience with prompt engineering, retrieval-augmented generation (RAG), and fine-tuning LLMs