What are the responsibilities and job description for the Senior Machine Learning Engineer position at Simbex?
The Senior Machine Learning Engineer designs and deploys machine learning models that transform sensor data into actionable insights, build advanced analytics pipelines, and architect agentic AI solutions that automate complex analytical workflows. This role works at the intersection of data science, software engineering, and product development and turns raw biomedical and environmental sensor streams into intelligence that drives real-world decisions for Simbex's clients.
RESPONSIBILITIES
Machine Learning & Sensor Data:
- 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.
Agentic AI Architectures:
- 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.
Platform & Engineering:
- 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.
REQUIRED QUALIFICATIONS
- 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.
NICE-TO-HAVES
- 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.
Salary : $130,000 - $150,000