Machine Learning Engineer

You will take loosely defined or complex business and operational problems and determine how to solve them using machine learning. This involves clarifying requirements, designing an approach, and selecting the right algorithms and architectures (e.g., supervised learning, CNNs).

You will design, implement, and train ML models using frameworks like Py Torch and Tensor Flow
, leveraging data tools like Pandas for preprocessing and analysis. The process will include:

• Data gathering
• Cleaning and preprocessing
• Model training and evaluation
• Optimization for performance and efficiency
• Deployment to production environments

You will work with large and varied datasets — including images, multi-spectral sensor outputs, voice, text, and tabular data — and develop preprocessing strategies to make this data usable for machine learning models.

You will partner with production, process, controls, and quality teams to understand operational pain points and design ML-based solutions that integrate seamlessly into existing workflows and systems.

You will own models after deployment, setting up robust alerting and monitoring systems to track performance, detect issues, and initiate quick fixes when needed.

You will improve speed and efficiency through quantization, pruning, and Tensor

RT conversion
, ensuring that models meet performance requirements in real-world environments — including embedded or firmware-integrated contexts (leveraging C++ if needed).

You will use expertise in linear algebra, geometry, probability theory, numerical optimization, and statistics to design models, assess feasibility, and ensure rigorous evaluation.

Depending on the project, you may work on problems in computer vision, large language models, recommender systems, or operations research
, applying domain-specific techniques to deliver maximum value.

You will produce clean, modular, and maintainable code to ensure that ML solutions are scalable and easy to update, supporting long-term sustainability of deployed systems.

Rapid prototyping of algorithms for high-performance, data-intensive applications
.

Optimization for speed, efficiency, and scalability in production environments.