Machine Learning Engineer - Modeling & Algorithms

Machine Learning Engineer – Modeling & Algorithms Job Openings Careers Jobs Data Machine Learning Engineer – Modeling & Algorithms Machine Learning Engineer – Modeling & Algorithms Data Machine Learning São Paulo, SP Remote SHARE

Why join us TRACTIAN is transforming the industrial world by empowering frontline maintenance workers to achieve more. We’ve fused cutting-edge hardware with innovative software into one powerful platform, disrupting legacy systems and delivering smarter, faster solutions for our clients. Data Science at TRACTIAN The Data Science team at TRACTIAN focuses on extracting valuable insights from vast amounts of industrial data. Using advanced statistical methods, algorithms, and data visualization techniques, this team transforms raw data into actionable intelligence that drives decision-making across engineering, product development, and operational strategies.

The team constantly works on optimizing prediction models, identifying trends, and providing data-driven solutions that directly enhance the company’s operational efficiency and the quality of its products.

What you'll do We are looking for a Machine Learning Engineer who focuses on the mathematical and algorithmic side of the product, but who is also capable of implementing their own solutions. You will be responsible for the full development cycle of a model: from statistical analysis and prototyping to writing the production code and APIs that integrate the model into our platform.

Responsibilities • Algorithm Development: Design and train models to solve specific physical problems (e.g., machine uptime detection or production count prediction). • Signal Processing: Apply statistical methods to raw time-series data to extract meaningful features and reduce noise. • Validation: Define and monitor metrics (accuracy, recall, precision) to validate model performance on real-world data before and after deployment. • Model Serving: Develop and maintain RESTful APIs (using frameworks like FastAPI) to expose your models for real-time inference. • Production Standards: Write clean, modular, and testable Python code. You are expected to use version control, write unit tests, and follow software design patterns. • Performance Optimization: Profile and optimize model inference code to ensure low latency and efficient resource usage.