Tractian9mo ago

Machine Learning Engineer

São PauloRemoteFull-timemid

Data ScienceMachine Learning EngineerDataData & AI

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Quick Summary

Overview

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,

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Data ScienceMachine Learning EngineerDataData & AI

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’re hiring a Machine Learning Engineer to bridge the gap between data science and production systems. You’ll own end-to-end deployment of machine learning models, work with real-time sensor data, and build reliable services that power diagnostics for industrial equipment. This is a hands-on role with real impact, ideal for engineers who want to grow their systems design and ML Ops skills.

Deploy and maintain ML models from the data science team

Design and implement APIs and real-time inference services

Work with large-scale time-series datasets from vibration and sensor systems

Improve the performance and reliability of model serving pipelines

Monitor system health and implement logging, alerting, and fallback mechanisms

Contribute to architectural decisions and collaborate across teams

2–4+ years of experience in software or machine learning engineering

Bachelor’s degree in Computer Science, Engineering, or related technical field

Solid background in math, statistics, and machine learning concepts

Strong Python skills and experience with ML libraries like scikit-learn or PyTorch

Experience deploying models in production environments

Familiarity with event-driven platforms and message queues (e.g., Kafka, Redis Streams)