Remote Machine Learning Engineer

Description:

• The Data Science team at TRACTIAN focuses on extracting valuable insights from vast amounts of industrial data.
• This team uses advanced statistical methods, algorithms, and data visualization techniques to transform raw data into actionable intelligence.
• The insights drive decision-making across engineering, product development, and operational strategies.
• The team works on optimizing prediction models, identifying trends, and providing data-driven solutions to enhance operational efficiency and product quality.
• The Mid-Level Machine Learning Engineer will bridge the gap between data science and production systems.
• Responsibilities include owning the end-to-end deployment of machine learning models, working with real-time sensor data, and building reliable services for diagnostics of industrial equipment.
• This is a hands-on role with real impact, ideal for engineers looking to grow their systems design and ML Ops skills.

Requirements:

• Candidates must have 2–4 years of experience in software or machine learning engineering.
• A Bachelor’s degree in Computer Science, Engineering, or a related technical field is required.
• A solid background in math, statistics, and machine learning concepts is necessary.
• Strong Python skills and experience with ML libraries like scikit-learn or PyTorch are essential.
• Experience deploying models in production environments is required.
• Familiarity with event-driven platforms and message queues, such as Kafka or Redis Streams, is necessary.
• Candidates should be comfortable working with streaming or time-series data.

Benefits:

• The position offers the opportunity to work with large-scale time-series datasets from vibration and sensor systems.
• Engineers will have the chance to improve the performance and reliability of model serving pipelines.
• The role includes monitoring system health and implementing logging, alerting, and fallback mechanisms.
• There is an opportunity to contribute to architectural decisions and collaborate across teams.
• Preferred qualifications include experience with containerization (Docker) and cloud deployment, exposure to real-time or low-latency systems, and an interest in optimizing inference latency and resource usage.