Machine Learning Contractor (Part-Time)

About Avolta

Avolta is a leading innovator in security solutions, dedicated to protecting critical assets and enhancing safety across industries. With a strong focus on automotive security, we develop advanced anti-theft systems designed to safeguard vehicles and protect drivers and passengers.

By leveraging artificial intelligence, machine learning, and computer vision, we build deployable technologies that address complex and evolving security threats in the automotive sector and beyond. Our team is composed of high-performing professionals committed to technical excellence, integrity, and the mission of creating a safer future.

Position Overview

Avolta is seeking a part-time Machine Learning Contractor to contribute directly to the development of intelligent detection and automotive anti-theft systems.

This is a paid contract position (10–20 hours per week) intended for capable engineers and researchers who can independently develop, evaluate, and improve machine learning models with real-world deployment considerations. This position is impact-driven: contractors are expected to produce measurable technical contributions from the outset.

You will work on applied machine learning and computer vision challenges, including detection, classification, anomaly recognition, and predictive modeling in automotive security environments.

Compensation (Greater Toronto Area market-aligned):

CAD $55–$80 per hour, depending on experience, research depth, and demonstrated applied ML capability.

Key Responsibilities

• Develop, train, and optimize machine learning models, with emphasis on computer vision applications for security and automotive anti-theft systems.
• Design and implement data preprocessing pipelines and feature engineering strategies.
• Evaluate model performance using appropriate statistical and experimental methodologies.
• Contribute to research and development initiatives exploring advanced ML techniques (e.g., deep learning, anomaly detection, edge inference).
• Support deployment of models into production environments, including optimization for latency and scalability.
• Collaborate with software, mobile, and embedded engineering teams to integrate ML components into broader system architecture.
• Maintain clear technical documentation of experiments, architectures, and results.
• Participate in structured technical discussions, sprint reviews, and planning sessions.
• Uphold high standards of professionalism, confidentiality, and technical integrity.

Required Qualifications

• Completed Bachelor’s degree in Computer Science, Machine Learning, Artificial Intelligence, Electrical Engineering, or a related field.
• 1–3+ years of hands-on experience in applied machine learning (research, industry, startup, or substantial project-based work).
• Strong understanding of machine learning fundamentals (supervised/unsupervised learning, model evaluation, overfitting, bias-variance tradeoffs).
• Practical experience with deep learning frameworks such as PyTorch or TensorFlow.
• Proficiency in Python and familiarity with ML/data libraries (NumPy, Pandas, OpenCV, etc.).
• Experience designing experiments and interpreting model performance metrics.
• Familiarity with version control systems (e.g., Git).
• Strong analytical and problem-solving skills.
• Ability to operate independently in a contractor capacity.

Preferred Qualifications

• Master’s or PhD in Machine Learning, Artificial Intelligence, Computer Vision, or related discipline.
• Experience in computer vision applications (object detection, tracking, segmentation, OCR, etc.).
• Familiarity with model optimization for edge devices or embedded systems.
• Experience deploying ML models in production or cloud environments (AWS, Azure, Google Cloud).
• Understanding of cybersecurity, anomaly detection, or behavioral modeling.
• Experience working with real-world, imperfect datasets.
• Interest or experience in automotive systems, telematics, or physical security technologies.

Job Category: AI/Machine Learning

Job Type: Part Time

Job Location: In-Person