Machine Learning Engineer
- Winnipeg, MB, Canada
Full-time
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Job details
- Job type
- Full-time
- Apply by
- Experience
- 3+ years of experience developing, deploying, and optimizing ML models, with a focus on computer vision and embedded systems.
- Posting language
- English
- Posted
Original job description
Solutions for Every Season. We engineer and deliver precision agriculture hardware, software, and cloud-based platforms that connect every corner of the farm. At PTx, we are redefining the future of agriculture through autonomy. Our focus is on building physical AI systems, where machine learning models directly interact with and control real-world machines operating in complex outdoor environments.
We are seeking a Machine Learning Engineer to develop embedded perception models and the infrastructure that supports them. This role is well-suited for a technically strong, hands-on individual who excels at solving complex, real-world challenges while maintaining a system-level perspective and delivering measurable impact in the field.
In this role, you will take ownership of high-level application objectives and drive them from concept through deployment. You will work closely with cross-functional teams to define problems, shape data strategies, and ensure solutions are robust, scalable, and production-ready.
Your Impact
You will collaborate closely with PTx colleagues in Winnipeg, Canada, in an onsite environment that supports hands-on testing, teamwork, and innovation.
We are committed to fostering an inclusive workplace and believe that diverse perspectives drive better outcomes for our customers, our communities, and our teams. Our approach to recruitment reflects that commitment by building teams that represent a broad range of experiences, backgrounds, cultures, and perspectives.
If you are contacted regarding an opportunity with us, you may request reasonable accommodation for the materials or activities used throughout the selection process.
Join us as we bring agriculture into the future and apply now!
We are seeking a Machine Learning Engineer to develop embedded perception models and the infrastructure that supports them. This role is well-suited for a technically strong, hands-on individual who excels at solving complex, real-world challenges while maintaining a system-level perspective and delivering measurable impact in the field.
In this role, you will take ownership of high-level application objectives and drive them from concept through deployment. You will work closely with cross-functional teams to define problems, shape data strategies, and ensure solutions are robust, scalable, and production-ready.
Your Impact
- Design, develop, and deploy computer vision and perception models for real-time operation on autonomous agricultural machines.
- Own the full ML lifecycle, including dataset design, data collection, training, evaluation, optimization, and deployment.
- Build and enhance ML infrastructure for embedded systems, including tools for validation, monitoring, and continuous improvement.
- Collaborate with robotics, embedded, perception, and systems engineering teams to integrate ML solutions into production environments.
- Prototype, validate, and continuously improve models using real-world and synthetic data to enhance detection, segmentation, and scene understanding under challenging conditions (e.g., dust, occlusion, lighting) and ensure robustness across diverse crops, terrains, and climates.
- Strong fundamentals in machine learning and computer vision.
- 3+ years of experience developing, deploying, and optimizing ML models, with a focus on computer vision and embedded systems.
- Experience with ML Ops practices and infrastructure development.
- Hands-on experience with GPU-based computing and embedded deployment (e.g., Jetson or similar platforms). Proficiency with Docker and ROS2. Experience in robotics, autonomous vehicles, or real-time systems is a strong plus.
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, with strong interpersonal skills and a collaborative, team-oriented mindset.
- Three (3) weeks of vacation per year.
- Employee Stock Purchase Plan offering eligible employees the ability to purchase AGCO stock at a discounted price.
- Group retirement plan with employer match.
- Group insurance paid at 75% by the employer.
- Employee Assistance Program.
You will collaborate closely with PTx colleagues in Winnipeg, Canada, in an onsite environment that supports hands-on testing, teamwork, and innovation.
We are committed to fostering an inclusive workplace and believe that diverse perspectives drive better outcomes for our customers, our communities, and our teams. Our approach to recruitment reflects that commitment by building teams that represent a broad range of experiences, backgrounds, cultures, and perspectives.
If you are contacted regarding an opportunity with us, you may request reasonable accommodation for the materials or activities used throughout the selection process.
Join us as we bring agriculture into the future and apply now!
More about this job
Responsibilities
Design, develop, and deploy computer vision and perception models for autonomous agricultural machinery. Manage the full ML lifecycle while collaborating with cross-functional teams to integrate solutions into production environments.
Requirements
Requires 3+ years of experience in machine learning, computer vision, and embedded systems deployment. Proficiency in Docker, ROS2, and GPU-based computing is essential for this role.
Skills
- Machine learning
- Computer vision
- Embedded systems
- ML Ops
- GPU computing
- Docker
- ROS2
- Robotics
- Autonomous vehicles
- Perception models
- Data strategy
- System-level design
- Python
- C++
- Real-time systems
Benefits
- Three weeks of vacation
- Employee stock purchase plan
- Group retirement plan with employer match
- Group insurance
- Employee assistance program
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Engineering
- Agriculture
- Software
- Manufacturing
Keywords
- Machine Learning
- Computer Vision
- Embedded Systems
- Autonomous Agriculture
- Perception Models
- ML Ops
- GPU Computing
- Docker
- ROS2
- Robotics
- Autonomous Vehicles
- Real-time Systems
- Data Collection
- Model Training
- Model Evaluation
- Precision Agriculture
- Cloud-based Platforms
- Synthetic Data
- Scene Understanding
- Segmentation
- Detection
- System Engineering
- Production-ready
- Scalability
- Hardware Engineering