As an AI Engineer, you will develop and deploy computer vision models, monitor their performance, own AI projects, and collaborate cross-functionally.
Your opportunity
Our client is a well-funded, seed-stage AI startup that builds agents for the factory floor. They develop and distribute a software-first agent layer that plugs into the cameras and machines factories already have. Their models run and act at the edge so agents can see, decide, and act in real time. Events and metrics flow into a dashboard that provides plant teams immediate visibility. They’re approaching a large (~$14B) and underserved market with a disruptive, asset-light alternative to hardware-heavy robotics and batch analytics and they’ve already found early traction with clients in the food & beverage, pharma/cosmetics, and materials processing verticals.
As an AI engineer you’ll bridge the gap between academic research and industry impact. You’ll be joining a flat, dynamic environment in the midst of its scale-up phase that’s led by an accomplished ex-Deepmind researcher with specialization in reinforcement learning, deep learning and robotics. Your role will centre around training, deploying and monitoring neural networks responsible for visual inspection and activity detection workloads. You’ll work across the full stack of applied computer vision from dataset curation through training, benchmarking, drift monitoring and closing the loop with updates.
The company closed 2 significant funding rounds in the last 12 months and are scaling R&D and delivery to meet accelerating demand, with headcount tracking to double by year-end.
Key responsibilities
- Vision model development: Evaluating, training and deploying classification, detection, and segmentation models for manufacturing use cases
- Monitoring & retraining: Monitoring production performance, diagnosing drift and edge-case failures, and retraining to improve reliability and accuracy
- Project ownership: Taking end-to-end ownership of 3-4 concurrent AI projects, with 1-3 neural networks in scope per project
- Collaboration, documentation & field support: Work with cross-functional teams, maintain clear processes for reproducibility, and support in-field operations
Your know-how
- You have experience training and validating deep learning models (CNNs, transformers) in PyTorch and supporting their deployment and post-deployment lifecycle in production environments
- You have experience using NumPy for numerical computing, data processing, and model-related workflows
- You have experience working in Linux-based environments
- You have experience packaging and deploying applications with Docker
- You have experience working with SQL to query, transform, and analyze structured data
- You have experience collaborating effectively within and across cross-functional delivery teams
- You are a contagiously curious person with entrenched learning habits
- You have a fantastic command of English
It’s a bonus if
- You have an academic research background in machine learning, computer vision, and/or artificial intelligence (likely, but not necessarily, reflected in a graduate degree in these fields)
- You have experience solving manufacturing and/or industrial automation problems with software solutions
- You have deep expertise in computer vision, robotics, or manufacturing field deployments
- You have experience deploying vision models at the edge
- You have experience scaling an AI and/or B2B SaaS venture
Tech stack
- Operating system: Linux
- Containers: Docker
- GPU/acceleration: CUDA, TensorRT, ONNX, OpenVINO
- ML/DL frameworks: PyTorch, TensorFlow, Keras, scikit-learn
- Scientific computing: NumPy, Pandas
- Computer vision: OpenCV, YOLO
- Cloud providers: AWS, Azure, GCP
- Edge platforms: NVIDIA Jetson, Raspberry Pi (ARM)
- Cameras & vision I/O: GenICam, GigE Vision, USB3 Vision
- Backend: Python (Flask, FastAPI), TypeScript/Node.js
- Frontend: TypeScript/React
- Orchestration & compute: Kubernetes, on-prem bare metal, VMs
- Messaging & IoT: MQTT, HTTP/REST, RabbitMQ, Apache Kafka
- Databases & storage: SQL, InfluxDB, MongoDB
- Monitoring, observability & logging: Prometheus, Grafana, ELK
- Industrial automation: PLC integration; protocols: Ethernet/IP, Modbus, Profinet, OPC UA
Interested in learning more?
Please upload your resume or a .pdf export of your LinkedIn profile using the following “Apply Now” button, or send your resume or LinkedIn profile URL to [email protected] with “AI Engineer, Computer Vision” as the subject line. One of our talent partners will be in contact shortly.
CompensationThe base pay range for this role is CA$120,000 – CA$160,000 per year.
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