MACHINE LEARNING ENGINEER | FALC AI

Job Type: Permanent, Full-time

Available Locations: Ottawa | Toronto | Remote/Other

Leading the charge of developing cutting edge hardware systems.

Collaborative and highly skilled cross-functional team.

Shaping the future of computing in key industries, turning ideas into reality.

That is what we do at J-Squared.

Are you passionate about using disruptive technology to solve problems previously unachievable? Are you excited about the latest innovations that are allowing us to create more powerful hardware in smaller form factors? Do you like getting your hands dirty while driving results and collaborating with colleagues? If this interests you, then we have an opportunity waiting for you!

What will your typical day look like?

As a Machine Learning Engineer in J-Squared’s FALC-AI division, you will build and deploy the ML and deep learning models behind our AI products, working with computer vision, Large Language Models (LLMs), Vision-Language Models (VLMs), Generative AI, and Agentic AI.

Our work spans edge to cloud, from rugged NVIDIA Jetson and Hailo devices in the field to on-premise GPU servers and AWS. You will take models from research to real-world deployment in demanding industrial environments including Defence, Mining, Manufacturing and Retail.

Specifically, your responsibilities will include:

AI Model Development

1. Design, train, fine-tune, and evaluate deep learning models for computer vision and/or NLP tasks.
2. Work with multi-modal data, including images, video, text, and sensor signals.
3. Stay current with the latest research and turn promising ideas into working prototypes.

LLMs, Generative AI, and Agentic AI

1. Build LLM and VLM-powered applications including RAG pipelines and Agentic AI systems using tool calling and the Model Context Protocol (MCP).
2. Fine-tune openweight LLMs and VLMs and serve them in production with engines such as vLLM and TensorRT-LLM.
3. Evaluate generative models for accuracy, hallucination, latency, and cost.





Optimization and Edge-to-Cloud deployment

1. Deploy models across edge devices (NVIDIA Jetson, Hailo), GPU servers, and AWS.
2. Optimize models using quantization, pruning, distillation, and conversion workflows.
3. Build high-performance inference pipelines with tools such as NVIDIA Triton and gRPC.

ML System Design and Architecture

1. Own the design of end-to-end ML systems across edge and cloud, making architecture decisions that balance accuracy, latency, scalability, and reliability.
2. Build ML systems that are secure and cost-efficient, from protecting data, models, and LLM/agent endpoints to optimizing GPU and cloud spend at scale.

ML Lifecycle and Leadership

1. Own the ML lifecycle end to end from data and training to CI/CD, monitoring, drift detection, and retraining.
2. Lead ML feature streams and work closely with software, hardware, and product teams.
3. Mentor junior engineers and help set ML best practices.

About Our Team

J-Squared has over 30 years of experience excelling in operationally demanding performance environments. Our ruggedized products and solutions are innovative, needs driven, and focused on quality and reliability. Our Octagon Systems line of products is a global leader in rugged computer systems built for use in extreme environments such as mining, defence & military, transportation, and marine. We architect and manufacture systems that work no matter what.

J-Squared’s significant growth has resulted in the company expanding its team to meet this increased demand.

Enough About Us, Let’s Talk About You For a candidate to be successful in this role,



the key qualifications include:

- A bachelor’s or master’s degree in Computer Science, Computer Engineering, or a related field, or equivalent practical experience.
- 4-7+ years of skilled experience, with a track record of taking ML models to production.
- Strong Python skills with PyTorch or Keras, and a solid understanding of the ML lifecycle.
- Robust foundation in machine learning and deep learning fundamentals.
- Hands-on experience in Computer Vision and/or NLP (one or both is great).
- Experience with LLMs or LVLMs/VLMs, through training, fine-tuning, or inference.
- Experience deploying ML models on edge devices or in the cloud.
- Proven experience designing scalable, secure, and cost-efficient ML systems and making clear architecture trade-offs between performance, reliability, and cost.

It would be great if you also bring experience with:
- Experience building agentic AI systems and RAG pipelines, including AI agents, tool calling, vector databases, and the Model Context Protocol (MCP).
- Experience with CUDA programming and GPU optimization for ML models including profiling, custom kernels, and mixed-precision inference.
- Experience with AWS SageMaker and machine learning on AWS, including training jobs, model endpoints, and services such as S3 and EC2.
- Model optimization including but not limited to quantization, pruning, or knowledge distillation.

Take The Next Step

Please submit a cover letter and CV directly on our LinkedIn post. We would like to thank all applicants for their interest, however, only candidates under consideration will be contacted.

J-Squared respects the dignity and independence of people with disabilities and provides accommodation and support to persons with disabilities throughout any recruitment process, once made aware of a need for accommodation. If you require any special accommodation or support during the recruitment process, please indicate so in your application.

📌 Machine Learning Engineer (Ontario)
🏢 J-Squared Technologies Inc.®
📍 Ontario