Data & ML Engineer

Jetson Home•Kitchener, ON

2d•Onsite

About The Position

Jetson is on a mission to accelerate the transition of 100 million homes across North America away from fossil fuels toward sustainable energy use. We believe in a future that is 100% electric and 100% better. Homes are one of the largest sources of carbon emissions, yet adoption of solutions like heat pumps remains slow due to cost and complexity. Jetson is solving this by building the first fully vertically integrated home electrification company — making clean energy simple, transparent, and affordable. We rely heavily on technology, automation, and data to scale this impact. At Jetson, we value people who are excellent at their craft, curious about new tools (including AI), and motivated to continuously improve how work gets done. The Opportunity: Build ML systems that interact with the physical world. You’ll gain experience applying data and machine learning to real-world control problems—where feedback loops, delayed effects, and physical constraints matter as much as algorithms. See the full picture, and influence it. At Jetson, you’ll see how data flows from devices in homes, through cloud systems, into models, and back into product behavior. That context will make you a stronger engineer and a better decision-maker. Shape our data and ML foundations. We’re early, and that’s an advantage. You’ll help define our data architecture, modeling standards, evaluation practices, and how we responsibly use AI to move faster and build better products.

Requirements

Comfortable operating without a playbook. You thrive in environments where the data is messy, the problem isn’t fully defined, and the right answer isn’t obvious. You’re able to frame problems, make reasonable assumptions, and iterate quickly as reality pushes back.
Someone who thinks in terms of systems, not just models. You care about how data is generated, how it flows through systems, how models are deployed, and how outputs actually influence product behavior. Accuracy matters - but so does latency, reliability, and explainability.
An empathetic collaborator. You can work effectively with product, firmware, backend, and design partners - translating between business goals, physical constraints, and technical implementation. You communicate clearly and value shared ownership over individual heroics.

Nice To Haves

Experience with time-series data, optimization, or control systems is a plus.

Responsibilities

Ownership of data and ML systems end-to-end. You’ll design, build, and maintain pipelines and models that power core product functionality—energy optimization, scheduling intelligence, anomaly detection, forecasting, and more. This includes data ingestion, feature engineering, training, deployment, monitoring, and iteration.
Strong foundations in data engineering. You’re comfortable working with production data systems: streaming and batch pipelines, data modeling, warehouses/lakes, and observability. You know from experience that reliable ML starts with reliable data.
Applied ML experience. You’ve built and shipped models into production, and you understand the tradeoffs involved - model complexity vs. robustness, retraining cadence, drift detection, and graceful failure modes. Experience with time-series data, optimization, or control systems is a plus.
Systems and API design judgment. You’ll design interfaces between ML systems, backend services, and edge devices. You think carefully about contracts, versioning, and what happens when predictions are wrong or unavailable.
Engineering rigor. You write maintainable, well-tested code. You value code review, automated testing, and CI/CD—not as overhead, but as enablers of speed and reliability in production ML systems.