Build the infrastructure
that AI runs on.

Our Values

Diversity & Inclusion, Ethics & Integrity, Sustainability, Tolerance & Respect, Excellence, and Innovation guide how we work, not as slogans, but as how we make daily decisions. Above all, we are looking for people who are hungry to grow, restless enough to keep pushing past what they already know, and genuinely excited to learn something new every week.

The Role

We're hiring a hands-on Technical Lead to take our AI-driven data centre infrastructure platform from prototype to production. This isn't a maintenance role — you'll architect, build, deploy, and scale the backend, own critical infrastructure decisions, and stay hands-on in implementation. Startup pace, high ownership, wearing multiple hats.

Key Responsibilities

• Own platform architecture and lead the transition from prototype to production-grade deployment.
• Design and run cloud infrastructure, CI/CD, and DevOps automation for reliable operations.
• Build and maintain backend systems, APIs, orchestration services, and operational tooling.
• Translate thermal, power, and closed-loop control requirements into platform architecture (no direct OT/industrial-systems engineering required).
• Support optimization and ML systems involving multi-objective decision-making.
• Set the bar for security, uptime, observability, and incident response.
• Give realistic estimates and flag risks or scope changes early, not after they've become a problem.
• As we grow: mentor, weigh in on hiring, and help set technical standards.

Requirements

• 6+ years in backend, infrastructure, or platform engineering, with real product experience (no formal "lead" title required if you're ready to act like one).
• Track record taking a product from MVP to production.
• Strong hands-on cloud experience (AWS, Azure, or GCP), plus CI/CD, infrastructure automation, and Docker/Kubernetes.
• Solid backend/API development and experience running high-uptime production systems.
• Security and infrastructure-hardening fluency.
• Startup experience — comfortable operating independently with ambiguity.
• Realistic scoping/estimation, and a habit of surfacing risk early.
• Depth in at least one of: optimization/ML infrastructure, multi-objective decision systems, or telemetry/monitoring at scale.

Nice to Have

• Background in AI infrastructure, data centers, energy systems, or industrial automation.
• Real-time orchestration, distributed systems, or edge computing.
• MILP solvers or operational AI/optimization frameworks.
• Liquid cooling, thermal optimization, or power-aware infrastructure.
• Battery management, power electronics, or energy dispatch systems.
• Founding engineer / early-stage technical leadership experience.
• SOC 2, ISO 27001, or similar compliance framework familiarity.

Benefits

• Milestone-based performance bonus.
• Stock options.
• Extended health and dental.
• Real room to grow as the engineering org scales.

If you get restless solving the same problem twice, and you would rather be the one pushing the boundary than waiting for someone else to, we would like to hear from you.

Our Values

Diversity & Inclusion, Ethics & Integrity, Sustainability, Tolerance & Respect, Excellence, and Innovation guide how we work, not as slogans, but as how we make daily decisions. Above all, we are looking for people who are hungry to grow, restless enough to keep pushing past what they already know, and genuinely excited to learn something new every week.

The Role

The Heat Transfer Scientist is a full-time, on-site role based in Vancouver, BC, focused on researching, designing, and optimizing advanced cooling technologies for AI data centres.

Day-to-day responsibilities include developing and validating thermal models, conducting laboratory and on-site experiments, and analyzing heat transfer performance for liquid and hybrid cooling systems. The role involves collaborating with mechanical, electrical, and data-centre engineering teams to translate thermal insights into scalable cooling solutions, and supporting prototype design, testing, and iteration.

The Heat Transfer Scientist will document findings, prepare technical reports, and contribute to patents and publications while ensuring solutions align with sustainability, safety, and reliability requirements.

Key Responsibilities

• Develop and validate thermal models for advanced cooling technologies used in AI data centres.
• Conduct laboratory and on-site experiments to evaluate heat transfer performance for liquid and hybrid cooling systems.
• Analyze experimental and field data to assess thermal performance and identify opportunities for improvement.
• Collaborate with mechanical, electrical, and data-centre engineering teams to translate thermal insights into scalable cooling solutions.
• Support prototype design, testing, and iteration for new cooling technologies.
• Document findings and prepare technical reports for internal and external use.
• Contribute to patents and technical publications arising from research and development work.
• Ensure all solutions align with sustainability, safety, and reliability requirements.

Qualifications

• Strong foundation in heat transfer, fluid mechanics, and thermodynamics, with experience applying these principles to cooling systems or thermal management.
• Proficiency in thermal modeling and simulation tools (e.g., CFD, FEA, or equivalent) and data analysis software for interpreting experimental results.
• Hands-on experience designing, building, or testing thermal or liquid-cooling prototypes, including experimental setup, instrumentation, and measurement.
• Knowledge of data-centre or electronics cooling, energy efficiency, and sustainable engineering practices is highly beneficial.
• Ability to work collaboratively in cross-functional teams, communicate complex technical concepts clearly, and document methods and results rigorously.
• Master's or PhD in Mechanical Engineering, Chemical Engineering, Applied Physics, or a closely related field, or equivalent practical experience.
• Experience contributing to intellectual property (patents) or peer-reviewed publications in thermal sciences or related domains is an asset.
• Commitment to inclusive, safe laboratory and work practices, and openness to learning within a fast-evolving AI infrastructure environment.