Staff Reliability Engineer: AI Hardware Strategy & Leadership

Tenstorrent

Toronto

Hybrid

CAD 140,000 - 200,000

Full time

14 days+

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Benefits offered by this job

Competitive compensation & benefits

Job summary

Tenstorrent in Toronto, Canada, is seeking a Staff Reliability Engineer to shape the reliability strategy for next‑generation AI computing systems. In this highly visible technical leadership role, you will drive reliability from architecture to production, partnering across hardware, software, and manufacturing teams to deliver uptime, durability, and quality.

We welcome candidates at various experience levels; the offer aligns with level and is based in a hybrid setup.

Qualifications

  • 8+ years in reliability engineering, preferably in AI hardware or HPC.
  • Experience with HALT, HASS, ALT, MTBF, Weibull analysis and FMEA.
  • Ability to communicate risks and trade-offs to leadership.
  • Proven ability to lead cross-functional teams across hardware, software and manufacturing.

Responsibilities

  • Set the reliability strategy for next‑gen AI computing systems.
  • Lead root‑cause investigations and implement fixes across engineering and supply chain.
  • Mentor engineers and lead design reviews to raise the bar.
  • Collaborate with data center and workstation teams for reliability across products.

Skills

Reliability engineering
High-performance computing
AI hardware
Data center systems
Thermal/Cooling analysis
Weibull analysis
FMEA
Root-cause investigations
Leadership
Cross-functional collaboration

Tools

Thermal testing equipment

Job description

Tenstorrent in Toronto, Canada, is seeking a Staff Reliability Engineer to shape the reliability strategy for next‑generation AI computing systems. In this highly visible technical leadership role, you will drive reliability from architecture to production, partnering across hardware, software, and manufacturing teams to deliver uptime, durability, and quality.

We welcome candidates at various experience levels; the offer aligns with level and is based in a hybrid setup.

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