Joblaze summary

In this role, the engineer is responsible for managing a fleet of GPUs across multiple providers, ensuring that compute resources are efficiently utilized for research purposes. Key skills include expertise in fleet management, scheduling, and reliability engineering, with a focus on maximizing performance and minimizing downtime. This position is suited for experienced professionals with a strong background in systems engineering or cloud infrastructure. Mirendil is a tech-driven company aiming to advance AI research, making this role critical to its mission.

Joblaze insights

Listed 5 days ago — first seen on Joblaze November 6, 2026. Last confirmed on Mirendil's careers page November 10, 2026.
Salary band is above the typical range for AI/ML roles (median ~$189,154).
Starts above 65% of 54 comparable staff ai/ml roles in United States that list AI/ML we track (median $248,000 across 22 companies). See AI/ML salary trends
AI/ML appears in 36.3% of 270 comparable staff ai/ml roles in United States.

Quick facts

What's the salary range?: Mirendil lists $300,000–$400,000 for this role.
What's the tech stack?: Joblaze extracted these technologies from the posting: AI/ML.
What seniority level is this role?: Mirendil targets staff-level candidates for this position.
Is this full-time or contract?: Full-time for this Member of Technical Staff, Compute role at Mirendil.

From the original posting

The Role

Our research moves only as fast as our compute. We’re looking for an engineer who can turn a lot of GPUs across a lot of providers into one fleet that researchers never have to think about. Your work determines how much of our compute actually goes to research. Some example areas you might work on:

Fleet bring-up and lifecycle - take new clusters from delivered to running real jobs fast, and keep them healthy as they age
Scheduling and allocation - decide what runs where, so pretraining, RL, and inference share capacity without fighting over it
Reliability at scale - detect and route around failing hardware automatically, before it costs us a training run
Capacity strategy - plan with research what compute we’ll need, and help decide what we buy, from whom, and when
Utilization - measure what every GPU-hour produces, and keep raising it

We offer a base salary of $300,000–$400,000 and a meaningful equity grant, depending on experience and background, along with competitive benefits.

Standard company text repeated across Mirendil's postings is omitted here.

Member of Technical Staff, Compute

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