Member of Technical Staff

Edviro; San Francisco (CA); Sur place; USD 140 000 - 210 000; Plein temps

Résumé du poste

Edviro is seeking a capable engineer to model electrical, thermal, and energy systems for data-center environments. You will develop agents that plan and execute within safety limits and connect models to live facility systems, validating decisions against real operating conditions.

You’ll contribute directly to early data-center deployments alongside the founders. The ideal candidate is a strong programmer with depth in ML, scientific computing, or systems engineering, and can tackle unfamiliar

Qualifications

• Strong programmer with depth in machine learning, scientific computing, or systems engineering.

Responsabilités

• Model electrical, thermal, and energy systems using sparse, noisy data.
• Build agents that plan and execute tasks within equipment and safety limits.
• Connect models to facility systems and test decisions against real operating conditions.

Connaissances

Programming

Machine learning

Scientific computing

Systems engineering

Description du poste

The work

• Model electrical, thermal, and energy systems using sparse, noisy data.
• Build agents that plan and execute tasks within equipment and safety limits.
• Connect models to facility systems. Test their decisions against real operating conditions. You’ll be helping lead directly on our first data-centers deployments.

Your first 90 days

Build and deploy a power-and-cooling model for a live data-center pod. Connect electrical load, rack temperatures, and cooling equipment behavior to identify bottlenecks, estimate usable capacity, and simulate how operating changes affect power use, temperatures, and equipment limits.

Put that model behind Edviro’s agents so an operator can ask, “Can we add more load here, what needs to change, and what could go wrong?”

You

You’re a strong programmer with depth in machine learning, scientific computing, or systems engineering. You can take an unfamiliar problem, understand the fundamentals, and build a solution without a detailed spec.

Experience with physical systems, time-series modeling, or controls is useful. We care more about the quality of your work than the length of your résumé.

Send us something technically difficult you’ve built or researched. Tell us what you contributed and what you learned.

Founders

Hursh Shah coauthored & presented at NeurIPS 2024 with a paper on quantum vision transformers for MRI analysis. At 16, he cofounded an AR aircraft-inspection startup, leading its AI and edge-computing systems. Tanuj Siripurapu worked on low-latency neural-network inference on RISC-V FPGA hardware for RF signal classification project at RTX and leads Edviro’s integrations, and deployment engineering. You’ll build directly alongside both founders.