Julia MTK Modeling Engineer

P-1 AI

23d•$150 - $200•Remote

About The Position

We are a small team tackling an ambitious problem. If we are successful, it will change the course of history. As such, we have a very high talent bar and are looking for people who have done something remarkable. This role builds the physics simulators that teach Archie how the real world works. You will develop first-principles, acausal models of thermofluid and electrical systems and use them to generate large-scale synthetic datasets that power Archie’s reasoning about physical systems. You will work on chillers, air handlers, cooling towers, hydronic networks, switchboards, transformers, and distribution systems—sweeping your models across thousands of operating conditions, fault modes, and design variants. The fidelity of your simulations directly determines the capabilities of our AI. You will collaborate closely with ML engineers to ensure simulation outputs translate into real-world performance. We don’t care if you’ve done it before. We just need you to be brilliant, mission-driven, and thirsty to learn. This role can be either remote (based in the US or Canada and with existing work authorization) or based in our SF office. If you are remote, you should plan to spend one week out of six co-working with the rest of the company in our SF office. We will support relocation for candidates interested in moving to SF.

Requirements

have done something remarkable, and have undeniable real-world proof-of-talent you can share with us
go from 0 → 1 on an idea before breakfast
always learning
believe in manifesting the future of physical engineering
be brilliant
be mission-driven
be thirsty to learn

Responsibilities

builds the physics simulators that teach Archie how the real world works
develop first-principles, acausal models of thermofluid and electrical systems
generate large-scale synthetic datasets that power Archie’s reasoning about physical systems
work on chillers, air handlers, cooling towers, hydronic networks, switchboards, transformers, and distribution systems—sweeping your models across thousands of operating conditions, fault modes, and design variants
collaborate closely with ML engineers to ensure simulation outputs translate into real-world performance