S
Artificial Intelligence Engineer
Stelvio Inc.
3 hours ago
Full-time
On-site
Austin, Texas, United States
Austin’s AI builders - what would make your next move worth it?
More difficult technical problems? Greater ownership? The opportunity to shape how an ambitious AI company builds its core technology?
I’m supporting an
Austin-based AI start-ups
that would like to connect with exceptional
Lead, Principal and deeply experienced Senior AI/ML Engineers. The conversations will centre on what you’ve personally built, the decisions you’ve owned, and how your systems performed when real customers depended on them. The experience I’m keen to hear about: Deep model development:
Designing, training or substantially adapting models, with a clear understanding of the choices behind the architecture, data and training approach. Production engineering:
Taking AI systems through deployment, evaluation and ongoing improvement, balancing model quality with latency, reliability and cost. Complex problem ownership:
Working through ambiguous requirements, difficult failure modes and interconnected systems where the answer takes investigation. Technical leadership:
Setting direction, challenging assumptions and raising engineering standards while remaining close to the code. You may have built this experience in a leading research or engineering organisation, an ambitious start-up, or across both. I’m particularly interested in engineers who can explain
what they built, why they built it that way, what broke, and what they changed. The appeal here is the scope: technically demanding work, substantial ownership and the opportunity to influence core AI engineering decisions directly with the CTO.
#J-18808-Ljbffr
Austin-based AI start-ups
that would like to connect with exceptional
Lead, Principal and deeply experienced Senior AI/ML Engineers. The conversations will centre on what you’ve personally built, the decisions you’ve owned, and how your systems performed when real customers depended on them. The experience I’m keen to hear about: Deep model development:
Designing, training or substantially adapting models, with a clear understanding of the choices behind the architecture, data and training approach. Production engineering:
Taking AI systems through deployment, evaluation and ongoing improvement, balancing model quality with latency, reliability and cost. Complex problem ownership:
Working through ambiguous requirements, difficult failure modes and interconnected systems where the answer takes investigation. Technical leadership:
Setting direction, challenging assumptions and raising engineering standards while remaining close to the code. You may have built this experience in a leading research or engineering organisation, an ambitious start-up, or across both. I’m particularly interested in engineers who can explain
what they built, why they built it that way, what broke, and what they changed. The appeal here is the scope: technically demanding work, substantial ownership and the opportunity to influence core AI engineering decisions directly with the CTO.
#J-18808-Ljbffr