Machine Learning Engineer
Indexed description
We're building that layer today by deploying alongside the world's highest-stakes teams — Olympic delegations, F1 paddocks, halftime shows, global tours, studio productions, senior government officials, and executive protection units. What we learn there becomes the foundation for a civilization-defining capability.
We're a small, talent-dense team with high ownership, high velocity, and low ego. We care deeply, move fast, and are here to build something that outlasts us.
Together, we'll redefine cyber-physical security for the AI age.
What makes this role special?
- First dedicated AI systems hire
- You're the difference between a system that exists and one that works
- Ensure reliability of the entire AI system—from data ingestion to operator decision
- Turn noisy cyber-physical observations into trusted operational decisions
- Define how the system reasons under uncertainty
- Your work is used in high-stakes environments where outputs must be trusted
- Become the technical lead for Sweep's AI decision system before Series A
- 5-10 years owning production systems end-to-end
- Strong system design across APIs, pipelines, and data storage
- Built production AI systems trusted in real-world operations
- Strong Python, plus Go/TypeScript (or similar)
- Comfortable building systems spanning edge devices, cloud, and intermittent connectivity
- Able to debug production systems quickly and decisively
- Communicates clearly and operates independently.
- U.S. Person status required (may involve export-controlled data)
- Handled streaming systems (Kafka, pub/sub)
- Created production LLM or inference pipelines (prompting, retrieval, evaluation)
- Designed for adversarial or security environments
- Built systems that run on-device as well as in the cloud
- Thrived in an early-stage startup environment.
- Shape how the production AI system behaves in the real world
- Design ingestion → reasoning → decision systems
- Drive inference reliability, predictable logic, and transparent reasoning
- Close the loop from deployments → system learning
- Make the system trusted under real-world conditions
- Partner with RF / hardware / field teams to deliver for elite users globally (:10-15% travel)
- Short application
- 20-minute intro call
- Technical deep-dive
- Practical problem discussion
- References and offer
You'll join us on-site at our HQ in New York City with occasional domestic and global deployments.
Apply. Make history. Build humanity's defense against machines.
Create a free Caio profile to unlock more results and save your role and location preferences.
Unlock free search