Machine Learning Engineer

TL;DR – We’re building humanity’s defense layer for the AI age and are looking for an exceptional machine learning engineer to build the AI decision system that turns raw signals into trusted operational decisions across device, cloud, and offline environments.

If you would have joined early Tesla to make Autopilot work in the real world and improve across the fleet or Figure AI before humanoids left the lab—to build the firmware that made them trustworthy, this is that role.

Why Sweep?

As intelligent machines proliferate into every part of the physical world, we humans still lack a defense layer to ensure the systems and devices we rely on remain aligned with us.

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.
  

What we’re looking for...

• 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).
  

Bonus if you’ve...

• 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..
  

What you’ll do...

• 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).
  

How we select...

• Short application
• 20-minute intro call
• Technical deep-dive
• Practical problem discussion
• References and offer
  

Final facts.

Base salary up to $240,000 depending on qualifications, experience, and impact. Total compensation includes equity, premium insurance, 401(k), flexible PTO, and other individual benefits.