Software Engineer, Machine Learning (Systems)

Sweep360•New York, NY

12h•$240,000•Onsite

About The Position

TL;DR — We’re building humanity’s defense layer for the AI age and are looking for an exceptional ML engineer to stabilize the system that turns raw signal into 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 — 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 ML systems hire. You’re the difference between a system that exists and one that works. Make the system reliable under pressure — data, pipelines, and decision logic. Take outputs from sensing systems and turn them into consistent, trusted decisions. Define how inference works when inputs are incomplete, noisy, or conflicting. Your work is used in high-stakes environments where outputs must be trusted. Gain pre-Series A ownership as one of the first 10 engineers.

Requirements

5–10 years building and operating production systems
Strong system design across APIs, pipelines, and data storage
Deployed ML / LLM systems in production and improved them via feedback loops
Strong Python, plus Go/TypeScript (or similar)
Comfortable working across device and cloud environments.
Able to debug production systems quickly and decisively.
Communicates clearly and operates independently.
U.S. Person status required (may involve export-controlled data).

Nice To Haves

Built RF / BLE classification systems and models from zero.
Handled streaming systems (Kafka, pub/sub).
Created LLM pipelines (prompting, retrieval, evaluation).
Designed for adversarial or security environments.
Built systems that run on-device as well as in the cloud.
Thrived in early-stage startup environment.

Responsibilities

Own system behavior and data pipelines.
Design ingestion → reasoning → decision systems.
Improve the decision layer for consistency and reliability.
Close the loop from deployments → system learning.
Ensure system reliability across device, cloud, and partial connectivity.
Partner with RF / hardware / field teams to deliver for elite users globally (~10–15% travel).