Senior ML Engineer: Edge Defense Vision, Austin, Equity

9 Mothers; Austin (TX); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Meaningful Early Equity

Direct Roadmap Influence

Mission‑Critical Work

The Builder's Playground

100% Employer‑Paid Premiums

Unlimited PTO

Zero Red Tape

Austin‑Based Culture

Relocation Assistance

Job summary

9 Mothers is seeking a Senior ML Engineer to design, train, and maintain models powering our counter‑sUAS perception stack. This individual contributor role focuses on building from scratch under SWaP constraints, without reliance on RAG, LLMs, or pre‑built cloud APIs.

You will own data pipelines, training infrastructure, and evaluation methods, contributing to field‑level performance and real‑world detection capabilities on an Austin‑based team.

Qualifications

• Shipping ML systems into production under real‑world operational requirements.
• Fluency in PyTorch or JAX with full training loop development.
• Strong software engineering skills and ownership of training infrastructure.
• Proficiency in Python; working knowledge of C++ or Rust at the training‑to‑deployment boundary.
• U.S. citizenship and ability to pass a background check.

Responsibilities

• Design, train, and iterate ML models for detection, classification, and tracking of aerial targets.
• Own the dataset pipeline end‑to‑end including labeling, augmentation, and retraining.
• Build and maintain training infrastructure, including experiment tracking and orchestration.
• Define metrics and evaluation methodologies aligned with operational performance.
• Support deployment of models into the production perception stack and address gaps.
• Analyze field data to identify and address model failure modes.

Skills

PyTorch

JAX

Python

C++ or Rust

Job description

9 Mothers is seeking a Senior ML Engineer to design, train, and maintain models powering our counter‑sUAS perception stack. This individual contributor role focuses on building from scratch under SWaP constraints, without reliance on RAG, LLMs, or pre‑built cloud APIs.

You will own data pipelines, training infrastructure, and evaluation methods, contributing to field‑level performance and real‑world detection capabilities on an Austin‑based team.