Senior ML Engineer - AI-First Cybersecurity

United States Digital Space LLC; United States; Remote; USD 140,000 - 215,000; Full time

Benefits offered by this job

Equity awards

Wellness program

Paid time off

Parental leave

Professional development

Job summary

United States Digital Space LLC is seeking a Sr. Machine Learning Engineer to join the AIDR Engineering Team. You will support high-throughput, low-latency LLM inferencing, data engineering aligned to rigorous evaluations, and work closely with data scientists.

This role focuses on enterprise workflows to interact safely with AI applications. You will mentor others, contribute to scalable architectures, and own end-to-end development from design to deployment in a collaborative, agile

Qualifications

• Prior work experience doing data engineering and architecture in support of advanced data science use cases.
• A deep understanding of LLM post-training methods and computational architectures.
• Understanding scalability and distributed systems (sharding, partitioning, concurrency).
• Be a team player and follow engineering best practices, testing paradigms and CI.

Responsibilities

• Innovate with state-of-the-art ML tech to accelerate data science.
• Provide pragmatic support for fast-moving research and development teams.
• Implement high-quality solutions for customer-facing applications at scale.
• Own end-to-end development: design, implement, test, deploy and monitor.

Skills

Data engineering

LLM post-training

Distributed systems

CI/CD

Unit testing

Tools

Docker

Kubernetes

AWS

GCP

Kafka

Cassandra

Spark

Elasticsearch

Terraform

Ansible

Job description

United States Digital Space LLC is seeking a Sr. Machine Learning Engineer to join the AIDR Engineering Team. You will support high-throughput, low-latency LLM inferencing, data engineering aligned to rigorous evaluations, and work closely with data scientists.

This role focuses on enterprise workflows to interact safely with AI applications. You will mentor others, contribute to scalable architectures, and own end-to-end development from design to deployment in a collaborative, agile