ML Engineer

Job Description

Job Title: ML Engineer

Location: Colorado Springs, CO or DC or Remote

Security Clearance: Uncleared (Secret Preferred) or must be willing to obtain

We question. We listen. We adapt.

Be honest. Be pragmatic.

Omni Federal, founded in 2017 and headquartered in Washington, DC, is a highly specialized software solutions provider with a robust presence in key locations across the United States, including Boston, MA, Colorado Springs, CO, San Antonio, TX, and St. Louis, MO. Born out of the Department of Defense's software factory ecosystem, Omni has rapidly distinguished itself by delivering both mission-critical and enterprise solutions that enhance the technological capabilities of the federal government. With a focus on areas such as Command and Control, Cybersecurity, Space, Geospatial, and Modeling & Simulation, Omni leverages cutting-edge commercial technology tailored to government objectives, improving mission performance and delivering transformative outcomes for the Department of Defense (DoD), Intelligence Community (IC), and their end-users. The company's innovative approach is backed by its Omni Labs and SBIR Innovation centers, where they develop advanced platforms and tools in data mesh, secure connectivity, and intelligent automation

Why Omni?

• Environment of Autonomy
• Innovative Commercial Approach
• People over process

We are seeking a strong Machine Learning Engineer to support the software and ML implementation of a groundbreaking Space Domain Awareness (SDA) initiative.Partnering closely with the Virginia Tech National Security Institute (VTNSI) and building upon successful organic research, you will help field a GEO-capable passive RF "voiceprint" combat ID capability. The research team has already demonstrated a functional, on-sky Unique Emitter ID (UEID) method using raw I/Q data to perform combat ID on Resident Space Objects (RSOs). Your mission is to bring this technology out of the lab and into active operations. You will be the sole Omni individual driving the implementation of this demonstrator, working alongside VTNSI researchers to deliver a fully autonomous, taskable capability to the Joint Commercial Office. Candidates must be passionate, energized, and excited to work on modern architectures and solve challenging problems for our clients

What You Will Do:

• Algorithm Implementation: Transition existing organic UEID research into a robust, production-ready machine learning pipeline capable of analyzing raw passive RF I/Q datato extract RSO "voiceprints."
• Data Integration: Engineer the connections required to ingest live sensor data from theVTNSI data source and route it effectively through the processing pipeline.
• UDL Tech Transfer: Develop and standardize the output for the new Unified Data Library (UDL) Passive RF I/Q endpoint, ensuring the techniques developed can be seamlessly transferred to any other willing passive RF provider.
• Autonomous Tasking: Integrate the completed passive RF combat ID capability into the IL2 MACHINA environment, enabling autonomous tasking and scheduling.
• JCO Delivery: Ensure the final demonstrator and risk-reduction sensor meet the strict operational requirements for delivery to the Joint Commercial Operations (JCO) cell.

The Hard Part (Why You'll Love This Role)

Finding talent for this specific intersection of skills is notoriously difficult. This isn't a standard computer vision or NLP machine learning role; it requires a deep understanding of RF signal processing and the ability to train models on raw, complex I/Q data rather than clean, pre-processed datasets. You will be operating at the bleeding edge of GEO passive RF tracking. If you are the kind of engineer who thrives on solving niche, highly technical problems that most ML practitioners have never even encountered, you will have the autonomy and runway to make a massive impact here.

Required Qualifications:

• Experience: 4+ years of experience as a Machine Learning Engineer, Data Scientist, or closely related role.
• Technical Skills: Deep expertise in Python and modern ML frameworks (PyTorch, TensorFlow).
• RF / Signal Processing: Demonstrated experience working with Radio Frequency (RF) systems, specifically handling, parsing, and applying machine learning techniques to raw I/Q data.
• Software Engineering: Strong capability in building production-ready pipelines, API integrations, and deploying models into operational environments.

Preferred Qualifications:

• Active DoD Security Clearance.
• Previous experience collaborating with academic or research institutions (e.g., VTNSI).
• Familiarity with Space Domain Awareness (SDA), satellite communications, or GEO RSO tracking.
• Experience working with the Unified Data Library (UDL), MACHINA, or delivering capabilities to the JCO.
• Background in automated scheduling or autonomous sensor tasking.

Our team is always hungry for more - more knowledge, more problem solving, more growth, more innovation, and, ultimately, customer success. This hunger and hustle fuel our determination to excel in everything we do. It's not just about meeting established goals; it's about exceeding them. We take immense pride in applying our expertise in cutting-edge technologies and our ability to adapt to emerging trends directly to the users in the field, as our nation's protectors deserve nothing less.

Omni Federal is an equal opportunity and affirmative action employer. Omni Federal is committed to administering all employment and personnel actions on the basis of merit and free of discrimination based on race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or status as an individual with a disability. Consistent with this commitment, we are dedicated to the employment and advancement of qualified minorities, women, individuals with disabilities, protected veterans, persons of all ethnic backgrounds and religions according to their abilities.

Proposed Pay Scale: $180,000 - $250,000