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About This Role
Scientific Applications & Research Associates, Inc. (SARA) is a world-class Defense Research and Development enterprise with more than 35 years of proven innovation. We create new and emerging technology solutions in support of Commercial & Government organizations, with expertise in RF, EMP, Pulsed Power, Acoustics, and Threat Warning Systems. As an Employee-Owned company, every team member has a personal stake in the success of our mission, fostering a culture of collaboration, accountability, and innovation. At SARA, you’ll have the opportunity to work alongside talented engineers, physicists, and technicians while making a direct impact on programs that matter.
The SARA High Power Electromagnetics Division (HPEM) is looking for a remarkably talented, motivated, and imaginative Machine Learning Systems Engineer (UAS) to support research and development activities and transition innovative technologies to the DoD, DoE, and their contractors in Colorado Springs, CO. The candidate will have the opportunity to grow and learn with talented engineers, physicists, and technicians on both long-standing programs and new initiatives, contributing in a multidisciplinary environment from concept through operation.
We are seeking a highly skilled engineer to spearhead the development of unmanned navigation and control technologies, with emphasis on flight controller integration, autonomous navigation, and AI-driven edge processing. This role involves developing and configuring flight control systems, integrating sensors, optimizing embedded software, and deploying AI models on embedded platforms. The ideal candidate will collaborate across hardware, software, and AI teams to advance the boundaries of autonomous aerial systems.
Core Responsibilities:
- Assist with configuring and integrating flight controllers running PX4 or ArduPilot, including Cube-based hardware, with various UAV platforms
- Support firmware flashing, flight controller parameter configuration, PID tuning, motor mapping, ESC calibration, and sensor integration and calibration under the guidance of experienced engineers
- Develop, modify, and test software using C++ and/or Python to support flight control, sensor processing, and communication with Jetson-based AI/vision systems
- Assist with integrating ROS and MAVLink interfaces for communication between flight controllers, companion computers, and ground control systems
- Support computer vision and AI-based perception activities, including object tracking and simultaneous localization and mapping (SLAM), using tools such as OpenCV, TensorFlow, PyTorch, and DeepStream
- Assist with deploying and evaluating AI models on NVIDIA Jetson platforms, learning techniques for efficient real-time inference
- Support networking, telemetry, and video streaming integration for UAV command, control, and monitoring
- Participate in laboratory, simulation, and field testing, including test setup, data collection, troubleshooting, and documentation, following established safety procedures
- Collaborate with hardware, software, and AI team members to resolve integration issues and document technical results
Requirements:
- Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Aerospace Engineering, or a related field
- 0–2 years of relevant experience in UAS, robotics, embedded software, controls, or related engineering activities; relevant internships, academic projects, research, and personal projects will be considered
- Foundational programming skills in C++ and/or Python
- Basic understanding of control systems, sensors, and embedded computing through coursework or practical experience
- Familiarity with one or more relevant technologies, such as PX4, ArduPilot, MAVLink, ROS, or UAV flight controllers
- Ability to troubleshoot technical problems, follow test procedures, and clearly document results
- Interest in autonomous navigation, computer vision, and AI processing on embedded platforms
- Ability to work effectively as a member of a multidisciplinary team, communicate technical information, and learn new tools and technologies
- U.S. citizen with the ability to obtain and maintain a DoD security clearance
- May require moderate physical effort, including standing, sitting, use of hands while performing duties, and carrying or lifting up to 50 pounds
- Working environment is indoor, moderately quiet, reasonably clean, well-lighted and well-ventilated; moderate physical hazards may exist
Preferred Qualifications:
- 2+ years of relevant hands-on experience with flight controller integration, UAV software or hardware development, robotics, or embedded systems
- Master’s degree in Computer Science, Robotics, Electrical Engineering, Aerospace Engineering, or a related field
- Hands-on experience through internships, capstone projects, research, robotics competitions, or personal UAV projects
- Familiarity with Linux, particularly Ubuntu
- Experience configuring PX4 or ArduPilot, using MAVLink or ROS, or working with QGroundControl or Mission Planner
- Exposure to PID tuning, sensor calibration, autonomous navigation, or navigation in GPS-denied environments
- Familiarity with NVIDIA Jetson platforms, OpenCV, TensorFlow, PyTorch, DeepStream, and/or CUDA
- Exposure to real-time embedded systems, wireless communications, telemetry, or video streaming
- Familiarity with UAV test and validation procedures and hardware debugging tools such as oscilloscopes, logic analyzers, or protocol analyzers
- Active DoD security clearance, preferably Top Secret SARA offers a competitive benefits package to include:
- Medical, dental, vision, disability and life insurance; and other supplemental programs
- A generous PTO program (vacation, sick, holiday)
- Tuition & certification reimbursement
- Flexible work schedules
- Discretionary Employee bonuses
- 401(k) match/profit sharing
- Employee Stock Ownership Plan (ESOP)
Pay Range
Salary at SARA is determined by various factors, including but not limited to location, the individual’s particular combination of education, knowledge, skills, competencies, and experience, as well as contract-specific affordability and organizational requirements. The projected compensation range for this position is $80,000 to $110,000 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of SARA’s total compensation package for employees. Positions are on-site, unless indicated otherwise.
Application Deadline: November 10, 2026
SARA is an equal employment opportunity employer.
Salary Context
This $80K-$110K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At SARA Inc, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($95K) sits 55% below the category median. Disclosed range: $80K to $110K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
SARA Inc AI Hiring
SARA Inc has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Research Engineer. Based in Colorado Springs, CO, US. Compensation range: $110K - $180K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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