Interested in this Research Engineer role at EO Solutions?
Apply Now →Skills & Technologies
About This Role
Job Title SDA Research Engineer Job ID 27779820 Work Remote Yes Location Remote Other Location Description Location: Remote (U.S.)
FLSA Status: Exempt
Job Type: Full-Time
Job Title: SDA Research Engineer
About EO Solutions:
EO Solutions is a pioneering physics and engineering company with operations in Nevada, Hawaii, and Alabama. We specialize in cutting-edge electro-optical technology and software solutions, with core expertise in AI/Autonomy for space domain awareness, laser sources, beam control, tracking/targeting, atmospheric modeling, and system architecture design, integration, and deployment. We are leaders in delivering Directed Energy (DE) capabilities and space environment monitoring, including satellite trajectory analysis and threat assessment. Our dedication to innovation positions us at the forefront of addressing multi-domain warfighting capability gaps.
Job Description:
EO Solutions, an aerospace defense contractor specializing in Space Domain Awareness (SDA), Directed Energy (DE), and advanced AI/ML technologies, is seeking an experienced SDA Research Engineer to join our growing team. This position will serve as the team’s subject-matter expert in astrodynamics and satellite tracking—developing the orbit determination, estimation, and tasking algorithms that drive our space surveillance sensor orchestration system and applying modeling and simulation to evaluate system performance and mission effectiveness. This role is positioned alongside the software development team, actively contributing to the codebase alongside developers to ensure research translates directly into reliable, operational capability.
Key Responsibilities
- + Serve as the team’s subject-matter expert in astrodynamics and satellite tracking, performing orbit determination, sensor tasking, and uncertainty characterization for resident space objects.
- + Develop, validate, and document estimation and filtering algorithms (e.g., Kalman filtering, batch least-squares) for orbit determination and multi-sensor data fusion.
- + Define tracking, tasking, and observation requirements that inform the space surveillance sensor orchestration system, and develop the algorithms that drive automated sensor tasking and scheduling.
- + Build and further develop modeling and simulation tools to evaluate system performance against key metrics.
- + Apply astrodynamics theory to prototype algorithms and reference implementations that the software engineering team integrates into production systems.
- + Participate in the software development cycle by attending stand-ups, managing code through Git, and actively developing technical portions of the codebase through bug fixes, performance analysis, and simulation validation.
- + Collaborate with aerospace SMEs and software engineers to verify algorithm performance against operational data and mission requirements.
Qualifications & Experience
- + Ph.D. in Aerospace/Astronautical Engineering (or a closely related field) preferred, with relevant professional experience; exceptional candidates holding an M.S. with substantial experience will also be considered.
- + Deep expertise in astrodynamics and orbital mechanics, including orbit determination, propagation, and uncertainty characterization.
- + Strong background in estimation and filtering techniques, such as Kalman filtering, batch least-squares, and multi-sensor data fusion.
- + Proficiency in Python for algorithm development, analysis, and prototyping.
- + Experience working with Git or other version tracking software for tracking issues and documenting software development progress
- + U.S. citizenship with the ability to obtain and maintain a U.S. Government security clearance.
Desired Experience
- + Experience with Space Domain Awareness (SDA) or space surveillance systems, sensor tasking, and catalog maintenance.
- + Modeling and simulation experience supporting sensor tasking and orchestration.
- + Familiarity with electro-optical and/or radar sensor phenomenology, observation processing, and tracking.
- + Experience working in a software development environment on production-level code
- + Publications or conference contributions in astrodynamics, estimation, or SDA.
- + Experience transitioning research algorithms into operational software in collaboration with software engineering teams.
Work Environment & Culture
EO Solutions is a rapidly growing startup defense contractor committed to innovation, collaboration, and mission impact. We value community, partnership, and supporting our employees across all locations—Las Vegas, Huntsville, Hawaii, and remote.
Compensation
Competitive salary based on experience + full benefits package (health, dental, vision, PTO, holidays, 401(k), and more). Final salary range determined by experience and location.
Why Join EO Solutions?
- Opportunity to contribute to cutting-edge national security and SDA work.
- Highly collaborative, people-centered culture.
- Growth-focused environment with room to build, refine, and lead processes.
- Meaningful mission supporting aerospace and defense innovation.
Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 9,079 AI roles we're tracking, Research Engineer positions make up 2% of the market. At EO Solutions, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $252,000 based on 457 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
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 AI Engineering Manager ($251,622). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
EO Solutions AI Hiring
EO Solutions has 1 open AI role right now. They're hiring across Research Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
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).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
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.
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).
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.