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About This Role
Job ID
329329
Job Title: AI/ML \& Networking Research Engineer
Job Category: Engineering
Time Type: Full time
Minimum Clearance Required to Start: None
Employee Type: Regular
Percentage of Travel Required: Up to 10%
Type of Travel: Local
\* \* \*### CACI has an exciting new opportunity in Florham Park, NJ for an Expert AI/ML Research Engineer. Apply machine learning, statistics, and best\-of\-breed AI capabilities to develop algorithms to solve challenging problems in networking, edge computing, cybersecurity, and autonomy.
### Research and develop innovative capabilities that incorporate machine learning and other advanced concepts in areas such as federated learning, network analysis, resource allocation \& planning, and robust communications in challenging networking environments. Develop machine learning and statistics\-enabled software capabilities and applications to be integrated into larger hardware and software systems. Provide subsystems or component level technical leadership and develop written materials and presentations for customers. Assist in developing new business concepts and successfully develop winning proposals to secure additional funding and work.
### Responsibilities:
- ### Develop and justify the performance of algorithms in networking and edge computing\-related domains.
- ### Perform extensive detailed modeling and simulation, including quantifying the relative impact of candidate solution architectures.
- ### Oversee the development of standalone applications and/or major software subsystems and components while adhering to software best practices.
- ### Assist with the development detailed technical plans with tasks, schedule, and labor estimates.
- ### Develop automated tests to rigorously test and evaluate algorithms and software capabilities.
- ### Act as a core contributor during proposal development and project execution.
### Qualifications:
*Required:*
- ### Bachelor's degree in computer science, computer engineering, or machine learning and at least 10 years’ experience.
- ### Experience developing algorithms, protocols, and software applications in networking, edge computing, and/or autonomy.
- ### Strong background in machine learning and statistics.
- ### Significant experience with Python and C\+\+.
- ### Comfortable using Linux operating systems and commonly used Linux utilities.
- ### Some advanced knowledge of computer networking, including TCP/IP networking/OSI model, routing algorithms, Linux networking utilities and interfaces.
- ### Ability to anticipate strengths and weaknesses of software solutions and perform simulation, testing, and evaluation to quantitatively compare alternative approaches.
- ### Ability to effectively articulate research and development outcomes to senior staff and customers and act as a technical representative of the team.
- ### Excellent oral and written communications skills.
- ### Demonstrated effectiveness in leading, organizing, and executing technical efforts.
- ### Must be a US Citizen with the ability to obtain, maintain and/or transfer the required security clearance as dictated by the contract
- ### Must be a US Citizen
### *Desired:*
- ### Master's degree or Ph.D. in computer science, computer engineering, or machine learning.
- ### Experience applying machine learning to signal processing and/or other time\-series data analysis applications.
- ### Experience with probabilistic generative modeling and Bayesian inference.
- ### Knowledge of or experience with information theory, probability theory, parametric and non\-parametric statistical tests.
- ### Familiarity with concepts and techniques associated with adversarial AI and AI/ML assurance.
- ### Top Secret with SCI preferred.
Insert 3\-5 bullet points here
*
What You Can Expect:
A culture of integrity.
At CACI, we place character and innovation at the center of everything we do. As a valued team member, you’ll be part of a high\-performing group dedicated to our customer’s missions and driven by a higher purpose – to ensure the safety of our nation.
An environment of trust.
CACI values the unique contributions that every employee brings to our company and our customers \- every day. You’ll have the autonomy to take the time you need through a unique flexible time off benefit and have access to robust learning resources to make your ambitions a reality.
A focus on continuous growth.
Together, we will advance our nation's most critical missions, build on our lengthy track record of business success, and find opportunities to break new ground — in your career and in our legacy.
Pay Range:
There are a host of factors that can influence final salary including, but not limited to, geographic location, Federal Government contract labor categories and contract wage rates, relevant prior work experience, specific skills and competencies, education, and certifications. Our employees value the flexibility at CACI that allows them to balance quality work and their personal lives. We offer competitive compensation, benefits and learning and development opportunities. Our broad and competitive mix of benefits options is designed to support and protect employees and their families. At CACI, you will receive comprehensive benefits such as; healthcare, wellness, financial, retirement, family support, continuing education, and time off benefits.
The proposed salary range for this position is:
$131,800 \- $290,000*CACI is* *an Equal Opportunity Employer.* *All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, age, national origin, disability, status as a protected veteran, or any* *other protected characteristic.*
Salary Context
This $131K-$290K range is above the median for Research Engineer roles in our dataset (median: $188K across 44 roles with salary data).
View full Research Engineer salary data →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 3,708 AI roles we're tracking, Research Engineer positions make up 2% of the market. At CACI International, 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 $280,000 based on 147 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($210K) sits 25% below the category median. Disclosed range: $131K to $290K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and AI Architect ($254,798). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
CACI International AI Hiring
CACI International has 6 open AI roles right now. They're hiring across Data Scientist, Research Engineer, AI/ML Engineer, Prompt Engineer. Positions span Camp Smith, HI, US, Florham Park, NJ, US, Remote, US. Compensation range: $133K - $290K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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