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About This Role
About CFD Research: Since its inception in 1987, CFD Research has delivered innovative technology solutions within the Aerospace & Defense, Biomedical & Life Sciences, Intelligence & Sensing, and Energy & Materials industries. CFD Research has earned multiple national awards for successful application and commercialization of innovative component/system technology prototypes, multi-physics simulation software, multi-disciplinary analyses, and expert support services. Based in Huntsville, Alabama where laboratory facilities and headquarters are located, CFD Research also has office and laboratory facilities in Dayton, Ohio, prototyping test and evaluation facilities in Hollywood, Alabama, and office facilities in Fort Walton Beach, Florida. CFD Research is an ISO9001:AS9100D registered company and is appraised at CMMI Level II for Services. CFD Research is a 100% ESOP (employee-owned company) recognized in Inc. Magazine's Inc5000 as a top growing company for four of the last five years. Learn more at www.cfd-research.com
CFD Research is looking for a Computer Vision Engineer who will develop, and optimize advanced computer vision and multi‑sensor fusion algorithms supporting GEOINT‑mission workflows. The candidate will participate in the development of deep learning models, real‑time tracking systems, and GPU‑accelerated pipelines deployed in operational environments. You will shape next‑generation tools that integrate imaging physics, ML-based behavior inference, and multi‑sensor fusion. CFD Research values first-principles reasoning and physically interpretable models, so experience developing hybrid systems that combine calibrated sensor models, deterministic algorithms, probabilistic inference, and learned components is particularly desirable. Candidates should be motivated by building systems that are scientifically explainable, empirically validated, and capable of operating robustly across both synthetic and real-world sensing environments.
- Develop and implement deep learning computer vision models, with a focus on sensor fusion and target tracking
- Collaborate with multidisciplinary teams to design, develop, test, and deploy technical solutions in Python or C++
- Utilize GPU programming, including CUDA or RAPIDs, to optimize the performance of computer vision applications
- Contribute to the architecture and implementation of novel single and multi-sensor detection and tracking and fusion of targets
Basic Qualifications:
- 1-5+ years of experience developing computer vision algorithms for detection, tracking, or multi‑sensor fusion in remote sensing or GEOINT‑relevant environments
- 1+ years of experience applying deep learning to computer vision problems using transformer‑based or self‑supervised architectures
- Candidate must possess an ACTIVE TS/SCI security clearance to be considered.
- Experience implementing model pipelines in Python or C++, including training, evaluation, and deployment workflows
- Knowledge of GPU‑accelerated development using CUDA, RAPIDS, or other GPU programming frameworks
- Knowledge of classical tracking or estimation methods (such as Kalman or extended filters) to support real‑time algorithm development
- Knowledge of Physics-Informed Neural Networks (PINNs), Physics-constrained loss functions
- Ability to design, test, and optimize algorithms for operational performance in constrained computing environments (i.e., multi‑GPU servers or cloud)
- Bachelor's degree in related STEM field
- U.S. Citizenship is REQUIRED.
Desired Qualifications:
- Experience with GPU‑accelerated deep learning, including CUDA kernel development, TensorRT optimization, RAPIDS, or distributed multi‑GPU training
- Experience developing synthetic data, kinematic target models, or scenario simulation tools to support algorithm training or evaluation
- Experience with advanced estimation, tracking, and fusion techniques (such as: joint multi‑sensor registration, Bayesian fusion, particle filters, deep multi‑object tracking pipelines, etc.)
- Knowledge of geospatial data formats, sensor phenomenology, or remote‑sensing exploitation workflows
- Experience with MLOps or scalable deployment systems (Docker, Kubernetes, ONNX Runtime, Triton Inference Server, or similar)
- Experience building or optimizing microservice architectures or distributed systems for real‑time data processing
- Experience integrating CV models into edge, embedded, or latency‑constrained operational environments
- Familiarity with transformer‑based vision architectures beyond DINO/CLIP/SAM (such as: ViT variants, self‑supervised multi‑modal encoders)
- Experience with cloud ML platforms (such as: AWS GovCloud, Azure ML, on‑prem GPU clusters)
- Master's degree or Ph.D. in Computer Science, Electrical/Computer Engineering, AI/ML, Physics, Mathematics or other related field
Location: This role is based in the Dayton, OHarea, and is 100% onsite.
Benefits: CFD Research offers competitive salaries and excellent employee benefits, including an employer matching 401(k) and Employee Stock Ownership Plan (ESOP). CFD Research offers our Full-Time, permanent employee-owners a highly competitive insurance package, including medical, vision, and dental insurance. Additionally, we offer company paid leave, compensation time, parental leave, long-term and short-term disability, accidental death and dismemberment, and life insurance. Part-Time and Temporary employee-owners may receive partial or reduced benefits and leave time based on their employment status level. Performance appraisals occur twice a year and annual pay increases are based upon corporate goals, personal development, performance, and outstanding achievements. In addition, group and individual bonuses are awarded for exceptional performance.
CFD Research is an EO employer - Veterans/Disabled and other protected categories
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At CFD Research Corporation, 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 $212,000 based on 11,857 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 $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
CFD Research Corporation AI Hiring
CFD Research Corporation has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Beavercreek, OH, Beavercreek, OH, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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