AI/ML Engineer
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." So we should have About the role heading, not About CFD Research. The "About CFD Research" paragraph is company overview; maybe we can include that under About the role? But the instruction says About the role section heading; we can put company overview there? Usually About the role includes description of role and maybe company. We'll include the description: "CFD Research is seeking an AI/ML Engineer to build and deploy advanced artificial intelligence and machine learning solutions that modernize mission systems and accelerate decision-making across the national security ecosystem. The candidate will contribute end-to-end—spanning data processing, model development, MLOps, and integration of scalable AI capabilities into operational environments. The work will leverage experience with ML-based NLP, LLMs and generative AI, computer vision, and modern software engineering practices. We value first-principles reasoning and physically interpretable models, so an interest in or experience developing hybrid systems is highly desirable. Candidates should be motivated by building systems that are scientifically explainable, empirically validated, and capable of operating robustly across sensing environments. You will partner with cross-functional engineering, mission SMEs, and product teams to deliver production-grade systems."
That seems appropriate for About the role.
Responsibilities: list of duties from "Develop and implement AI/ML models—including NLP, LLM-based pipelines, deep learning architectures, and computer vision models—aligned to mission requirements." etc. We'll extract each bullet.
Responsibilities list:
- Develop and implement AI/ML models—including NLP, LLM-based pipelines, deep learning architectures, and computer vision models—aligned to mission requirements.
- Build and maintain scalable data pipelines to support training, evaluation, and continuous improvement of models.
- Develop components of end-to-end production systems including microservices, APIs, automation frameworks, and cloud-native AI services.
- Integrate AI/ML capabilities into operational environments with attention to security, reliability, observability, and performance.
- Conduct model optimization, tuning, versioning, and A/B testing to ensure robust mission impact.
- Apply modern software engineering patterns (Python, Java, Scala, Rust; microservices; distributed systems).
- Collaborate with multi-disciplinary teams to identify mission needs and translate requirements into technical solutions.
- Prepare technical documentation, contribute to design reviews, and support accreditation processes in classified environments.
Requirements: Basic Qualifications list.
- 2–6 years of experience developing machine learning models (such as: NLP/LLM, CV, deep learning, generative AI, or related).
- 2–6 years of experience developing AI/ML capabilities to operate in a microservice stack.
- Candidate must possess an ACTIVE TS/SCI security clearance to be considered.
- Experience working with APIs, microservices, or distributed systems.
- Experience with data processing frameworks and scientific computing libraries (such as: NumPy, Pandas, PyTorch, TensorFlow, or scikit-learn).
- Experience configuring or deploying software systems in operational environments.
- Experience with CI/CD tooling, source control (such as GitLab), and containerization technologies (such as: Docker or Kubernetes).
- TS/SCI clearance, and willingness to take a polygraph. (duplicate of earlier? Actually appears again; we should deduplicate.)
- Bachelor's degree in a related STEM field.
- U.S. Citizenship is REQUIRED.
We need to avoid duplicate lines. We'll combine TS/SCI clearance and willingness to take polygraph as one bullet.
Qualifications: Desired Qualifications list.
- Exposure to GPU programming (CUDA or RAPIDs) or AWS containerization.
- Understanding of LLM orchestration, RAG pipelines, or hallucination mitigation workflows.
- Familiarity with MLOps platforms or hybrid cloud environments (AWS, Azure, GCP).
- Familiarity with automated testing tools and model evaluation frameworks.
- Master’s degree in Computer Science, AI/ML Engineering, Data Science, or related STEM field.
Skills: maybe we can extract technical skills from responsibilities and qualifications? But we might not need a separate Skills section if content overlaps. The instruction: "Prefer a few substantive sections over many thin ones." We could combine Skills into Requirements or Qualifications. But we need to follow headings list; we can include Skills if we have specific content. We could list skills like: Python, Java, Scala, Rust, NLP, LLM, generative AI, computer vision, deep learning, microservices, APIs, CI/CD, Docker, Kubernetes, GitLab, NumPy, Pandas, PyTorch, TensorFlow, scikit-learn, GPU programming (CUDA/RAPIDs), AWS, Azure, GCP, MLOps, automated testing, model evaluation. That's derived from posting. Could be a Skills section.
But we must not invent or summarize away detail; we can list them as they appear. However, the posting does not have a explicit "Skills" heading; but we can create a Skills section if we have specific content. The instruction: "include a section ONLY when the posting has real, specific content for it — never emit a heading followed by filler/placeholder text". So we can create Skills if we have specific content (like list of technologies). That seems okay.
Benefits: we have explicit Benefits paragraph.
Pay: none.
Schedule: location and onsite; but schedule not specified. Could we treat location as Schedule? Not really. The instruction says only create Schedule if posting actually states them. It does not state schedule (like full-time, part-time, shift). It says "This role is based in the Dayton, OH area, and is 100% onsite." That's location and work arrangement, not schedule. So we should not create Schedule.
Thus sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits.
Now produce HTML fragment with headings and - for lists,
for prose.
We need to ensure we don't include any boilerplate like EEO line. Also remove duplicated lines like TS/SCI clearance repeated.
Let's craft.
About the role: paragraph(s). Could be two paragraphs but we can combine.
Responsibilities: list.
Requirements: list.
Qualifications: list.
Skills: list.
Benefits: paragraph(s) maybe multiple paragraphs but we can combine.
Make sure to not include navigation like "Learn more at www.cfd-research.com". Remove.
Also remove "CFD Research is an ISO9001:AS9100D registered company..." etc? That's company overview; we might have omitted that from About the role. Should we include company overview? The About the role section could include company overview as part of role description? The instruction says About the role heading; we could include both company overview and role description. But we already have role description; we could add company overview as part of About the role. However, the instruction says "Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short
or drop it." The company overview is not minor metadata; it's substantive. Should we include it? The posting starts with "About CFD Research: Since its inception..." That's company overview. It's substantive. We could include it in About the role section as a paragraph before role description. That seems appropriate.
Thus About the role will have two paragraphs: company overview and role description.
Now Benefits: we have paragraph about competitive salaries, excellent employee benefits, including employer matching 401(k) and ESOP, insurance package, company paid leave, etc. Also mention performance appraisals twice a year, annual pay increases, group and individual bonuses. Also note part-time/temporary may receive partial benefits. We'll keep all.
Now ensure we don't include "CFD Research is an EO employer - Veterans/Disabled and other protected categories". Remove.
Now produce HTML.
Let's draft:
About the role
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. The company 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 with laboratory facilities and headquarters there, 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.
CFD Research is seeking an AI/ML Engineer to build and deploy advanced artificial intelligence and machine learning solutions that modernize mission systems and accelerate decision-making across the national security ecosystem. The candidate will contribute end-to-end—spanning data processing, model development, MLOps, and integration of scalable AI capabilities into operational environments. The work will leverage experience with ML-based NLP, LLMs and generative AI, computer vision, and modern software engineering practices. We value first-principles reasoning and physically interpretable models, so an interest in or experience developing hybrid systems is highly desirable. Candidates should be motivated by building systems that are scientifically explainable, empirically validated, and capable of operating robustly across sensing environments. You will partner with cross-functional engineering, mission SMEs, and product teams to deliver production-grade systems.
Responsibilities
- Develop and implement AI/ML models—including NLP, LLM-based pipelines, deep learning architectures, and computer vision models—aligned to mission requirements.
- Build and maintain scalable data pipelines to support training, evaluation, and continuous improvement of models.
- Develop components of end-to-end production systems including microservices, APIs, automation frameworks, and cloud-native AI services.
- Integrate AI/ML capabilities into operational environments with attention to security, reliability, observability, and performance.
- Conduct model optimization, tuning
- for lists,
for prose. We need to ensure we don't include any boilerplate like EEO line. Also remove duplicated lines like TS/SCI clearance repeated. Let's craft. About the role: paragraph(s). Could be two paragraphs but we can combine. Responsibilities: list. Requirements: list. Qualifications: list. Skills: list. Benefits: paragraph(s) maybe multiple paragraphs but we can combine. Make sure to not include navigation like "Learn more at www.cfd-research.com". Remove. Also remove "CFD Research is an ISO9001:AS9100D registered company..." etc? That's company overview; we might have omitted that from About the role. Should we include company overview? The About the role section could include company overview as part of role description? The instruction says About the role heading; we could include both company overview and role description. But we already have role description; we could add company overview as part of About the role. However, the instruction says "Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short
or drop it." The company overview is not minor metadata; it's substantive. Should we include it? The posting starts with "About CFD Research: Since its inception..." That's company overview. It's substantive. We could include it in About the role section as a paragraph before role description. That seems appropriate. Thus About the role will have two paragraphs: company overview and role description. Now Benefits: we have paragraph about competitive salaries, excellent employee benefits, including employer matching 401(k) and ESOP, insurance package, company paid leave, etc. Also mention performance appraisals twice a year, annual pay increases, group and individual bonuses. Also note part-time/temporary may receive partial benefits. We'll keep all. Now ensure we don't include "CFD Research is an EO employer - Veterans/Disabled and other protected categories". Remove. Now produce HTML. Let's draft:
About the role
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. The company 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 with laboratory facilities and headquarters there, 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.
CFD Research is seeking an AI/ML Engineer to build and deploy advanced artificial intelligence and machine learning solutions that modernize mission systems and accelerate decision-making across the national security ecosystem. The candidate will contribute end-to-end—spanning data processing, model development, MLOps, and integration of scalable AI capabilities into operational environments. The work will leverage experience with ML-based NLP, LLMs and generative AI, computer vision, and modern software engineering practices. We value first-principles reasoning and physically interpretable models, so an interest in or experience developing hybrid systems is highly desirable. Candidates should be motivated by building systems that are scientifically explainable, empirically validated, and capable of operating robustly across sensing environments. You will partner with cross-functional engineering, mission SMEs, and product teams to deliver production-grade systems.
Responsibilities
- Develop and implement AI/ML models—including NLP, LLM-based pipelines, deep learning architectures, and computer vision models—aligned to mission requirements.
- Build and maintain scalable data pipelines to support training, evaluation, and continuous improvement of models.
- Develop components of end-to-end production systems including microservices, APIs, automation frameworks, and cloud-native AI services.
- Integrate AI/ML capabilities into operational environments with attention to security, reliability, observability, and performance.
- Conduct model optimization, tuning