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Machine Learning Engineer Ts Sci Jobs in Reston, VA

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Machine Learning Engineer

Reston, VA

TS/SCI w/ Full-Scope Poly Salary: Competitive We are seeking a highly skilled and motivated Machine Learning Engineer to join our dynamic team. The ideal candidate will have a strong background in ...

Kitware

Machine Learning Engineer

Arlington, VA · Hybrid

$110K - $160K/yr

Machine learning experience using visual data * Understanding of a variety of machine learning ... If not already cleared TS/SCI, willingness and ability to apply for and maintain a TS/SCI security ...

Kitware

Machine Learning Engineer

Arlington, VA · Hybrid

Machine learning experience using visual data * Understanding of a variety of machine learning ... If not already cleared TS/SCI, willingness and ability to apply for and maintain a TS/SCI security ...

Kitware

Machine Learning Engineer

Arlington, VA · On-site

$110K - $160K/yr

Machine learning experience using visual data * Understanding of a variety of machine learning ... If not already cleared TS/SCI, willingness and ability to apply for and maintain a TS/SCI security ...

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How much do machine learning engineer ts sci jobs pay per year?

As of Jun 7, 2026, the average yearly pay for machine learning engineer ts sci in Reston, VA is $133,966.00, according to ZipRecruiter salary data. Most workers in this role earn between $105,600.00 and $161,300.00 per year, depending on experience, location, and employer.

What are Machine Learning Engineer TS/SCI positions?

Machine Learning Engineer TS/SCI positions are specialized roles where engineers design, develop, and implement machine learning models and systems, often for government or defense projects that require a Top Secret/Sensitive Compartmented Information (TS/SCI) security clearance. These professionals work on advanced AI algorithms, data processing, and secure software, ensuring that sensitive information is protected throughout the process. They collaborate with data scientists, software developers, and security experts to solve complex problems using data-driven approaches while adhering to strict security protocols.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer with TS/SCI clearance, and why are they important?

To thrive as a Machine Learning Engineer with TS/SCI clearance, you need strong skills in machine learning algorithms, programming (Python, R), data analysis, and a relevant degree in computer science or a related field, along with active TS/SCI security clearance. Familiarity with frameworks like TensorFlow or PyTorch, experience with cloud platforms (AWS, Azure), and knowledge of secure data handling are commonly required. Excellent problem-solving, teamwork, and clear communication are vital soft skills for collaborating on complex, sensitive projects. These skills ensure effective development of secure, high-impact AI solutions in environments where data protection and analytical precision are critical.

What are some common challenges faced by Machine Learning Engineers with TS/SCI clearance in day-to-day work?

Machine Learning Engineers with TS/SCI clearance often encounter unique challenges, such as working with highly sensitive data in secure environments, which can limit access to certain tools or cloud resources. Collaboration is often restricted to cleared team members, and sharing findings externally is not permitted. Additionally, projects may have ambiguous requirements due to their classified nature, requiring strong problem-solving skills and adaptability. However, these roles offer the chance to work on impactful projects with significant national security implications, providing both technical and professional growth.

What is the difference between Machine Learning Engineer Ts Sci vs Data Scientist?

AspectMachine Learning Engineer Ts SciData Scientist
Required CredentialsBachelor's or Master's in CS, Data Science, or related fields; certifications in ML or AIBachelor's or Master's in Statistics, Data Science, or related fields; certifications in data analysis or visualization
Work EnvironmentDevelops and deploys ML models, often in production environmentsAnalyzes data, builds models, and provides insights for decision-making
Employer & Industry UsageTech companies, AI firms, R&D departmentsFinance, healthcare, marketing, and tech sectors

While both roles require strong analytical skills and knowledge of machine learning, Machine Learning Engineer Ts Sci focuses on developing and deploying scalable ML models, whereas Data Scientists primarily analyze data to generate insights. The roles often overlap but differ in their core responsibilities and work environments.

What are popular job titles related to Machine Learning Engineer Ts Sci jobs in Reston, VA? For Machine Learning Engineer Ts Sci jobs in Reston, VA, the most frequently searched job titles are:
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What cities near Reston, VA are hiring for Machine Learning Engineer Ts Sci jobs? Cities near Reston, VA with the most Machine Learning Engineer Ts Sci job openings:
Machine Learning Engineer Ts Sci jobs near you
Machine Learning Engineer - Geospatial (TS/SCI)

Machine Learning Engineer - Geospatial (TS/SCI)

LaunchCode

Springfield, VA • On-site

$175K - $250K/yr

Full-time

Posted 5 days ago

Job description

Description
Title: AI/Machine Learning Engineer - Vision Language Models / Multimodal AI (NGA)
Location: Springfield or Herndon, VA (onsite)
Clearance: TS/SCI (CI Poly preferred)
Position Type: Full-Time, Direct Hire
Pay: $175,000 to $250,000 for an SME
Company: The name of our partner organization will be disclosed during the interview process. This is not a direct role with LaunchCode; it is a position through LaunchCode, working with one of our partner companies.
Disclaimer: We are unable to provide work sponsorship for this role
Overview:
We're hiring a AI/Machine Learning Engineer with strong experience in multimodal AI and large-scale model training to support advanced vision-language initiatives in a secure government environment. This role will focus on fine-tuning Vision Language Models (VLMs) on domain-specific geospatial imagery, building scalable AWS training infrastructure, and developing evaluation frameworks for image understanding and spatial reasoning. Ideal candidates will have deep experience with PyTorch, HuggingFace, distributed training, and computer vision, along with the ability to optimize and deploy multimodal models in mission-critical environments.
Huge plus for candidates who have hands-on experience taking multimodal models such as CLIP, LLaVA, Qwen-VL, or similar Vision Language Models and fine-tuning them on classified or mission-specific imagery datasets. The ideal candidate can build the AWS infrastructure needed to train and scale these models, evaluate performance improvements across real-world use cases, and deploy solutions into secure government or air-gapped environments.
Key Responsibilities:
  • Design and execute fine-tuning pipelines for Vision Language Models (VLMs) using domain-specific imagery datasets
  • Handle data preprocessing, training orchestration, and hyperparameter optimization for multimodal models
  • Build evaluation frameworks for image understanding, visual question answering, and spatial reasoning tasks
  • Develop scalable AWS-based ML infrastructure using SageMaker and GPU-enabled EC2 for distributed training
  • Create data pipelines for curating, annotating, and transforming geospatial imagery into model-ready datasets
  • Partner with applied scientists and architects on model architecture improvements, LoRA/QLoRA strategies, and inference optimization

Required Qualifications:
  • Active TS/SCI with CI Poly
  • 5+ years of machine learning engineering experience focused on deep learning
  • 1+ year of hands-on experience fine-tuning foundation models (LLMs or VLMs)
  • Experience with LoRA, QLoRA, adapters, supervised fine-tuning, instruction tuning, and RLHF/DPO
  • 4+ years of advanced Python development for ML workloads
  • Strong PyTorch and HuggingFace experience (Transformers, PEFT, Datasets, Accelerate)
  • Experience with distributed training frameworks such as DeepSpeed, FSDP, or Megatron
  • 3+ years working with computer vision or multimodal models
  • Familiarity with vision transformer architectures (ViT, CLIP, LLaVA, etc.)
  • Experience processing and augmenting image datasets at scale
  • 3+ years with AWS ML infrastructure including SageMaker, EC2 GPU environments, and S3
  • Experience with ML evaluation pipelines, benchmarking, metrics, and result analysis
  • Strong software engineering fundamentals including version control, testing, and CI/CD

Preferred Qualifications:
  • 2+ years working with geospatial or remote sensing imagery
  • Experience with EO or SAR satellite imagery
  • Understanding of geospatial metadata, coordinate systems, and imagery preprocessing
  • Experience with model quantization / inference optimization (vLLM, TensorRT, ONNX)
  • MLOps tooling experience (MLflow, Weights & Biases, SageMaker Experiments)
  • Familiarity with annotation tools and active learning workflows
  • Containerized ML experience with Docker / ECR / ECS / EKS
  • Experience supporting ATO processes and NIST 800-53 compliance
  • Experience deploying in air-gapped/disconnected environments
  • Familiarity with multimodal evaluation benchmarks (MMMU, MMBench, GQA)
  • Publications or contributions in computer vision, multimodal AI, or VLMs
  • Synthetic data generation experience for training augmentation

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