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Junior Aws Machine Learning Jobs in Virginia (NOW HIRING)

Quevera LLC

Software Engineer II

Herndon, VA · On-site

$100K - $137K/yr

Working alongside applied scientists and engineering teams, you will design scalable machine learning pipelines, fine-tune Vision-Language Models (VLMs), build AWS-based training infrastructure, and ...

Quevera LLC

Software Engineer II

Herndon, VA · On-site

$100K - $137K/yr

Working alongside applied scientists and engineering teams, you will design scalable machine learning pipelines, fine-tune Vision-Language Models (VLMs), build AWS-based training infrastructure, and ...

Halvik

Machine Learning Engineer - Remote

Vienna, VA · On-site

$140K - $150K/yr

Share knowledge and mentor junior team members.Required Skills:5+ years of experience in ML ... Experience with AWS-based data infrastructure and related DevOps practices.Demonstrated ability to ...

Halvik

Machine Learning Engineer - Remote

Vienna, VA · On-site +1

$140K - $150K/yr

Leverage AWS services including S3, EC2, Lambda, SageMaker, and Step Functions. Collaboration ... Share knowledge and mentor junior team members. Required Skills: * 5+ years of experience in ML ...

Unissant

Machine Learning Engineer

Ashburn, VA · On-site

$110 - $170/hr

We are seeking a Machine Learning Engineer to join our team and support our client in Ashburn, VA ... Exposure to cloud platforms such as AWS, Azure, or GCP for model deployment, storage, or related ...

UNISSANT

Machine Learning Engineer

Ashburn, VA

We are seeking a Machine Learning Engineer to join our team and support our client in Ashburn, VA ... Exposure to cloud platforms such as AWS, Azure, or GCP for model deployment, storage, or related ...

Cymertek

Machine Learning Engineer

Chantilly, VA · On-site

Familiarity with cloud-based platforms for machine learning (e.g., AWS, Google Cloud, Azure) * Strong problem-solving skills and analytical thinking REQUIRED SKILLS * Proficiency in programming ...

Cymertek

Machine Learning Engineer

Tysons, VA · On-site

Familiarity with cloud-based platforms for machine learning (e.g., AWS, Google Cloud, Azure) * Strong problem-solving skills and analytical thinking REQUIRED SKILLS * Proficiency in programming ...

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Junior Aws Machine Learning information

What is a junior AWS machine learning engineer?

Junior AWS Machine Learning engineers are entry-level professionals who work with Amazon Web Services (AWS) to develop, deploy, and maintain machine learning models. They assist in data preparation, model training, and integration of AI solutions using AWS tools such as SageMaker, Lambda, and S3. These engineers often collaborate with data scientists and software teams to implement predictive analytics and automation solutions on the AWS cloud platform. Their role typically involves learning best practices for cloud security, data handling, and scalable machine learning deployment.

What are the key skills and qualifications needed to thrive as a junior AWS machine learning engineer?

To thrive as a Junior AWS Machine Learning Engineer, you need a solid grasp of programming (especially Python), foundational knowledge of machine learning concepts, and a relevant degree in computer science or a related field. Familiarity with AWS services like SageMaker, Lambda, and S3, as well as certifications such as AWS Certified Machine Learning – Specialty, are highly valuable. Strong problem-solving skills, attention to detail, and the ability to communicate technical ideas clearly help you stand out in this role. These skills and qualities are crucial for efficiently developing, deploying, and maintaining machine learning solutions on AWS in collaborative, fast-paced environments.

Are there entry level AWS jobs?

Yes, there are entry-level AWS jobs such as Junior AWS Machine Learning roles that typically require foundational knowledge of cloud computing, basic understanding of machine learning concepts, and familiarity with AWS services like S3, EC2, and SageMaker. These roles often serve as starting points for careers in cloud and machine learning fields and may require certifications like AWS Certified Cloud Practitioner or AWS Certified Machine Learning – Specialty. Candidates should be prepared to learn on the job and develop skills through training and hands-on experience.

What are some common challenges faced by junior AWS machine learning engineers when deploying models to production environments?

Junior AWS Machine Learning Engineers often encounter challenges such as managing the scalability of their models, ensuring data security and compliance in the cloud, and integrating machine learning pipelines with existing AWS services. Since production environments require high reliability, newcomers may also need to learn how to monitor model performance and troubleshoot issues using AWS tools like SageMaker and CloudWatch. Collaborating closely with data engineers and DevOps teams is essential to streamline deployment and maintain model accuracy over time.

What is the difference between Junior Aws Machine Learning vs Data Scientist?

AspectJunior Aws Machine LearningData Scientist
Required CredentialsBasic AWS certifications, entry-level ML knowledgeAdvanced degrees, certifications like AWS, data analysis skills
Work EnvironmentCloud platforms, machine learning projects, collaborative teamsData analysis, modeling, research, cross-functional teams
Employer & Industry UsageTech companies, startups, cloud service providersFinance, healthcare, tech, research institutions

Junior AWS Machine Learning roles focus on implementing ML models using AWS tools with foundational knowledge, while Data Scientists typically handle broader data analysis, modeling, and research tasks. The roles overlap in cloud-based ML work but differ in scope and experience level.

What are the most commonly searched types of Aws Machine Learning jobs in Virginia? The most popular types of Aws Machine Learning jobs in Virginia are:
What are popular job titles related to Junior Aws Machine Learning jobs in Virginia? For Junior Aws Machine Learning jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Junior Aws Machine Learning jobs in Virginia look for? The top searched job categories for Junior Aws Machine Learning jobs in Virginia are:
What cities in Virginia are hiring for Junior Aws Machine Learning jobs? Cities in Virginia with the most Junior Aws Machine Learning job openings:
Junior Aws Machine Learning jobs near you

Software Engineer II

Quevera LLC

Herndon, VA • On-site

$100K - $137K/yr

Other

Medical, Dental, Vision, Life, Retirement

Re-posted 10 days ago

Job description


Quevera is seeking a highly skilled Software Engineer II with an active TS/SCI clearance with Polygraph to support mission-critical programs. In this role, you will develop and optimize advanced machine learning solutions supporting multimodal artificial intelligence and computer vision applications for national security missions.
Working alongside applied scientists and engineering teams, you will design scalable machine learning pipelines, fine-tune Vision-Language Models (VLMs), build AWS-based training infrastructure, and develop data processing and evaluation frameworks for large-scale geospatial imagery datasets. You'll leverage modern AI technologies to deliver high-performing, secure, and production-ready machine learning solutions.
As a Software Engineer II, you'll have the opportunity to work with cutting-edge AI technologies, collaborate with industry experts, and contribute to innovative solutions supporting critical national security missions.
Work Schedule:
Work Location: Must be willing to work onsite in a SCIF daily, or as required.
Job Responsibilities:
  • Design and execute fine-tuning pipelines for Vision-Language Models (VLMs) using domain-specific imagery datasets.
  • Develop data preprocessing, training orchestration, and hyperparameter optimization workflows.
  • Build and implement evaluation frameworks for multimodal model performance, including image understanding, visual question answering, and spatial reasoning.
  • Develop scalable distributed training infrastructure using AWS services, including SageMaker and EC2 GPU instances.
  • Engineer data pipelines for curating, annotating, and transforming geospatial imagery into model-ready datasets.
  • Collaborate with applied scientists and solutions architects to optimize model architectures and parameter-efficient fine-tuning strategies, including LoRA and QLoRA.
  • Optimize model inference performance and deployment workflows.
  • Develop secure, scalable machine learning solutions that support mission requirements.

Minimum Requirements:
  • Active TS/SCI clearance with Polygraph required.
  • Current NGA eligibility with active SBU, SECNet, and COE accounts.
  • Five (5) or more years of professional machine learning engineering experience with a focus on deep learning.
  • One (1) or more years of experience fine-tuning large language models (LLMs) or Vision-Language Models (VLMs).
  • Experience with parameter-efficient fine-tuning techniques, including LoRA, QLoRA, and adapters.
  • Familiarity with supervised fine-tuning, instruction tuning, and RLHF/DPO alignment techniques.
  • Four (4) or more years of advanced Python development for machine learning workloads.
  • Strong proficiency with PyTorch and the Hugging Face ecosystem, including Transformers, PEFT, Datasets, and Accelerate.
  • Experience with distributed training frameworks such as DeepSpeed, FSDP, or Megatron.
  • Three (3) or more years of experience with computer vision or multimodal AI models.
  • Understanding of Vision Transformer architectures, including ViT, CLIP, LLaVA, or similar models.
  • Experience processing and augmenting image datasets at scale.
  • Three (3) or more years of experience with AWS machine learning infrastructure, including SageMaker, EC2 GPU instances, and Amazon S3.
  • Experience building machine learning evaluation pipelines, including automated benchmarking, metric computation, and result analysis.
  • Strong software engineering fundamentals, including version control, testing, and CI/CD practices for machine learning workflows.

Desired Skills:
  • Active TS/SCI clearance with Polygraph required.
  • Current NGA eligibility with active SBU, SECNet, and COE accounts.
  • Five (5) or more years of professional machine learning engineering experience with a focus on deep learning.
  • One (1) or more years of experience fine-tuning large language models (LLMs) or Vision-Language Models (VLMs).
  • Experience with parameter-efficient fine-tuning techniques, including LoRA, QLoRA, and adapters.
  • Familiarity with supervised fine-tuning, instruction tuning, and RLHF/DPO alignment techniques.
  • Four (4) or more years of advanced Python development for machine learning workloads.
  • Strong proficiency with PyTorch and the Hugging Face ecosystem, including Transformers, PEFT, Datasets, and Accelerate.
  • Experience with distributed training frameworks such as DeepSpeed, FSDP, or Megatron.
  • Three (3) or more years of experience with computer vision or multimodal AI models.
  • Understanding of Vision Transformer architectures, including ViT, CLIP, LLaVA, or similar models.
  • Experience processing and augmenting image datasets at scale.
  • Three (3) or more years of experience with AWS machine learning infrastructure, including SageMaker, EC2 GPU instances, and Amazon S3.
  • Experience building machine learning evaluation pipelines, including automated benchmarking, metric computation, and result analysis.
  • Strong software engineering fundamentals, including version control, testing, and CI/CD practices for machine learning workflows.

Why Join Quevera?
Award-Winning Culture
Quevera was recognized as a Top Workplace in the Washington, DC/Baltimore region for 2025, marking our fifth consecutive year receiving this distinction based on employee feedback.
Outstanding Benefits
  • We invest in our employees and their families through a highly competitive benefits package, including:
  • 100% employer-paid medical coverage (optional plan)
  • Competitive options for Medical, Dental and Vision insurance
  • Employer-paid short-term and long-term disability coverage
  • Employer-paid life insurance
  • $5,000 annually for education, training, certifications, and professional development
  • Career advancement through our structured IQWay Program
  • Up to 6% 401(k) match
  • Additional 4% profit-sharing contribution

At Quevera, we believe exceptional people deserve exceptional opportunities. We're more than just a workplace-we're a team of innovators, problem-solvers, and industry experts committed to delivering mission-critical solutions while fostering professional growth, collaboration, and technical excellence.
Quevera is an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected veteran status, age or any other characteristic protected by law. #LI-AA1

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