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Remote Deep Learning Jobs in Virginia (NOW HIRING)

... deep learning. Experience with at least two of the following: remote sensing of surface and ground water resources, analysis of satellite gravimetry (GRACE) data, analysis of radar and optical remote ...

Senior ML/AI Engineer

Richmond, VA ยท On-site +1

$103K - $142K/yr

... or remote applicants residing in states/locations under Eastern Standard Time: Connecticut ... Evaluate when to apply classical ML, deep learning, or LLM-driven approaches based on business ...

Senior ML/AI Engineer

Richmond, VA ยท On-site +1

$103K - $142K/yr

... or remote applicants residing in states/locations under Eastern Standard Time: Connecticut ... Evaluate when to apply classical ML, deep learning, or LLM-driven approaches based on business ...

Senior ML/AI Engineer

Richmond, VA ยท On-site +1

$103K - $142K/yr

... or remote applicants residing in states/locations under Eastern Standard Time: Connecticut ... Evaluate when to apply classical ML, deep learning, or LLM-driven approaches based on business ...

Description Type: Full-Time(W2) On-site/Hybrid, Arlington, VA (Remote option available for the right candidate) DeepSig is defining the future of wireless communications by merging deep learning with ...

Software Engineer II

Herndon, VA ยท On-site +1

$100K - $137K/yr

... on deep learning * 1+ years of hands-on experience fine-tuning large foundation models (LLMs or ... remote sensing imagery Familiarity with electro-optical and SAR satellite imagery formats and ...

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Remote Deep Learning information

See Virginia salary details

$20.9K

$127.4K

$208.1K

How much do remote deep learning jobs pay per year?

As of Jun 19, 2026, the average yearly pay for remote deep learning in Virginia is $127,398.00, according to ZipRecruiter salary data. Most workers in this role earn between $84,610.00 and $164,844.00 per year, depending on experience, location, and employer.

What is a Remote Deep Learning job?

A Remote Deep Learning job involves working with artificial intelligence and machine learning models, particularly using deep neural networks, from a location outside a traditional office, often from home. Professionals in this field design, build, and optimize algorithms that enable computers to learn from large amounts of data. They often work on projects such as image and speech recognition, natural language processing, or autonomous systems. The remote aspect allows flexibility and access to global opportunities, but requires strong communication skills and the ability to collaborate virtually with teams.

What are some common challenges faced by remote deep learning engineers, and how can they be addressed?

Remote deep learning engineers often encounter challenges such as limited access to high-performance computing resources, communication barriers with distributed teams, and difficulties in collaborating on large codebases or datasets. These issues can be mitigated by leveraging cloud-based platforms for scalable computing, using clear communication tools like Slack or Zoom for regular check-ins, and employing version control systems like Git for collaborative code management. Proactively setting up workflows and documentation helps ensure smooth collaboration and project continuity within a remote environment.

What is the difference between Remote Deep Learning vs Remote Machine Learning Engineer?

AspectRemote Deep LearningRemote Machine Learning Engineer
Required CredentialsBachelor's/Master's in CS, AI, or related; experience with neural networksBachelor's/Master's in CS, Data Science, or related; experience with algorithms and data modeling
Work EnvironmentCollaborative teams, research-focused, often in tech or AI companiesDevelopment teams, data-driven projects, across various industries
Employer & Industry UsageTech firms, AI startups, research institutionsTech companies, finance, healthcare, e-commerce

Remote Deep Learning specialists focus on designing and training neural networks for AI applications, often requiring advanced knowledge of deep neural architectures. Remote Machine Learning Engineers work on developing algorithms and models for broader data analysis and predictive tasks. While both roles involve machine learning, deep learning emphasizes neural networks, whereas machine learning engineers may work with a variety of algorithms across industries.

What are the key skills and qualifications needed to thrive as a Remote Deep Learning Engineer, and why are they important?

To thrive as a Remote Deep Learning Engineer, you need strong programming skills in Python, a deep understanding of machine learning algorithms, and typically a degree in computer science, engineering, or a related field. Proficiency with frameworks like TensorFlow or PyTorch, as well as cloud computing platforms such as AWS or Google Cloud, is essential, and certifications in these technologies can be advantageous. Excellent problem-solving abilities, self-motivation, and clear communication are crucial soft skills for remote collaboration and project delivery. These skills ensure effective development, deployment, and maintenance of deep learning models while working independently in distributed teams.
What are the most commonly searched types of Deep Learning jobs in Virginia? The most popular types of Deep Learning jobs in Virginia are:
What are popular job titles related to Remote Deep Learning jobs in Virginia? For Remote Deep Learning jobs in Virginia, the most frequently searched job titles are:
What cities in Virginia are hiring for Remote Deep Learning jobs? Cities in Virginia with the most Remote Deep Learning job openings:
Distinguished AI/ML Engineering Lead

Distinguished AI/ML Engineering Lead

Frontier Technology Inc.

Chesapeake, VA โ€ข Remote

$99K - $131K/yr

Full-time

Posted yesterday


Job description

FTI Defense delivers mission-focused solutions to the Department of Defense and Intelligence Community through advanced engineering, digital transformation, and program execution expertise. We help our customers solve complex challenges and achieve mission success by integrating people, process, and technology.

FTI Defense is seeking a Distinguished AI/ML Engineer to serve as a technical leader, architect, and integrator โ€” designing, building, deploying, and sustaining AI systems that transform complex mission data into trusted, explainable insights.

This is a hands-on builder role, not an analytics management position. The ideal candidate is equally comfortable writing model code, standing up ML pipelines, and integrating AI inference services into operational systems within secure environments. The right candidate blends deep AI/ML engineering expertise with system-level architecture leadership and an ability to unify data engineering, simulation modeling, and responsible AI principles into scalable, mission-ready capabilities.


  • Architect and integrate hybrid AI systems that combine traditional machine learning, deep learning, large language models (LLMs), and retrieval-augmented generation (RAG) pipelines.
  • Design and deploy scalable AI architectures including APIs, microservices, and model-serving frameworks that integrate seamlessly with analytic, simulation, or operational systems.
  • Lead the full AI/ML lifecycle โ€” from data ingestion and feature engineering through training, deployment, and sustainment within secure DoD environments (IL5/IL6, ATO, GovCloud).
  • Engineer event-driven data pipelines and feature stores for both structured and unstructured data, including text, imagery, and simulation outputs.
  • Ensure Responsible AI practices by embedding traceability, explainability, and confidence scoring into deployed systems.
  • Implement and maintain MLOps pipelines (MLflow, Kubeflow, Airflow, Docker/Kubernetes) to support continuous integration, retraining, and drift detection.
  • Transition R&D prototypes into production, optimizing for mission constraints such as limited compute, edge environments, or disconnected operations.
  • Provide technical leadership and mentorship, setting standards for model quality, architectural design, and ethical AI deployment across programs.
  • Collaborate across engineering, data, and modeling teams to unify FTIโ€™s AI portfolio, ensuring interoperability and reuse across mission systems.
  • Support proposal and solution development, providing technical inputs for AI/ML architectures, data strategies, and Responsible AI assurance frameworks.

  • Active Secret clearance required; TS/SCI strongly preferred.
  • Bachelorโ€™s degree in Computer Science, Engineering, or a related technical field (Masterโ€™s or Ph.D. preferred).
  • 10+ years of overall experience in AI/ML development, with 5+ years designing and deploying scalable AI/ML architectures, including at least two full lifecycle implementations (from prototype to operational system).
  • Proficiency in Python, PyTorch, TensorFlow, and modern ML frameworks.
  • Experience designing or deploying systems using vector databases (Milvus, Pinecone, Weaviate), knowledge graphs, and semantic search frameworks.
  • Proven ability to design event-driven data pipelines using Databricks, Spark, Flink, or Kafka.
  • Demonstrated experience deploying AI/ML systems in secure, classified, or edge environments.
  • Familiarity with Responsible AI and assurance principles, including bias detection, explainability, human-machine teaming, and hallucination prevention.
  • Experience integrating AI models into simulation, modeling, or operational planning systems is highly desirable.
  • Experience transitioning R&D systems into accredited production environments.
  • Strong communication and mentoring skills, with the ability to lead technically while remaining deeply hands-on.

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