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Remote Deep Learning Jobs in Ashburn, VA (NOW HIRING)

NT Concepts

Data Scientist

Herndon, VA · On-site +1

Herndon, VA with remote flexibility. Must be local to the DC Metro area. Responsibilities * Curate ... Proficiency in Python and familiarity with machine learning and deep learning libraries such as ...

Team Velocity

Senior Data Scientist

Herndon, VA · On-site +1

$160K - $190K/yr

XGBoost, LightGBM, Random Forests, Neural Networks, Deep Learning * Statistics: Regression ... Local and remote candidates (living within Eastern or Central Time Zone) will be considered. No ...

Team Velocity

Senior Data Scientist

Herndon, VA · On-site +1

$160K - $190K/yr

XGBoost, LightGBM, Random Forests, Neural Networks, Deep Learning * Statistics: Regression ... Local and remote candidates (living within Eastern or Central Time Zone) will be considered. No ...

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All JobsDeep Learning JobsRemote Deep Learning JobsRemote Deep Learning Jobs in Ashburn, VA

Remote Deep Learning information

See Ashburn, VA salary details

$11.2K

$85.8K

$143.2K

How much do remote deep learning jobs pay per year?

As of Aug 7, 2026, the average yearly pay for remote deep learning in Ashburn, VA is $85,782.00, according to ZipRecruiter salary data. Most workers in this role earn between $73,600.00 and $142,100.00 per year, depending on experience, location, and employer.

What is a remote deep learning engineer?

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 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 skills and qualifications are needed to thrive as a remote deep learning engineer?

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 Ashburn, VA? The most popular types of Deep Learning jobs in Ashburn, VA are:
What are popular job titles related to Remote Deep Learning jobs in Ashburn, VA? For Remote Deep Learning jobs in Ashburn, VA, the most frequently searched job titles are:
What job categories do people searching Remote Deep Learning jobs in Ashburn, VA look for? The top searched job categories for Remote Deep Learning jobs in Ashburn, VA are:
What cities near Ashburn, VA are hiring for Remote Deep Learning jobs? Cities near Ashburn, VA with the most Remote Deep Learning job openings:
Remote Deep Learning jobs near you

SAR Data/Imagery Scientist (TS/SCI Clearance required)

NorthHill Technology

Falls Church, VA • On-site, Remote

Full-time

Posted 23 days ago

Job description

NorthHill Technology Resources has a need for a SAR Data Scientist/Imagery Scientist to join a Federal Program in Falls Church, VA.  This is a direct-hire opportunity with our client, a highly respected Federal Integrator.  A current TS/SCI Clearance is required. 

We are seeking an experienced SAR Data Scientist/Imagery Scientist to join a high-performing team supporting advanced AI and machine learning initiatives for complex national security and intelligence missions.

This program focuses on evaluating AI models against Government datasets to assess their performance, resilience, and robustness across a broad spectrum of adversarial scenarios. The effort also includes testing and validating autonomous algorithms designed to support operational decision-making in dynamic mission environments.

As a SAR Exploitation/Imagery Scientist, you will provide subject matter expertise in Synthetic Aperture Radar (SAR) imagery, geospatial analysis, and quantitative assessment to support data curation, imagery exploitation, and dataset development for machine learning model testing and evaluation. You will collaborate with engineers, data scientists, and mission analysts to ensure imagery products are prepared, standardized, and optimized for AI/ML applications.

Required Qualifications

  • Active TS/SCI clearance with eligibility for CI Polygraph (we can sponsor your CI poly if you don't already have one)
  • 4+ years of experience working with Synthetic Aperture Radar (SAR) imagery, including collection methodologies, radar phenomenology, image formation, and exploitation products.
  • Experience evaluating SAR imagery quality metrics and interpreting sensor metadata, including the effects of collection geometry (e.g., graze angle, squint angle, azimuth) on SAR phenomenology.
  • Demonstrated experience exploiting SAR imagery to detect, identify, and geolocate objects of interest.
  • Strong understanding of remote sensing principles, imagery processing, and advanced SAR exploitation techniques.
  • Excellent communication skills with the ability to effectively present SAR methodologies, imagery products, and analytical findings to both technical and non-technical audiences.
Preferred Qualifications
  • Experience applying computer vision (CV), machine learning (ML), or deep learning techniques to SAR imagery and geospatial data to support intelligence, defense, or remote sensing applications.
Key Responsibilities
  • Support the Lead SAR Scientist in evaluating emerging sensor capabilities and comparing new collection platforms with existing operational systems.
  • Assess the impact of new sensor data on existing data architectures, including metadata, file formats, schemas, APIs, and ETL processes required to ingest and integrate data into operational pipelines.
  • Evaluate data acquisition strategies, expected collection latency, available data formats, and applicable security domains for new sensor sources.
  • Develop preprocessing and standardization workflows to prepare imagery for labeling, analytics, and AI/ML model testing. This may include file format conversion, image tiling, geospatial normalization, and other data preparation activities to ensure compatibility with established data standards.