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Remote Deep Learning Jobs in Washington, DC (NOW HIRING)

System One

Machine Learning/AI Engineer

Merrifield, VA · Remote

Hybrid in Vienna, VA or Remote Pay Rate: Open to Both W2 and established 1099 options (no c2c ... The position also emphasizes experience with Computer Vision, NLP, Deep Learning, LLMs, Agentic AI ...

System One

Machine Learning/AI Engineer

Merrifield, VA · Remote

Hybrid in Vienna, VA or Remote Pay Rate: Open to Both W2 and established 1099 options (no c2c ... The position also emphasizes experience with Computer Vision, NLP, Deep Learning, LLMs, Agentic AI ...

System One

Machine Learning/AI Engineer

Merrifield, VA · Remote

Hybrid in Vienna, VA or Remote Pay Rate: Open to Both W2 and established 1099 options (no c2c ... The position also emphasizes experience with Computer Vision, NLP, Deep Learning, LLMs, Agentic AI ...

Maximus

Data Scientist (Remote)

VA · On-site +1

General information Job Posting Title Data Scientist (Remote) Date Tuesday, August 4, 2026 City ... learning/deep learning frameworks (e.g., Scikit-learn, PyTorch, TensorFlow and Keras) and ...

Halvik

Machine Learning Engineer - Remote

Vienna, VA · On-site +1

$140K - $150K/yr

Proven experience with machine learning models and algorithms (supervised, unsupervised, deep learning, etc.). * Solid background in software engineering principles and best practices. * Hands-on ...

ASSYST, Inc.

Software Developer

Sterling, VA · Remote

Proficiency in data science, deep learning, and neural networks. * Ability to travel to local clients as required by project or client needs. * Remote work is not permitted. ASSYST Benefits: We are ...

ASSYST, Inc.

Software Developer

Sterling, VA · Remote

Proficiency in data science, deep learning, and neural networks. * Ability to travel to local clients as required by project or client needs. * Remote work is not permitted. ASSYST Benefits: We are ...

SAIC

Data Scientist

Mclean, VA · On-site +1

$200K - $240K/yr

None Potential for Remote Work: ORA_ON_SITE Description SAIC is seeking a Data Scientist to join ... Demonstrated professional or academic experience with deep learning frameworks such as PyTorch ...

SAIC

Data Scientist

Mclean, VA · On-site +1

$200K - $240K/yr

None Potential for Remote Work: ORA_ON_SITE Description SAIC is seeking a Data Scientist to join ... Demonstrated professional or academic experience with deep learning frameworks such as PyTorch ...

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Showing results 1-20

All JobsDeep Learning JobsRemote Deep Learning JobsRemote Deep Learning Jobs in Washington, DC

Remote Deep Learning information

See Washington, DC salary details

$12.5K

$95K

$158.6K

How much do remote deep learning jobs pay per year?

As of Sep 3, 2026, the average yearly pay for remote deep learning in Washington, DC is $95,008.00, according to ZipRecruiter salary data. Most workers in this role earn between $81,500.00 and $157,400.00 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 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 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 most commonly searched types of Deep Learning jobs in Washington, DC?

The most popular types of Deep Learning jobs in Washington, DC are:

What are popular job titles related to Remote Deep Learning jobs in Washington, DC?

For Remote Deep Learning jobs in Washington, DC, the most frequently searched job titles are:

What job categories do people searching Remote Deep Learning jobs in Washington, DC look for?

The top searched job categories for Remote Deep Learning jobs in Washington, DC are:

Remote Deep Learning jobs near you
Infographic showing various Remote Deep Learning job openings in Washington, DC as of June 2026, with employment types broken down into 74% Full Time, 22% Part Time, and 4% Contract. Highlights an 48% Physical, 3% Hybrid, and 49% Remote job distribution, with an average salary of $95,008 per year, or $45.7 per hour.

Senior Wireless Machine Learning Engineer, AI-RAN

DeepSig Inc

Arlington, VA • On-site, Remote

Full-time

Posted 8 days ago

Job description

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 the Radio Access Network (RAN). We are seeking an experienced Technical Lead to architect and drive the development of our next-generation AI-native RAN.


In this role, you will design, prototype, and validate novel AI/ML components-such as neural receivers, neural beamforming, neural scheduling, digital twin, and ISAC (Integrated Sensing and Communications)-that outperform traditional signal processing methods. You will work at the cutting edge of 6G innovation, taking concepts from mathematical intuition to simulation (e.g. NVIDIA Sionna) and real-time implementation.


What You'll be Doing 

  • Applied AI Research: Design and train modern deep learning models (Transformers, Vision architectures, etc.) to solve complex physical layer problems, including channel estimation, MIMO detection, and beam management
  • Simulation & Validation: Build high-fidelity link-level simulations using NVIDIA Sionna and ray-tracing to train, test, and benchmark AI models against legacy 5G baselines
  • Prototyping & Deployment: Transition research models into deployable "dApps" for the Distributed Unit (DU), optimizing inference for latency and compute efficiency on NVIDIA GPUs
  • New Capabilities: Explore emerging AI-RAN frontiers such as Integrated Sensing and Communications (ISAC), neural scheduling, and channel digital twins
  • Innovation & IPR: Drive technical innovation by authoring invention disclosures, filing patents, and generating technical reports to support our standardization team in 3GPP and O-RAN Alliance contributions
  • Data Engineering: Architect data pipelines for generating synthetic training datasets and developing "Sim-to-Real" transfer techniques to ensure robust performance in real-world networks

Required Qualifications

  • Education: Ph.D. or Master's in Computer Science, Electrical Engineering, or Applied Mathematics with a focus on Deep Learning and/or Communications Systems
  • AI/ML Expertise: 3+ years of experience designing and training deep neural networks from scratch. Strong grasp of modern architectures and optimization techniques
  • Applied Signal Processing: Experience applying machine learning to real-time time-series data, signal processing, or physics-based problems (Audio, RF, or similar domains)
  • Research to Code: Proven ability to read academic papers and implement their methods in robust Python code
  • Simulation Skills: Experience with differentiable simulation or digital twins (e.g., Sionna, JAX-based physics sims)

Preferred Qualifications

  • Wireless Knowledge: Understanding of wireless fundamentals (OFDM, MIMO, IQ data) is highly helpful, though we prioritize strong ML intuition over pure communication theory
  • Performance Optimization: Experience with model quantization (FP16/INT8), pruning, or using TensorRT for real-time inference
  • Standardization Support: Experience writing technical whitepapers or supporting patent filings in a research environment
  • C++ Integration: Ability to write C++ bindings or integrate Python models into C++, SIMD, and Cuda production pipelines

Working at DeepSig

DeepSig is growing its technical team while cultivating a collaborative, agile, and fun small-team culture. We value creativity, knowledge sharing, and employee growth, and we encourage participation in scientific publications, conferences, and open-source software. We offer competitive salaries and benefits, an employee stock option grant program, an environment where we are excited to be transforming and disrupting how signal processing is done with AI/ML, a welcoming and inclusive environment, a flexible schedule, and a great work / life balance.


DeepSig is an equal-opportunity employer and does not discriminate based on race, ethnicity, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability. We are dedicated to cultivating an inclusive, diverse, and engaging workplace where individuals feel fulfilled, inspired, and motivated. We value the unique perspectives that our team brings.





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