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Remote Neural Engineer Jobs in Reston, VA (NOW HIRING)

Your focus will include Machine Learning, Natural Language Processing, Neural Networks, Large ... This role is remote/hybrid in the VA/MD/DC area. There may be occasional travel to client site in ...

AI Engineer

Washington, DC · On-site +1

$63.25 - $84.50/hr

Your focus will include Machine Learning, Natural Language Processing, Neural Networks, Large ... This role is remote/hybrid in the VA/MD/DC area. There may be occasional travel to client site in ...

... * We're remote - Work from wherever you want. We collaborate in real time on Slack or ... Expertise in PyTorch for building and training neural networks * Experience training and serving ...

Herndon, VA with remote flexibility. Must be local to the DC Metro area. Responsibilities * Curate ... Train, evaluate, and optimize deep neural network models on overhead imagery, including ...

Remote Neural Engineer information

See Reston, VA salary details

$61.9K

$116.1K

$211.2K

How much do remote neural engineer jobs pay per year?

As of May 29, 2026, the average yearly pay for remote neural engineer in Reston, VA is $116,137.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,700.00 and $137,800.00 per year, depending on experience, location, and employer.

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

To thrive as a Remote Neural Engineer, you need a solid background in neuroscience, biomedical engineering, or electrical engineering, often supported by a relevant degree or advanced certification. Proficiency with neural signal processing software, programming languages like Python or MATLAB, and brain-computer interface (BCI) systems is typically required. Strong problem-solving skills, attention to detail, and effective virtual communication are vital soft skills in this role. These skills and qualities are essential for developing, analyzing, and troubleshooting complex neural systems while collaborating with teams remotely.

How do Remote Neural Engineers typically collaborate with cross-functional teams while working off-site?

Remote Neural Engineers frequently use digital collaboration tools such as video conferencing, shared code repositories, and project management platforms to stay connected with colleagues in neuroscience, software development, and data science. Regular virtual meetings and asynchronous communication help ensure alignment on project goals, data analysis, and protocol development. This structure allows for flexibility, but also requires proactive communication and strong organizational skills to manage complex, interdisciplinary tasks from a distance.

What is a Remote Neural Engineer?

A Remote Neural Engineer is a professional who designs, develops, and maintains neural engineering systems—such as brain-computer interfaces or neural prosthetics—while working remotely. They often collaborate with multidisciplinary teams to create solutions that interface with the nervous system, using expertise in neuroscience, biomedical engineering, and software development. Remote Neural Engineers may work from home or distributed locations, utilizing digital tools to analyze neural data, develop algorithms, and contribute to research or product development in the neural technology field.

What is the difference between Remote Neural Engineer vs Remote Data Scientist?

AspectRemote Neural EngineerRemote Data Scientist
Required CredentialsDegree in neuroscience, biomedical engineering, or related fields; knowledge of neural interfacesDegree in computer science, statistics, or related fields; proficiency in data analysis
Work EnvironmentResearch labs, tech companies, healthcare institutions with focus on neural dataTech firms, finance, healthcare, analyzing large datasets
Industry UsageNeuroscience, biomedical engineering, neurotechnologyTechnology, finance, healthcare, research
Common Search/ComparisonYesYes

Remote Neural Engineers focus on developing and implementing neural interfaces and understanding neural systems, often requiring knowledge of neuroscience and biomedical engineering. Remote Data Scientists analyze large datasets to extract insights, typically with skills in statistics and programming. While both roles involve technical expertise and data analysis, Neural Engineers are more specialized in neural technologies, whereas Data Scientists have a broader application across industries.

What are popular job titles related to Remote Neural Engineer jobs in Reston, VA? For Remote Neural Engineer jobs in Reston, VA, the most frequently searched job titles are:
What job categories do people searching Remote Neural Engineer jobs in Reston, VA look for? The top searched job categories for Remote Neural Engineer jobs in Reston, VA are:
What cities near Reston, VA are hiring for Remote Neural Engineer jobs? Cities near Reston, VA with the most Remote Neural Engineer job openings:
Infographic showing various Remote Neural Engineer job openings in Reston, VA as of May 2026, with employment types broken down into 79% Part Time, and 21% Contract. Highlights an 86% Physical, and 14% Remote job distribution, with an average salary of $116,137 per year, or $55.8 per hour.
Senior Wireless Machine Learning Engineer, AI-RAN

Senior Wireless Machine Learning Engineer, AI-RAN

DeepSig Inc

Arlington, VA • On-site, Remote

Other

Posted 12 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.