Senior Wireless Machine Learning Engineer, AI-RAN

DeepSig Inc; Arlington (VA); Hybrid; USD 140,000 - 230,000; Full time

Benefits offered by this job

Employee stock option

Job summary

DeepSig Inc seeks a Technical Lead to architect and drive AI-native RAN development. You will design, prototype, and validate neural receivers, neural beamforming, and related AI/ML components for real-time deployment on NVIDIA GPUs in an on-site/hybrid setting in Arlington, VA.

Qualified candidates have 3+ years building DL models for signal processing, with strong Python and differentiable-simulation experience. Remote option considered for the right candidate.

Qualifications

• Ph.D. or Master’s in CS, EE, or Applied Math with DL/communications focus.
• 3+ years designing and training deep neural networks.
• Experience applying ML to real-time time-series data or physics-based problems.
• Ability to read papers and implement methods in robust Python code.
• Familiarity with differentiable simulation or digital twins.

Responsibilities

• Design and train DL models for channel estimation, MIMO detection and beam management.
• Build high-fidelity simulations using Sionna and ray-tracing to benchmark models.
• Prototype research models into deployable dApps for the DU with GPU inference.
• Explore AI-RAN frontiers like ISAC, neural scheduling, and channel digital twins.
• Drive IP through invention disclosures, patents, and technical docs.

Skills

Ph.D./Master's in CS/EE/Applied Math

DL/ML model design

Applied signal processing

Research to code

Simulation with differentiable sims

Education

Tools

NVIDIA Sionna

JAX

CUDA

Python

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.