Key Responsibilities- Conduct advanced research in wireless communication systems, including PHY/MAC layers, radio architectures, networking protocols, and end-to-end system design
- Design and evaluate AI/ML-driven wireless algorithms (e.g., semantic communications, intent based and agentic AI communications, GenAI-assisted network optimization, resource allocation, edge intelligence)
- Develop and implement link-level and system-level simulations using tools such as MATLAB, Python, Sionna, or equivalent frameworks
- Build proof-of-concept prototypes (e.g., OAI, SDR, digital twins, edge AI testbeds) and validate concepts on practical platforms or testbeds
- Prototype Agentic AI and GenAI workflows for autonomous network planning, troubleshooting, orchestration, and standards-oriented research acceleration
- Explore Physical AI concepts that connect perception, sensing, wireless control, robotics/automation, and embodied AI systems over reliable low-latency networks
- Evaluate networking protocol behavior across IP, transport, application, RAN/core, edge/cloud, and distributed AI deployment environments
- Analyze and optimize system performance using theoretical and data-driven approaches
- Contribute to technical reports, publications, and patent filings
- Collaborate with cross-functional research teams across wireless, AI, standards, and prototyping groups
Required Qualifications- Ph.D. recently completed in Electrical Engineering, Computer Engineering, Computer Science, or a closely related field.
- Strong foundation in one or more of the following areas:
- Wireless communications
- Signal processing
- Networking
- AI/ML for communication systems
- Distributed or edge intelligence
- Solid knowledge of AI/ML techniques and their application to communication systems including GenAI, Agentic AI, foundation models, and edge learning
- Understanding of networking protocols and architectures such as TCP/IP, UDP, QUIC, HTTP/2/3, gRPC, MQTT, routing, SDN/NFV, and edge/cloud networking
- Programming proficiency in Python and MATLAB, with working knowledge of C/C++ and Linux-based development environments
- Experience with simulation and programming tools, such as:
- MATLAB, Python, C/C++, or similar scientific and systems programming languages
- Machine learning frameworks (e.g., TensorFlow, PyTorch)
- Demonstrated ability to translate theory into working simulations or prototypes
Preferred Qualifications- Hands-on experience with:
- System-level or link-level simulators (e.g., Sionna or equivalent)
- Wireless prototyping platforms (e.g., SDRs, OAI testbeds, or real-time systems)
- Generative AI and agent toolchains, including LLM APIs, RAG pipelines, vector databases, prompt/evaluation frameworks, and autonomous agent orchestration
- Physical AI, robotics, sensing, perception, control, embodied AI, or cyber-physical systems integrated with wireless connectivity
- MLOps and reproducible AI workflows using Docker, Kubernetes, MLflow, CI/CD, and GPU acceleration (CUDA where applicable)
- Research track record including publications, patents, or technical contributions
- Familiarity with cellular standards (4G/5G/6G evolution)
- Experience in AI-native wireless systems, edge intelligence, or semantic communications
- Strong software engineering practices (e.g., Git, reproducible research workflows)
- Additional AI technologies: RAG, multimodal/foundation models, edge AI, federated/split learning, digital twins, Physical AI, robotics/automation, sensing, perception, and control
Locations: Conshohocken, PA; Melville, NY; Montréal, QC, Canada
What You Bring- Curiosity and passion for pushing the boundaries of wireless and AI convergence
- Ability to balance theoretical rigor with practical implementation
- Strong analytical, problem-solving, and communication skills
- A collaborative mindset aligned with a global research environment
A reasonable estimate of the current salary range specific to NY/CA/DE/DC is $80,000 - $120,000 /annually + discretionary incentive bonus, benefits and may include other forms of compensation components such as long-term incentives. Compensation for the role will depend on a number of factors, including a candidate's qualifications, skills, competencies and experience and may fall outside of the range shown.
