Example Company
Software engineer
- Location
- San Francisco
- Type
- Full time
- Compensation
- $150k-$200k
- Posted
- 2 days ago
Join a team building useful software. Work closely with engineering and design to turn new ideas into products.
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Student Intern - Research
- Type
- Full time
- Posted
- 6D AGO
About the role
Join us as an Intern with Nokia Bell Labs in Bangalore. The Bell Labs Platform ASIC Research lab explores advanced computing architectures and algorithms for next-generation communication systems. In this internship, you will focus on enabling energy-efficient AI and wireless communication workloads through hardware-aware AI algorithms, neuromorphic computing, and domain-specific hardware acceleration, with potential exploration of RISC-V-based accelerators. You will work across AI/ML, wireless signal processing, energy-efficient computing, and hardware/software co-design, developing concepts and running experiments in Python and C. The work may also involve evaluating compute and energy-efficiency trade-offs and contributing to Verilog-based implementations, accelerator microarchitecture, or architecture design studies. You will work closely with Nokia Bell Labs researchers on early-stage research that could influence future intelligent and energy-efficient wireless systems.
You have
- Enrolled in a Ph.D. or MS Research program at the time of the internship.
- Strong background in one or more areas such as computer architecture, VLSI/digital design, AI/ML algorithms, wireless communications, signal processing, embedded systems or hardware/software co-design
- Hands-on programming experience in Python and/or C.
- Ability to design experiments, analyze results, and iterate on research ideas independently.
- Strong fundamentals in algorithms, digital systems, and performance/energy trade-off analysis.
- Availability for a 6-month internship.
It would be nice if you also have
- Exposure to RISC-V architecture, instruction-set extensions, accelerator design, or processor microarchitecture.
- Interest or experience in neuromorphic computing, spiking neural networks, event-driven compute, or low-power AI.
- Familiarity with energy-efficient computing techniques such as quantization, sparsity, approximate computing, pruning, or workload-specific acceleration.
- Understanding of RTL design, hardware simulation, or FPGA/ASIC design flows.
Key Responsibilities
- Explore research ideas related to energy-efficient compute for AI-driven wireless communication systems.
- Study and evaluate neuromorphic computing approaches and their applicability to wireless workloads.
- Develop experimental prototypes, models, and benchmarks using Python and C.
- Explore accelerator concepts, including RISC-V-based acceleration, custom instruction extensions, or hardware/software co-design approaches.
- Potentially contribute to Verilog implementation, microarchitecture exploration, or architecture-level design and simulation.
- Compare conventional AI compute approaches with low-power, event-driven, approximate, or neuromorphic compute techniques.
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