Embedded Platform Intern

Why Us?

• Competitive incentives, galvanizing workspace, blazing team, frequent outings—pretty much everything you have heard about a startup + you get to work on space technology.
• Hustle in a well-funded startup allowing you to take charge of your responsibilities and create your moonshot.

Ideal Candidate:

A curious, hands-on candidate with a strong grounding in Linux architecture, keen to understand how vision and ML algorithms behave on embedded hardware.

Responsibilities

• Bring up and configure embedded Linux boards, including drivers, runtimes and SDKs, for benchmarking.
• Design benchmark plans for image processing pipelines (resize, colour conversion, filtering, ISP-style stages, optical flow) and ML models (classification, detection, segmentation).
• Map workloads across CPU cores, GPU and NPU/DSP, and compare execution on each, including heterogeneous split pipelines.
• Measure end-to-end and per-stage latency, throughput (FPS), jitter, CPU/GPU/NPU utilisation, memory bandwidth and footprint, power and thermal behaviour.
• Use Linux profiling tools to locate bottlenecks.
• Evaluate the effect of quantisation (FP32/FP16/INT8), batch size, input resolution, threading, CPU affinity, DVFS governors and zero-copy buffer sharing.
• Validate accuracy against reference results so that performance gains do not hide quality loss.
• Automate repeatable benchmark runs, keep results traceable to hardware and software configuration, and document findings with clear recommendations.

Required Qualifications

• Pursuing B.Tech/B.E or M.Tech/M.E/M.S in Electronics, Electrical, Computer Science or a related discipline.
• Solid grasp of computer architecture: caches, memory hierarchy, SIMD, multi-core execution, and how GPUs and NPUs differ from CPUs.
• Working knowledge of Linux: shell, processes and threads, file systems, building and cross-compiling software.
• Programming in C/C++ or Python sufficient to run, modify and script experiments.
• Basics of digital image processing (convolution, colour spaces, filtering, scaling) and neural networks (CNN layers, inference vs training).
• Understanding of measurement fundamentals: averages vs percentiles, warm-up, variance and controlled experiments.

Preferred Skills

• Experience with vision libraries such as OpenCV, GStreamer, V4L2 or OpenVX.
• Exposure to inference runtimes such as TensorRT, TensorFlow Lite, ONNX Runtime, OpenVINO, TVM or vendor NPU SDKs (TIDL, eIQ, SNPE/QNN, RKNN).
• Knowledge of parallel programming with CUDA, OpenCL, Vulkan compute, NEON intrinsics, OpenMP or pthreads.
• Familiarity with PyTorch or TensorFlow, ONNX model export and INT8 quantisation.
• Understanding of Linux internals: device tree, kernel modules, schedulers, cgroups, DVFS/cpufreq, DMA-BUF.
• Prior projects on Raspberry Pi, Jetson or similar boards, and use of Yocto/Buildroot, Docker, Git and Python data tools.

General Requirements

• Ability to work in a mission-focused, operational environment.
• Ability to think critically and make independent decisions.
• Interpersonal skills to enable working in a diverse and dynamic team.
• Maintain a regular and predictable work schedule.
• Writing and delivering technical reports and briefings.
• Verbal and written communication skills as well as organisational skills.

Duration

6 months (provisionally extendable), full time.

Job Location

Hebbal, Bengaluru (on-site, lab-based).

Work Type

WFO with alternate Saturdays.