{"id":3086255,"url":"https://alion.io/job/digantara-embedded-platform-intern","title":"Embedded platform Intern","company":{"id":1970,"name":"Digantara","domain":"digantara.co.in","url":"https://alion.io/company/digantara","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Kula","truth_index":null},"role":"Early Careers","role_family":"Early Careers","seniority":"intern","employment_type":"internship","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":8000,"max_usd":15500,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":768},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"CNN","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Apache TVM","optional":true},{"name":"C","optional":true},{"name":"CUDA","optional":true},{"name":"Docker","optional":true},{"name":"Git","optional":true},{"name":"GStreamer","optional":true},{"name":"LiteRT","optional":true},{"name":"ONNX","optional":true},{"name":"ONNX Runtime","optional":true},{"name":"OpenCL","optional":true},{"name":"OpenCV","optional":true},{"name":"OpenMP","optional":true},{"name":"OpenVINO","optional":true},{"name":"Pthreads","optional":true},{"name":"PyTorch","optional":true},{"name":"TensorFlow","optional":true},{"name":"TensorFlow C++","optional":true},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2026-10-21T13:44:25Z","employer_posted_date":"2026-10-21","last_verified_at":"2026-10-23T16:13:11Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Digantara is a leading Space Surveillance and Intelligence company focused on ensuring orbital safety and sustainability. With expertise in space-based detection, tracking, identification, and monitoring, Digantara provides comprehensive domain awareness across regimes, allowing end users to have actionable intelligence on a single platform. At the core of its infrastructure lies a sophisticated integration of hardware and software capabilities aligned with the key principles of situational awareness: perception (data collection), comprehension (data processing), and prediction (analytics). This holistic approach empowers Digantara to monitor all Resident Space Objects (RSOs) in orbit, fostering comprehensive domain awareness.\nDigantara is seeking an Embedded software, AI & Vision algorithms Intern to benchmark and characterise image processing and machine learning algorithms on embedded platforms with multi-core CPUs, GPUs and NPUs running Linux. The intern will help determine how on-board processing algorithms should be mapped to heterogeneous compute engines to achieve the best performance, power efficiency and accuracy.\nWhy Us?\nCompetitive incentives, galvanizing workspace, blazing team, frequent outings-pretty much everything you have heard about a startup + you get to work on space technology.\n\nHustle in a well-funded startup allowing you to take charge of your responsibilities and create your moonshot.\n\nIdeal Candidate:\nA curious, hands-on candidate with a strong grounding in Linux architecture, keen to understand how vision and ML algorithms behave on embedded hardware.\nResponsibilities\nBring up and configure embedded Linux boards, including drivers, runtimes and SDKs, for benchmarking.\n\nDesign benchmark plans for image processing pipelines (resize, colour conversion, filtering, ISP-style stages, optical flow) and ML models (classification, detection, segmentation).\n\nMap workloads across CPU cores, GPU and NPU/DSP, and compare execution on each, including heterogeneous split pipelines.\n\nMeasure end-to-end and per-stage latency, throughput (FPS), jitter, CPU/GPU/NPU utilisation, memory bandwidth and footprint, power and thermal behaviour.\n\nUse Linux profiling tools to locate bottlenecks.\n\nEvaluate the effect of quantisation (FP32/FP16/INT8), batch size, input resolution, threading, CPU affinity, DVFS governors and zero-copy buffer sharing.\n\nValidate accuracy against reference results so that performance gains do not hide quality loss.\n\nAutomate repeatable benchmark runs, keep results traceable to hardware and software configuration, and document findings with clear recommendations.\n\nRequired Qualifications\nPursuing B.Tech/B.E or M.Tech/M.E/M.S in Electronics, Electrical, Computer Science or a related discipline.\n\nSolid grasp of computer architecture: caches, memory hierarchy, SIMD, multi-core execution, and how GPUs and NPUs differ from CPUs.\n\nWorking knowledge of Linux: shell, processes and threads, file systems, building and cross-compiling software.\n\nProgramming in C/C++ or Python sufficient to run, modify and script experiments.\n\nBasics of digital image processing (convolution, colour spaces, filtering, scaling) and neural networks (CNN layers, inference vs training).\n\nUnderstanding of measurement fundamentals: averages vs percentiles, warm-up, variance and controlled experiments.\n\nPreferred Skills\nExperience with vision libraries such as OpenCV, GStreamer, V4L2 or OpenVX.\n\nExposure to inference runtimes such as TensorRT, TensorFlow Lite, ONNX Runtime, OpenVINO, TVM or vendor NPU SDKs (TIDL, eIQ, SNPE/QNN, RKNN).\n\nKnowledge of parallel programming with CUDA, OpenCL, Vulkan compute, NEON intrinsics, OpenMP or pthreads.\n\nFamiliarity with PyTorch or TensorFlow, ONNX model export and INT8 quantisation.\n\nUnderstanding of Linux internals: device tree, kernel modules, schedulers, cgroups, DVFS/cpufreq, DMA-BUF.\n\nPrior projects on Raspberry Pi, Jetson or similar boards, and use of Yocto/Buildroot, Docker, Git and Python data tools.\n\nGeneral Requirements\nAbility to work in a mission-focused, operational environment.\n\nAbility to think critically and make independent decisions.\n\nInterpersonal skills to enable working in a diverse and dynamic team.\n\nMaintain a regular and predictable work schedule.\n\nWriting and delivering technical reports and briefings.\n\nVerbal and written communication skills as well as organisational skills.\n\nDuration: 6 months (provisionally extendable), full time.\nJob Location: Hebbal, Bengaluru (on-site, lab-based).\nWork Type: WFO with alternate 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