AI Research Engineer

3 days ago

Bengaluru, Karnataka, India beBeeResearch Full time ₹ 3,00,000 - ₹ 7,50,000

AI Research Engineer Role

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We are seeking a highly motivated and detail-oriented AI Research Engineer to join our team. As an Embedded AI researcher, you will be responsible for taking models from dataset to experiment to deployment on Mankomb hardware with tight latency/memory/power budgets.

Data Collection and Preparation
  • You will plan and execute data collection on devices (vision/sensors), including checklists, labelling, sampling plans, and consent.
  • You will build labeling pipelines (e.g., CVAT/Label Studio) and ensure dataset hygiene (splits, versioning, metadata).
  • You will implement simple data quality gates (blur/occlusion checks, class balance) and maintain a data catalog.
Experimentation and Metrics
  • You will write reproducible experiments (config-driven) with versioned runs; track metrics (precision/recall/F1, mAP, ROC-AUC) and confusion matrices.
  • You will perform hyperparameter search, ablations, and error analysis; summarize results in weekly experiment notes.
Model Development and Optimization
  • You will implement and train models in Python (PyTorch/TensorFlow) with clean module structure, typing, and tests.
  • You will apply compression: quantization-aware training, pruning, distillation. Export to ONNX/TFLite as needed.
Deployment on Mankomb Hardware
  • You will convert/optimize models for edge: TFLite/TFLite-Micro/ONNX-Runtime; leverage NEON, CMSIS-NN, or vendor NPUs where applicable.
  • You will write/optimize C/C++ inference code, circular buffers, and low-latency I/O. Profile latency, RAM/Flash, FPS, power, and thermal behavior.
  • You will integrate with firmware/OS (Linux/RTOS/FreeRTOS), cross-compile toolchains, and CMake builds.
Embedded and Systems Integration
  • You will implement sensor drivers (I²C/SPI/UART), basic image signal pipeline tweaks, and real-time scheduling.
  • You will collaborate with mech/EE teams on fixtures, lighting, shielding, and EMI/EMC-aware data capture.

Key Requirements:

  • Strong in C/C++ and embedded concepts (memory management, interrupts, DMA, RTOS basics).
  • Proficient in Python for data/ML (NumPy/Pandas, PyTorch or TensorFlow, OpenCV).
  • Comfortable with Linux, cross-compilers, CMake, Git; basic shell scripting.
  • Understanding of model deployment constraints (INT8 quantization, fixed-point math, on-device profiling).
  • Clear fundamentals in DSA, OOP, numerics, and signals.

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