Data Science Lead Edge AI

Location : Bangalore, India.

About Us :

Ambient Scientific is revolutionizing AI at the edge with ultra-low-power AI chips.

Our technology :

enables AI applications to run efficiently on battery-powered devices without reliance on cloud or network connectivity.

We are now building a robust software ecosystem to support developers in deploying AI applications seamlessly on our hardware.

Role Overview :

We are seeking a Data Science Lead specialized in Edge AI to drive the development and deployment of intelligent models on edge devices.

You will lead a team of data scientists and ML engineers to design, optimize, and deliver models that operate efficiently in constrained environments (e.g., microcontrollers, wearables, mobile platforms).

You will play a key role in bridging developer needs with internal engineering efforts, ensuring a seamless AI application development experience.

This role blends AI model innovation, low-power edge optimization and cross-functional leadership.

Key Responsibilities :

- Lead end-to-end development of Edge AI models : (Human activity recognition, fall detection, predictive maintenance, anomaly detection, etc.

- Architect lightweight models (e.g., TinyML, TFLite Micro) for microcontrollers and IoT devices.

- Collaborate with hardware, embedded, and firmware teams for edge deployment.

- Use techniques like : (Model quantization, pruning, knowledge distillation, ONNX conversion).

- Signal preprocessing (FFT, time-domain, sensor fusion).

- Drive data collection strategies for real-world edge use cases.

- Own model performance metrics : (Accuracy, latency, power consumption, memory footprint).

- Mentor a team of ML engineers and scientists; review code and model pipelines.

- Stay up to date with trends in TinyML, edge computing, and AI acceleration.

Key Requirements :

- Bachelors/Masters in computer science, Data Science, Electrical Engineering, or related.

- 5-7 years of experience in machine learning, with at least 2+ years in Edge AI.

- Proficiency in : (Python, NumPy, SciPy, TensorFlow Lite, PyTorch Mobile).

- Strong foundation in : (Signal processing (FFT, wavelets, sensor fusion)).

- Time series analysis (accelerometer, gyroscope, environmental sensors) Hands-on with hardware interfaces : I2C, SPI, BLE, ADC/DAC.

- Experience with tools like ONNX, MLIR is a plus.

- Knowledge of edge hardware (e.g., STM32, ESP32, ARM Cortex-M) is desirable.

- Excellent problem-solving, communication, and stakeholder .