Edge AI Researcher (Speech & Audio Models)

Computer Futures; India; On-site; INR 6,000,000 - 9,000,000; Full time

Job summary

Computer Futures seeks an Edge AI Research Scientist to develop next-generation speech and audio AI systems that run efficiently on smartphones, wearables and other resource-constrained devices. You will design compact model architectures and optimize inference pipelines for real-time experiences on end-user devices.

The role sits at the intersection of ML research, systems optimization, and production engineering, requiring hands-on implementation and collaboration with mobile engineers and ML

Qualifications

• Master's degree, Ph.D., or equivalent in Computer Science or related field.
• Strong track record in efficient deep learning, model compression, or edge AI.
• Familiar with hardware-aware optimization and deployment to resource-constrained devices.

Responsibilities

• Drive research in efficient machine learning and edge AI for speech and audio applications.
• Design compact models for strict latency, memory, and power constraints.
• Develop and improve state-of-the-art approaches for compression and hardware-aware architectures.
• Build optimized inference pipelines for mobile and embedded hardware.
• Collaborate with ML researchers, mobile engineers, and systems engineers to productionize research.

Skills

Efficient Deep Learning

Model compression

Edge AI

Hardware-aware optimization

Software engineering

Education

Master's degree or PhD in Computer Science

Tools

PyTorch

JAX

C/C++

ONNX Runtime

TensorRT

Core ML

ExecuTorch

TensorFlow Lite

CUDA

Vulkan

XNNPACK

QNN

Job description

We are seeking an Edge AI Research Scientist to develop next-generation speech and audio AI systems that run efficiently on smartphones, wearables, and other resource-constrained devices.

This role sits at the intersection of machine learning research, systems optimization, and production engineering. You will design compact model architectures, develop advanced compression techniques, and optimize inference pipelines that enable real-time speech AI experiences directly on end-user devices.

You will work across a broad range of voice technologies, including automatic speech recognition (ASR), text-to-speech (TTS), speech translation, speech-to-speech systems, and neural audio codecs.

The ideal candidate combines strong research credentials with hands‑on implementation skills and has a deep understanding of efficient deep learning, model optimization, and hardware‑aware machine learning.

What You’ll Do

Research and Model Development

• Drive research in efficient machine learning and edge AI for speech and audio applications.


• Design compact model architectures capable of operating under strict latency, memory, and power constraints.


• Develop and improve state‑of‑the‑art approaches for:


• Low‑rank adaptation and compression


• Hardware‑aware architectures


• Efficient training and inference techniques


• Contribute to speech‑to‑speech, speech recognition, speech translation, text‑to‑speech, and audio generation systems.

Inference Optimization

• Build highly optimized inference pipelines for mobile and embedded hardware.


• Improve performance across CPUs, GPUs, NPUs, and other acceleration hardware.


• Optimize:


• Operator execution


• Scheduling strategies


• Caching mechanisms


• End‑to‑end system latency


• Integrate models with production runtimes and deployment frameworks.

Performance Evaluation

• Develop rigorous benchmarking methodologies for edge AI systems.


• Measure and improve:


• Real‑time factor


• Time‑to‑first‑audio


• Thermal behavior


• Speech quality and accuracy


• Validate performance directly on target devices rather than relying solely on simulator environments.

Cross‑Functional Collaboration

• Partner with machine learning researchers, mobile engineers, and systems engineers to bring research into production.


• Translate research prototypes into scalable products and customer‑facing technologies.


• Communicate findings through internal documentation, technical publications, conference papers, and open‑source contributions where appropriate.

Required Qualifications

• Master's degree, Ph.D., or equivalent industry experience in:


• Computer Science


• Efficient Deep Learning


• Demonstrated expertise in model compression, efficient inference, or edge AI through research publications, production systems, or both.


• Strong understanding of one or more of the following:


• Hardware‑aware optimization


• Strong software engineering skills with:


• PyTorch or JAX


• C/C++ or equivalent systems‑level programming experience


• Experience optimizing neural networks for resource‑constrained hardware.


• Practical knowledge of:


• GPUs


• NPUs


• Memory systems


• Numerical precision tradeoffs


• Ability to make informed tradeoffs between model quality, latency, memory footprint, power consumption, and deployment portability.


• Professional proficiency in English.


• Ability to thrive in a fast‑moving, research‑driven environment.

Preferred Qualifications

• Experience working with:


• Automatic Speech Recognition (ASR)


• Speech‑to‑Speech Models


• Familiarity with deployment frameworks such as:


• Core ML


• ExecuTorch


• ONNX Runtime


• LiteRT / TensorFlow Lite


• TensorRT


• Experience with acceleration technologies including:


• Metal


• Vulkan


• CUDA


• QNN


• XNNPACK


• Custom kernels and operator fusion


• Knowledge of:


• Experience building streaming and low‑latency audio systems.


• Experience deploying machine learning models to:


• Embedded systems


• Experience training, distilling, or evaluating large‑scale foundation models using distributed GPU infrastructure.


• Previous experience in industrial research labs, AI startups, or leading technology companies.


• Publication record at relevant conferences or meaningful open‑source contributions