Job Title : ML Benchmarking Engineer

Experience : 3-6 Years

Location : Mumbai

Work Mode : Hybrid

Job Description :

We are seeking a highly motivated ML Benchmarking Engineer with strong expertise in C++, Python, and Machine Learning performance evaluation. The ideal candidate will be responsible for benchmarking, profiling, optimizing, and validating ML workloads across embedded systems, MCUs, DSPs, and edge computing platforms. The role involves close collaboration with AI/ML, firmware, hardware, and software engineering teams to measure and improve the performance of machine learning models on resource-constrained devices.

Key Responsibilities :

- Design, develop, and execute benchmarking frameworks for Machine Learning and AI workloads.

- Evaluate ML model performance across Embedded, MCU, DSP, and Edge AI platforms.

- Develop benchmarking tools and automation scripts using Python and C++.

- Analyze latency, throughput, memory utilization, power consumption, and model accuracy metrics.

- Profile ML workloads and identify performance bottlenecks across hardware and software stacks.

- Collaborate with AI/ML engineers to optimize model deployment and inference performance.

- Validate and compare performance across different processors, accelerators, and embedded architectures.

- Generate benchmark reports, performance dashboards, and optimization recommendations.

- Work closely with firmware, hardware, and platform teams to improve system efficiency.

- Support performance tuning and optimization of embedded AI applications.

- Create technical documentation, test plans, and benchmarking methodologies.

Required Skills :

- 3-6 years of experience in Software Development, Embedded Systems, or Performance Engineering.

1. Strong programming expertise in :

- C++

- Python

2. Hands-on experience in :

- ML Benchmarking

- Performance Analysis

- Profiling Tools

- Performance Optimization

3. Experience working with :

- Embedded Systems

- Microcontrollers (MCU)

- Digital Signal Processors (DSP)

- Edge Computing Platforms

4. Strong understanding of Machine Learning model execution and inference workflows.

5. Experience analyzing performance metrics such as :

- Latency

- Throughput

- Memory Usage

- CPU/GPU Utilization

- Power Consumption

Preferred Skills :

1. Experience with ML frameworks such as :

- TensorFlow

- TensorFlow Lite

- PyTorch

- ONNX Runtime

2. Knowledge of AI accelerators, NPUs, GPUs, and embedded AI chipsets.

3. Experience with Linux-based embedded platforms.

4. Familiarity with ARM architectures and embedded hardware platforms.

5. Exposure to model quantization, optimization, and deployment techniques.

6. Knowledge of CI/CD and automated performance testing frameworks.

Behavioral Skills :

- Strong analytical and problem-solving abilities.

- Excellent debugging and performance tuning skills.

- Good communication and collaboration capabilities.

- Ability to work effectively with cross-functional teams.

- Strong attention to detail and quality-focused mindset.

Qualification :

- Bachelor's or Master's degree in Computer Science, Electronics, Embedded Systems, Electrical Engineering, Artificial Intelligence, or a related field.