New opportunity

Lead Engineer, Senior - Computer Vision

Qualcomm India Private Limited · Bengaluru, India

About this role

The Lead Engineer, Senior - Computer Vision will join the Multimedia Systems Team in Bengaluru to develop embedded camera and imaging solutions. The role focuses on designing algorithms that enable devices to perceive and understand their environment. Responsibilities include developing cutting-edge computer vision algorithms for analytical tasks, creating fixed-point implementations and C/C++ models for hardware-destined image processing modules, and optimizing algorithm complexity to balance quality and performance. Candidates must possess deep knowledge of signal processing, computer vision, and deep learning, along with strong command of C/C++, data structures, and fixed-point implementation. Experience working across Windows and Linux platforms is required. Preferred qualifications include a background in digital signal processing, statistics, data analysis, image processing algorithms, color science, ISP pipelines, and deep learning training methodologies. Minimum qualifications vary by degree: Bachelor's degree with 4+ years of experience, Master's degree with 3+ years of experience, or PhD with 2+ years of experience in Engineering, Information Systems, Computer Science, or a related field.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Computer VisionDeep LearningSignal ProcessingC++CFixed-point implementationData structuresMATLABPythonDigital signal processingStatisticsData analysisImage processingColor scienceISP pipelines

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Embedded Hardware-Algorithm Co-design
The role requires translating high-level computer vision algorithms into fixed-point C/C++ models suitable for hardware implementation. Success depends on your ability to bridge the gap between theoretical algorithm performance and the constraints of embedded silicon.
Performance and Quality Optimization
You must balance algorithm complexity against strict performance requirements. This involves optimizing code and models to ensure real-time execution on embedded systems while maintaining high-quality image processing results.
Deep Learning and Signal Processing Integration
The position demands a strong foundation in both deep learning methodologies and traditional signal processing. You will be expected to apply these to analytical tasks that enable devices to perceive and interpret their environment.

Likely challenges to think through

Fixed-Point Precision Constraints
Transitioning from floating-point research models to fixed-point hardware implementations often introduces precision loss. Prepare by reviewing techniques for quantization and error analysis to ensure your models remain robust when deployed on hardware.
Hardware-Software Trade-off Analysis
You will face the difficulty of optimizing for hardware that has limited memory and compute resources. Prepare by being ready to discuss how you make architectural trade-offs between algorithm accuracy and computational latency.

Practice your answers

Technical practice

Fixed-point implementation challenges

What to coverExplain the process of converting floating-point computer vision algorithms to fixed-point for hardware deployment. Discuss how you manage quantization errors, dynamic range limitations, and precision loss while maintaining algorithm accuracy.

Balancing algorithm quality and performance

What to coverDescribe your approach to optimizing computer vision models for embedded systems where computational resources are constrained. Suggest strategies for reducing latency and memory footprint without significantly degrading the output quality.

C/C++ for hardware-destined modules

What to coverDiscuss best practices for writing C/C++ code intended for hardware-destined image processing modules. Focus on memory management, cache efficiency, and ensuring code is suitable for synthesis or integration into an ISP pipeline.

Deep learning training and evaluation

What to coverDetail your methodology for training and evaluating deep learning models for computer vision tasks. Address how you handle dataset bias, validation strategies, and metrics used to ensure robustness in real-world analytical scenarios.

Signal processing in computer vision

What to coverExplain how fundamental signal processing concepts are applied to modern computer vision tasks. Provide examples of how filtering, frequency analysis, or noise reduction techniques improve the performance of image processing algorithms.

ISP pipeline architecture

What to coverDescribe the typical stages of an Image Signal Processor (ISP) pipeline and how computer vision algorithms interact with these stages. Suggest how you would optimize an algorithm to leverage existing ISP hardware blocks.

Data structures for real-time processing

What to coverDiscuss the selection of data structures when implementing computer vision algorithms for real-time embedded systems. Explain how your choices impact data access patterns and overall system throughput.

Color science and image processing

What to coverExplain the importance of color science in camera and imaging solutions. Discuss how color space conversions and white balance algorithms influence the performance of downstream computer vision analytical tasks.

Behavioural practice

Collaborating in a cross-functional R&D team

What to coverDescribe a situation where you had to collaborate with hardware or systems engineers to integrate a software algorithm. Focus on how you communicated technical constraints and reached a consensus on design trade-offs.

Handling technical ambiguity in R&D

What to coverProvide an example of a project where the requirements for an imaging solution were initially unclear. Explain how you defined the scope, evaluated potential technical paths, and drove the project toward a successful implementation.

Managing conflicting performance requirements

What to coverDescribe a time when you had to balance competing demands, such as high accuracy versus strict power or latency constraints. Detail the steps you took to analyze the trade-offs and the final decision-making process.

Adapting to new technical environments

What to coverDiscuss your experience working across different platforms like Windows and Linux. Share an example of how you successfully migrated or adapted a complex codebase to a new environment while maintaining system stability.

Sharpen your knowledge

Topics to prepare

Fixed-Point Arithmetic and Hardware Modeling
The role requires converting floating-point algorithms into fixed-point implementations for hardware-destined modules. Exercise: Take a standard computer vision algorithm (e.g., Sobel edge detection) and implement it using fixed-point arithmetic in C++, ensuring you manage quantization errors and overflow while maintaining performance. This is critical for the role's focus on embedded camera solutions.
ISP Pipeline Architecture
Understanding the Image Signal Processor (ISP) pipeline is essential for embedded imaging. Exercise: Review the stages of a typical ISP pipeline (demosaicing, noise reduction, color correction, tone mapping). Research how these stages interact with deep learning models to optimize image quality. This is relevant to the role's focus on designing algorithms for mobile multimedia devices.

Skills to revise

C/C++ Optimization for Embedded Systems
The role demands strong command of C/C++ and algorithm optimization. Exercise: Profile a C++ computer vision function to identify bottlenecks. Refactor the code using SIMD instructions or cache-friendly data structures to improve execution speed. This directly addresses the requirement to balance algorithm quality and performance.
Deep Learning Training and Evaluation
The position involves developing algorithms for analytical tasks. Exercise: Implement a training pipeline for a lightweight computer vision model (e.g., MobileNet) and evaluate its performance using metrics like mAP or inference latency. Focus on methodologies that ensure the model is suitable for deployment on embedded hardware.
Digital Signal Processing (DSP) Fundamentals
DSP is a core requirement for the Multimedia Systems Team. Exercise: Apply Fourier transforms or filtering techniques to image data to demonstrate noise reduction or feature extraction. This is relevant to the role's focus on signal processing and image processing algorithms.

Your preparation checklist

Prepare a portfolio of fixed-point implementation projects
Create a technical summary or code samples demonstrating your ability to convert floating-point computer vision algorithms into fixed-point C/C++ models suitable for hardware deployment, highlighting your approach to managing precision and performance trade-offs.
Review ISP pipeline and color science fundamentals
Refresh your knowledge on Image Signal Processor (ISP) architectures and color science, as the role involves developing imaging solutions that require understanding how raw sensor data is processed into high-quality images.
Develop a performance optimization case study
Prepare a walkthrough of a past project where you balanced algorithm quality against computational complexity, specifically detailing how you optimized code for embedded systems or hardware-destined modules.
Synthesize deep learning and signal processing expertise
Be ready to discuss how you integrate deep learning training methodologies with traditional digital signal processing techniques to solve real-world analytical computer vision tasks.
Formulate targeted questions for the Multimedia Systems team
Prepare questions regarding the team's current hardware constraints, the specific types of analytical computer vision tasks they are prioritizing, and how the R&D cycle integrates with hardware design teams at Qualcomm.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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