AS
Embedded Firmware Principal Architect
Accepting applicationsAmbient Scientific · Bengaluru, Karnataka, India
Full-Time Mid_senior AIARMASICC++I2C
Estimated market salary
₹28-50 LPA
This is a SiliconBoard market estimate, not an employer-posted salary.
Posted
1d ago
Category
Design
Experience
Mid_senior
Country
India
JOB DESCRIPTION
Embedded Firmware Principal Architect
Embedded Systems Division
Department :- Embedded Software Engineering
Reports To :- VP of Engineering / Chief Architect
Location :- On-site / Hybrid
Level :- Principal / Staff-Level (Individual Contributor)
Role Overview
We are seeking an exceptional Embedded Firmware Principal Architect to serve as the top individual-contributor technical authority for our organization. This role demands a rare combination of technical depth, architectural vision, and AI/ML knowledge to define the technical direction for embedded software and edge intelligence systems, working hands-on and in close partnership with embedded software developers, validation engineers, and data scientists delivering mission-critical embedded and edge intelligence software solutions for Ambient Scientific AI SoCs.
This is a high-impact individual-contributor position for a technologist who thrives at the intersection of embedded engineering excellence and edge AI innovation. You will set the technical direction, drive architectural decisions, guide the development of data science models deployed on resource-constrained edge hardware, and own deep hands-on delivery of embedded software across the full development lifecycle — influencing engineering teams through technical authority and mentorship rather than formal management.
💻 Embedded Software :- Firmware, BSP, drivers, RTOS, middleware, application software
✅ Validation & Quality :- SW validation, test automation, quality assurance
🧠 Data Science :- ML/AI model development, edge deployment, sensor fusion, data pipelines
Key Responsibilities
Technical Strategy & Direction
● Act as the top technical authority for embedded software and edge AI, owning the technical roadmap and engineering vision in partnership with business objectives.
● Partner with executive leadership (VP Engineering, CTO, CEO) to shape the multi-year software and data science technical strategy for the embedded systems division.
● Drive cross-functional technical alignment with Hardware, Systems Engineering, Product Management, and Program Management teams.
● Build and communicate a unified technical vision where embedded software and edge AI/ML capabilities are co-designed for optimal real-world performance.
● Champion the adoption of AI-assisted tools for quick, high-quality software development, automated unit testing, and full software validation.
Software Architecture & Technical Governance
● Serve as the primary Software Architect, defining and governing embedded system software architectures including BSP, middleware, RTOS, device drivers, and application layers.
● Lead architecture design reviews, establish design patterns, coding standards, and ensure architectural integrity across all software subsystems.
● Make critical technical decisions on platforms, toolchains, SDKs, and RTOS selection (FreeRTOS, Zephyr).
● Define the edge AI/ML software architecture: model inference pipelines, sensor data ingestion layers, pre/post-processing frameworks, and on-device model lifecycle management.
● Champion software reusability, modularity, and scalability; drive adoption of model-based design and component-based architectures where applicable.
● Own the software architecture documentation and ensure adherence to relevant standards.
Hands-On Software Development
● Remain deeply engaged in software development — writing, reviewing, and debugging embedded C/C++ and Python code as the primary technical contributor on critical path items.
● Conduct deep technical reviews of system-level software: bootloaders, firmware update mechanisms, memory management, interrupt handling, power management, and real-time scheduling.
● Lead proof-of-concept development for new hardware bring-ups, new SoC/MCU/NPU platforms, and emerging edge AI technology integrations.
● Personally drive root-cause analysis on high-severity software defects, model inference failures, system-level integration issues, and field escalations.
Data Science & Edge AI Technical Leadership
● Provide technical leadership to the Data Science Engineering function on developing, optimizing, and deploying ML/AI models targeting edge and embedded hardware.
● Define the end-to-end edge AI development workflow: data collection and labeling pipelines, model training infrastructure, quantization and pruning strategies, deployment packaging, and on-device validation.
● Guide data scientists in selecting appropriate model architectures (CNNs, RNNs, Transformers, GNNs, classical ML) optimized for inference on Ambient Scientific existing and upcoming SoCs.
● Oversee model compression techniques — quantization (INT8/INT4), pruning, knowledge distillation, and neural architecture search — to meet embedded memory, latency, and power constraints.
● Champion edge inference frameworks (like TensorFlow Lite, STM32Cube.AI) for Ambient Scientific edge AI SoCs.
● Drive sensor fusion model development integrating data from IMU, radar, camera, temperature, acoustic, and other embedded sensor modalities.
● Establish model performance benchmarking standards: accuracy, latency, memory footprint, energy consumption, and robustness under real-world operating conditions.
● Ensure data science work is tightly integrated with the embedded software team — co-designing data interfaces, inference APIs, and real-time triggering mechanisms.
Technical Mentorship & Cross-Disciplinary Influence
● Mentor senior, staff, and principal engineers and data scientists through design reviews, architecture discussions, and hands-on pairing — influencing through technical authority rather than formal reporting lines.
● Bridge the cultural and technical gap between embedded engineers and data scientists.
● Act as a technical role model, raising the engineering bar through example, code review, and design leadership.
Software Validation & Quality Engineering
● Define embedded software validation strategy: test plans, coverage requirements, and validation methodologies (unit, integration, HIL, SIL, regression).
● Extend validation practices to cover ML model validation: dataset quality audits, model robustness testing, edge-case coverage, adversarial testing, and on-device accuracy benchmarking.
● Champion shift-left testing practices, integrating validation early in the development process.
● Drive adoption of automated testing frameworks, CI/CD pipelines for embedded targets and ML models, and static analysis tooling.
Process, Tools & Delivery
● Define and continuously improve development processes aligned with Agile across embedded, validation, and data science workstreams.
● Own the software development environment: version control strategy (Git/DVC), branching models, build systems (CMake, Make, Yocto), MLOps pipelines, and release management.
● Drive technical risk management: proactively identify risks across firmware, AI model performance, and data pipeline reliability; develop mitigation plans and communicate to stakeholders.
● Report technical status, architectural milestones, and quality metrics to senior leadership with clarity and transparency.
Required Qualifications
Education
● Bachelor's degree in Computer Engineering, Electrical Engineering, Computer Science, Data Science, or related technical field.
● Master's degree or Ph.D. in a relevant field preferred.
Experience
● 12+ years of progressive embedded software or software engineering experience, with demonstrated experience operating at a principal/staff architect level.
● Demonstrated experience acting as a Software Architect or principal-level technical authority, even if not in title.
● Proven hands-on technical contributor: ability to read, write, and review production-quality embedded C/C++ code and ML/AI development artifacts.
● Experience technically guiding or closely collaborating with engineers across software development, validation, and/or data science functions.
● At least 2 years of direct technical collaboration with a data science or ML engineering team.
Embedded Software Technical Expertise
● Languages: Embedded C/C++, Python
● RTOS / OS: FreeRTOS, Zephyr, or equivalent production RTOS experience
● Processors: MCU/MPU/NPU architectures: ARM Cortex-M/A, RISC-V, PowerPC, or similar
● Protocols: SPI, I2C, UART, USB, and wireless protocols
● Tools & DevOps: Git, CMake/Make, Yocto/Buildroot, Jenkins/GitLab CI, Jira
● Debug & Validation: JTAG/trace debugging, oscilloscopes, logic analyzers, hardware bring-up
Data Science & Edge AI Technical Expertise
● Languages: Python (NumPy, Pandas, Scikit-learn), C/C++ inference integration
● ML Frameworks: TensorFlow/Keras, PyTorch, ONNX — training and export workflows
● Edge Inference: TensorFlow Lite / TFLite Micro, ONNX Runtime, TVM, Edge Impulse
● Model Optimization: INT8/INT4 post-training quantization, QAT, weight pruning, knowledge distillation
● Model Architectures: CNNs, RNNs/LSTMs, Transformers, autoencoders, classical ML (SVM, decision trees, ensembles)
● Application Domains: Time-series analysis, anomaly detection, predictive maintenance, sensor fusion, computer vision on edge
● MLOps: MLflow, DVC, Weights & Biases, or equivalent MLOps tooling
● Data Engineering: Dataset curation, labeling pipelines, data augmentation, train/val/test split strategies
Preferred Qualifications
● Experience in wearable devices, edge AI enabled commercial products, or defense embedded systems.
● Hands-on experience deploying neural networks on AI accelerators: Coral Edge TPU, NXP i.MX RT, STM32H7, Qualcomm AI Engine, NVIDIA Jetson, Renesas DRP-AI, or similar.
● Experience with hardware-software co-design and early-stage SoC/ASIC/NPU bring-up.
● Exposure to cybersecurity in embedded systems (secure boot, firmware signing, TrustZone) and model IP protection.
● Published work, patents, or conference presentations in embedded systems or edge AI.
Interested candidates can share their applications or reach out to:
📧 [email protected], 📧 [email protected]
Show more Show less
Embedded Firmware Principal Architect
Embedded Systems Division
Department :- Embedded Software Engineering
Reports To :- VP of Engineering / Chief Architect
Location :- On-site / Hybrid
Level :- Principal / Staff-Level (Individual Contributor)
Role Overview
We are seeking an exceptional Embedded Firmware Principal Architect to serve as the top individual-contributor technical authority for our organization. This role demands a rare combination of technical depth, architectural vision, and AI/ML knowledge to define the technical direction for embedded software and edge intelligence systems, working hands-on and in close partnership with embedded software developers, validation engineers, and data scientists delivering mission-critical embedded and edge intelligence software solutions for Ambient Scientific AI SoCs.
This is a high-impact individual-contributor position for a technologist who thrives at the intersection of embedded engineering excellence and edge AI innovation. You will set the technical direction, drive architectural decisions, guide the development of data science models deployed on resource-constrained edge hardware, and own deep hands-on delivery of embedded software across the full development lifecycle — influencing engineering teams through technical authority and mentorship rather than formal management.
💻 Embedded Software :- Firmware, BSP, drivers, RTOS, middleware, application software
✅ Validation & Quality :- SW validation, test automation, quality assurance
🧠 Data Science :- ML/AI model development, edge deployment, sensor fusion, data pipelines
Key Responsibilities
Technical Strategy & Direction
● Act as the top technical authority for embedded software and edge AI, owning the technical roadmap and engineering vision in partnership with business objectives.
● Partner with executive leadership (VP Engineering, CTO, CEO) to shape the multi-year software and data science technical strategy for the embedded systems division.
● Drive cross-functional technical alignment with Hardware, Systems Engineering, Product Management, and Program Management teams.
● Build and communicate a unified technical vision where embedded software and edge AI/ML capabilities are co-designed for optimal real-world performance.
● Champion the adoption of AI-assisted tools for quick, high-quality software development, automated unit testing, and full software validation.
Software Architecture & Technical Governance
● Serve as the primary Software Architect, defining and governing embedded system software architectures including BSP, middleware, RTOS, device drivers, and application layers.
● Lead architecture design reviews, establish design patterns, coding standards, and ensure architectural integrity across all software subsystems.
● Make critical technical decisions on platforms, toolchains, SDKs, and RTOS selection (FreeRTOS, Zephyr).
● Define the edge AI/ML software architecture: model inference pipelines, sensor data ingestion layers, pre/post-processing frameworks, and on-device model lifecycle management.
● Champion software reusability, modularity, and scalability; drive adoption of model-based design and component-based architectures where applicable.
● Own the software architecture documentation and ensure adherence to relevant standards.
Hands-On Software Development
● Remain deeply engaged in software development — writing, reviewing, and debugging embedded C/C++ and Python code as the primary technical contributor on critical path items.
● Conduct deep technical reviews of system-level software: bootloaders, firmware update mechanisms, memory management, interrupt handling, power management, and real-time scheduling.
● Lead proof-of-concept development for new hardware bring-ups, new SoC/MCU/NPU platforms, and emerging edge AI technology integrations.
● Personally drive root-cause analysis on high-severity software defects, model inference failures, system-level integration issues, and field escalations.
Data Science & Edge AI Technical Leadership
● Provide technical leadership to the Data Science Engineering function on developing, optimizing, and deploying ML/AI models targeting edge and embedded hardware.
● Define the end-to-end edge AI development workflow: data collection and labeling pipelines, model training infrastructure, quantization and pruning strategies, deployment packaging, and on-device validation.
● Guide data scientists in selecting appropriate model architectures (CNNs, RNNs, Transformers, GNNs, classical ML) optimized for inference on Ambient Scientific existing and upcoming SoCs.
● Oversee model compression techniques — quantization (INT8/INT4), pruning, knowledge distillation, and neural architecture search — to meet embedded memory, latency, and power constraints.
● Champion edge inference frameworks (like TensorFlow Lite, STM32Cube.AI) for Ambient Scientific edge AI SoCs.
● Drive sensor fusion model development integrating data from IMU, radar, camera, temperature, acoustic, and other embedded sensor modalities.
● Establish model performance benchmarking standards: accuracy, latency, memory footprint, energy consumption, and robustness under real-world operating conditions.
● Ensure data science work is tightly integrated with the embedded software team — co-designing data interfaces, inference APIs, and real-time triggering mechanisms.
Technical Mentorship & Cross-Disciplinary Influence
● Mentor senior, staff, and principal engineers and data scientists through design reviews, architecture discussions, and hands-on pairing — influencing through technical authority rather than formal reporting lines.
● Bridge the cultural and technical gap between embedded engineers and data scientists.
● Act as a technical role model, raising the engineering bar through example, code review, and design leadership.
Software Validation & Quality Engineering
● Define embedded software validation strategy: test plans, coverage requirements, and validation methodologies (unit, integration, HIL, SIL, regression).
● Extend validation practices to cover ML model validation: dataset quality audits, model robustness testing, edge-case coverage, adversarial testing, and on-device accuracy benchmarking.
● Champion shift-left testing practices, integrating validation early in the development process.
● Drive adoption of automated testing frameworks, CI/CD pipelines for embedded targets and ML models, and static analysis tooling.
Process, Tools & Delivery
● Define and continuously improve development processes aligned with Agile across embedded, validation, and data science workstreams.
● Own the software development environment: version control strategy (Git/DVC), branching models, build systems (CMake, Make, Yocto), MLOps pipelines, and release management.
● Drive technical risk management: proactively identify risks across firmware, AI model performance, and data pipeline reliability; develop mitigation plans and communicate to stakeholders.
● Report technical status, architectural milestones, and quality metrics to senior leadership with clarity and transparency.
Required Qualifications
Education
● Bachelor's degree in Computer Engineering, Electrical Engineering, Computer Science, Data Science, or related technical field.
● Master's degree or Ph.D. in a relevant field preferred.
Experience
● 12+ years of progressive embedded software or software engineering experience, with demonstrated experience operating at a principal/staff architect level.
● Demonstrated experience acting as a Software Architect or principal-level technical authority, even if not in title.
● Proven hands-on technical contributor: ability to read, write, and review production-quality embedded C/C++ code and ML/AI development artifacts.
● Experience technically guiding or closely collaborating with engineers across software development, validation, and/or data science functions.
● At least 2 years of direct technical collaboration with a data science or ML engineering team.
Embedded Software Technical Expertise
● Languages: Embedded C/C++, Python
● RTOS / OS: FreeRTOS, Zephyr, or equivalent production RTOS experience
● Processors: MCU/MPU/NPU architectures: ARM Cortex-M/A, RISC-V, PowerPC, or similar
● Protocols: SPI, I2C, UART, USB, and wireless protocols
● Tools & DevOps: Git, CMake/Make, Yocto/Buildroot, Jenkins/GitLab CI, Jira
● Debug & Validation: JTAG/trace debugging, oscilloscopes, logic analyzers, hardware bring-up
Data Science & Edge AI Technical Expertise
● Languages: Python (NumPy, Pandas, Scikit-learn), C/C++ inference integration
● ML Frameworks: TensorFlow/Keras, PyTorch, ONNX — training and export workflows
● Edge Inference: TensorFlow Lite / TFLite Micro, ONNX Runtime, TVM, Edge Impulse
● Model Optimization: INT8/INT4 post-training quantization, QAT, weight pruning, knowledge distillation
● Model Architectures: CNNs, RNNs/LSTMs, Transformers, autoencoders, classical ML (SVM, decision trees, ensembles)
● Application Domains: Time-series analysis, anomaly detection, predictive maintenance, sensor fusion, computer vision on edge
● MLOps: MLflow, DVC, Weights & Biases, or equivalent MLOps tooling
● Data Engineering: Dataset curation, labeling pipelines, data augmentation, train/val/test split strategies
Preferred Qualifications
● Experience in wearable devices, edge AI enabled commercial products, or defense embedded systems.
● Hands-on experience deploying neural networks on AI accelerators: Coral Edge TPU, NXP i.MX RT, STM32H7, Qualcomm AI Engine, NVIDIA Jetson, Renesas DRP-AI, or similar.
● Experience with hardware-software co-design and early-stage SoC/ASIC/NPU bring-up.
● Exposure to cybersecurity in embedded systems (secure boot, firmware signing, TrustZone) and model IP protection.
● Published work, patents, or conference presentations in embedded systems or edge AI.
Interested candidates can share their applications or reach out to:
📧 [email protected], 📧 [email protected]
Show more Show less