Senior Silicon System and Software Integration Engineer

SPD Technology

. Drive custom Machine Learning Accelerator (ASIC) technology for large-scale AI applications and global cloud infrastructure .

Posted 10/15/2026full-timeRemote • United Kingdom, Colombia, Israel, Netherlands, France, GermanySeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Machine Learning Accelerator technology, focusing on hardware-software integration and validation for AI applications. Proficient in developing validation firmware and executing hardware-software co-simulation to ensure optimal performance and functionality.

Highest-signal resume keywords

Machine Learning Accelerator TechnologyHardware-Software IntegrationRTL DevelopmentC++/Python Software DesignFirmware Development for Embedded Systems

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning Accelerator TechnologyHardware-Software IntegrationRTL DevelopmentDesign VerificationFirmware DevelopmentDebugging FirmwarePerformance ModelingValidation TestingMicrobenchmarkingCross-Functional Collaboration

Soft Skills

Problem-SolvingCross-Functional LeadershipCommunication

Tools & Technologies

Architectural SimulatorsFPGA EnvironmentsSimulation ToolsReal-Time Operating Systems

Certifications & Qualifications

Bachelor's Degree in Electrical EngineeringBachelor's Degree in Computer EngineeringBachelor's Degree in Computer Science

Industry Keywords

ASIC DesignDigital Design EvaluationAI/ML-Driven SystemsIntegration of Third-Party IPsValidation Coverage Improvement

Tech Stack

Tools & technologies

CloudPythonRTOSC++

About the role

Key responsibilities & impact

Drive custom Machine Learning Accelerator (ASIC) technology for large-scale AI applications and global cloud infrastructure
Evaluate complex digital designs focused on ML compute architecture and integration within AI/ML-driven systems
Own cross-functional workstreams for end-to-end hardware-software integration and validation
Bridge specification, design, verification, compiler, and performance teams across the ML compute IP ecosystem
Review chip specifications and designs
Develop integration plans with software and system partners and coordinate hardware/software delivery
Lead bringup of ML compute features and integrate and validate complex hardware/software designs, including third-party IPs
Develop validation firmware to verify hardware functionality
Execute hardware-software co-simulation using RTL, emulation, and FPGA environments
Use architectural simulators and performance models to correlate results
Design validation tests and microbenchmarks to verify IP functionality and performance
Author test plans with Design, Design Verification, Firmware, and Architecture teams
Lead debugging with Design, DV, Software, and Architecture teams
Root-cause functional failures and performance issues throughout the product development cycle
Improve validation coverage and sign-off processes for tapeout and production deployment

Requirements

What you’ll need

Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related technical field, or equivalent practical experience
5 years of experience in two of the following: computer architecture, embedded firmware, ASIC design or verification, or integration and enablement of first/third party IPs
Experience in hardware/software integration and validation
Experience with register-transfer level (RTL) development, design verification, and evaluation
Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture (bonus)
4 years of experience with C++/Python software design principles (bonus)
Experience integrating complex hardware/software systems in accelerators (bonus)
Experience developing firmware for embedded systems or accelerators (bonus)
Experience debugging firmware using simulation tools (bonus)
Knowledge of real-time operating system (RTOS) internals (bonus)

Benefits

Comp & perks

Fully remote work around the globe
Hybrid work model from office hubs
Flexible working schedule
Stable workload and stable income
Performance reviews and career path
Mentorship program
Personal development plan
Individual learnings through the corporate library
Public speaking support
Company-wide tech and cultural events
Participation in meaningful CSR initiatives
Supportive and flexible work environment
Collaboration, open communication and mutual respect