Staff AI Systems Engineer

OMNIVISION; Singapore; On-site; SGD 180,000 - 280,000; Full time

Job summary

OMNIVISION seeks a Staff AI Systems Engineer to translate AI product requirements into deployable AI solutions and drive HW/SW co-design across ML algorithms, software frameworks, compiler support, system architecture, and silicon capabilities.

You will evaluate AI workloads, study model optimization techniques, and validate architectural concepts through simulation and FPGA prototyping, collaborating with IC Design, Software, and System Engineering teams.

Qualifications

• Master's or PhD in Computer Engineering, Electrical Engineering, Computer Science, Machine Learning, or related discipline.
• 8+ years of experience in AI systems, computer architecture, machine learning deployment, semiconductor development, or related technical fields.
• Familiarity with Verilog, SystemVerilog, architecture simulation, performance modeling, and design space exploration.
• Experience with MLIR, TVM, ONNX Runtime, TensorRT, OpenVINO, or similar deployment frameworks.
• Experience with computer vision, imaging pipelines, AI-enabled sensor systems, or in-sensor / near-sensor AI applications.
• Familiarity with RISC-V Vector, SIMD architectures, or vector computing concepts.
• Publications, patents, or open-source contributions in AI systems, computer architecture, machine learning optimization, or semiconductor technologies.
• Experience leading cross-functional technical initiatives involving software, systems, and silicon teams.

Responsibilities

• Translate AI product requirements and architectural vision into deployable AI solutions.
• Drive hardware/software co-design across ML algorithms, software frameworks, compiler support, system architecture, and silicon capabilities.
• Evaluate AI workloads, perform roofline and performance bottleneck analysis, and study model optimization techniques.
• Collaborate with IC Design, Software Engineering, and System Engineering to validate concepts via simulation and FPGA prototyping.

Skills

Python

C/C++

Analytical thinking

Cross-functional communication

Education

Master's or PhD in Computer Engineering, Electrical Engineering, Computer Science, Machine Learning, or related

Tools

MLIR

TVM

ONNX Runtime

TensorRT

OpenVINO

Job description

Our mission is to enable next-generation AI capabilities through tightly integrated hardware and software innovation across imaging and vision systems. We believe meaningful advances in Edge AI require optimization across the entire technology stack, from machine learning algorithms and deployment toolchains to processor architecture and silicon implementation.

The AI Architecture team serves as the bridge between Machine Learning, Software Engineering, System Engineering, and IC Design. The team is responsible for evaluating emerging AI workloads, defining future AI compute capabilities, and driving hardware/software co-design for in-sensor and near-sensor AI deployment.

We work closely with IC Design, System Engineering, and Software Engineering teams to ensure future AI solutions are optimized across the entire stack, balancing model accuracy, performance, memory efficiency, power consumption, and hardware cost.

About the Role

Reporting directly to the AI Architect, the Staff AI Systems Engineer will serve as a key technical partner in translating AI product requirements and architectural vision into deployable AI solutions.

Working closely with the AI Architecture team, you will drive hardware/software co-design across machine learning algorithms, software frameworks, compiler support, system architecture, and silicon capabilities. You will evaluate emerging AI workloads, perform roofline and performance bottleneck analysis, study model optimization techniques such as quantization, sparsity, pruning, and compression, and identify opportunities to improve AI deployment efficiency on future hardware platforms.

You will collaborate extensively with IC Design, Software Engineering, and System Engineering teams to assess architectural trade-offs, influence future hardware capabilities, develop performance models and benchmarks, and validate architectural concepts through simulation and FPGA-based prototyping.

You might thrive in this role if you have strong experience in AI systems, computer architecture, machine learning deployment, or semiconductor development; possess a deep understanding of AI accelerators, NPUs, SIMD or vector architectures; are familiar with neural network topologies and their deployment optimizations; and have hands‑on experience with performance analysis, model compression techniques, compiler‑aware optimization, FPGA prototyping, or RTL collaboration. Strong Python and C/C++ programming skills, analytical problem‑solving abilities, and excellent cross‑functional communication skills are essential.

Preferred Qualifications & Ideal Candidate Profile

• Master's or PhD in Computer Engineering, Electrical Engineering, Computer Science, Machine Learning, or a related discipline.
• 8+ years of experience in AI systems, computer architecture, machine learning deployment, semiconductor development, or related technical fields.
• Familiarity with Verilog, SystemVerilog, architecture simulation, performance modeling, and design space exploration.
• Experience with MLIR, TVM, ONNX Runtime, TensorRT, OpenVINO, or similar deployment frameworks.
• Experience with computer vision, imaging pipelines, AI-enabled sensor systems, or in-sensor / near-sensor AI applications.
• Familiarity with RISC-V Vector, SIMD architectures, or vector computing concepts.
• Publications, patents, or open-source contributions in AI systems, computer architecture, machine learning optimization, or semiconductor technologies.
• Experience leading cross-functional technical initiatives involving software, systems, and silicon teams.

The ideal candidate is a system-level engineer who can bridge machine learning algorithms, software infrastructure, system architecture, and silicon implementation. This individual works comfortably across AI system architecture, NPU architecture, HW/SW co-design, machine learning optimization, compiler‑aware deployment, performance analysis, FPGA prototyping, and RTL collaboration while building strong partnerships with AI Architecture, IC Design, Software Engineering, and System Engineering teams to deliver production‑ready AI solutions.