Software Senior Engineer

Job description

Software Senior Engineer, Software Engineering AI Innovation Lab

In the rapidly evolving Artificial Intelligence era, few domains are advancing faster than the AI-enabled PC. As a leading provider of world-class technology, Dell Technologies is driving intelligence deeper into the PC ecosystem — delivering superior performance, enhanced productivity, and delightful user experiences while upholding privacy and security.

What you’ll achieve: 

The AI Innovation Lab is CSG's engine of applied AI — delivering end-to-end AI solutions and earning the position as the most secure and reliable place to run AI agents and solutions, from the device in the hand to the private stack behind the perimeter.

We are hiring a Senior Software Engineer to join the AI Innovation Lab and contribute to our portfolio of applied AI initiatives spanning edge AI, agentic solutions, model evaluation, and intelligent workload orchestration.

You will: 

As a Senior Software Engineer on the CSG AI Innovation Lab, you will:

• Design and implement backend services and APIs for scalable AI model and system evaluation platforms, enabling benchmarking across client silicon and inference frameworks.
• Build persona-driven dashboards and evaluation pipelines to assess model performance, latency, and quality across diverse hardware configurations.
• Develop and deploy AI/ML software solutions tailored for client silicon, integrating telemetry and performance metrics.
• Develop and deploy AI/ML software solutions tailored for client silicon, optimizing inference for on-device and edge deployment scenarios.
• Evaluate and integrate inference frameworks, optimize model serving, and explore multi-tier compute architectures spanning CPU, GPU, NPU to high-end AI silicon.
• Build agentic workflows, tools, and agent skills that enable AI-powered automation for internal and external use cases.
• Build and manage containerized applications using Docker and orchestrate deployments with Kubernetes (K8s).
• Automate system-level tasks using shell scripting to streamline development, deployment, and telemetry workflows.

• Apply strong system design and architecture principles to build scalable, low-latency server-side systems.

• Collaborate with AI engineers and data scientists to optimize model lifecycle, telemetry integration, and AI/ML model execution on client devices.

Conduct experiments to train, quantize, and optimize ML/DL models for delivery onto client and edge devices.

Take the first step towards your dream career 

Every Dell Technologies team member brings something unique to the table. Here’s what we are looking for with this role: 

Essential Requirements 

• Bachelor’s degree + 5 years, Master’s + 3 years, or PhD in Computer Science, Engineering, AI/ML, or related fields.
• Strong proficiency in Python, RUST and C++ for backend, apps and performance-critical components.
• Experience with shell scripting for automation and system orchestration.
• Proven expertise in backend service development and API design.
• Hands-on experience with Kubernetes (K8s) and Docker for container orchestration and deployment.
• Solid understanding of Software Development Life Cycle (SDLC) and agile methodologies.
• Experience developing for Microsoft Windows OS, including SDKs and embedded device development in RTOS environments.
• Strong knowledge of embedded systems and their application in client ecosystems.

Desirable Requirements 

• Experience with AI software development tools, such as model quantization frameworks, inference optimization libraries, or agentic AI platforms.
• Familiarity with development in real-time embedded environments and major hardware vendor toolchains (e.g., Intel OpenVino, Qualcomm QNN, NVIDIA CUDA, AMD Ryzen AI Software).
• Prior work on model evaluation frameworks or benchmarking systems for edge AI.
• Experience with LLM inference serving frameworks (e.g., vLLM, Ollama, SGLang) for local and edge AI deployment.
• Exposure to agentic AI workflows, tool-use patterns, or agent harness development.
• Familiarity with hybrid AI orchestration — routing workloads across device, edge, and cloud tiers.