About Us

Qualcomm is enabling a world where everyone and everything can be intelligently connected. You interact with products and technologies made possible by Qualcomm every day, including intelligent edge devices, next-generation computing platforms, and advanced AI solutions. Qualcomm’s leadership in AI, high‑performance compute, and connectivity is driving innovation across cloud, edge, and data center environments-delivering scalable, power‑efficient platforms that power the next generation of intelligent infrastructure.

About the role

We are seeking a Staff Solutions Engineerto supportexecuting theportfolio of edge initiatives by translating customer opportunities into shippable solutions-from solution definition to demo/pilot execution and into commercial deployment readiness. The ideal candidate is a self-starter who can operate independently, lead technical execution with minimal supervision, and serve as a trusted technical partner to sales, customers, and internal engineering teams

This role spans the full lifecycle: pre‑sales solutioning, sales enablement, and post‑sales technical success, with direct accountability for technical outcomes in demos,PoCs, pilots, and scaled rollouts across edge initiatives such as edge AI devices and edge infrastructure use cases

You should have strong expertise in AI models, quantization, performance optimization, and deployment, along with the ability to define architecture, size workloads, and design systems. They should also have experience developing deep learning models across hardware platforms, strong programming skills, the ability to work effectively with cross-functional teams, and proficiency in machine learning frameworks, Linux, and container orchestration tools.

What You’ll Do

Solutioning & Technical Business Development (Pre‑Sales)

• Lead discovery withinternalstakeholders and end customers to capture requirements, define success criteria, and shape opportunities into a clear solution scope, architecture, and execution plan.

• Create solution blueprints that integrate edge compute + AI workloads + connectivity, including feasibility assessments and “fit-to-platform” recommendations.

• Build and maintain reusable technical assets (reference architectures, demo scripts, BOM guidance, integration patterns) that accelerate field execution and repeatability.

• Partner with internal engineering/product teams to translate customer needs into actionable technical requirements, while providing structured feedback based on field learnings.

Sales Support & Deal Acceleration (Sales Cycle)

• Support sales by leading technical engagements: architecture reviews, technical workshops, competitive positioning discussions, and stakeholder presentations,ensuring technical clarity and risk closure.

• Own technical responses for customer-facing deliverables (e.g., technical proposals, solution narratives, pilot plans), ensuring alignment to commercialization goals and operationalrequirements

• Coordinate cross-functional execution (BD, engineering, PMO, partners) to keep pilots and deal-critical milestones on-track and unblock dependencies.

Demo / Pilot Leadership (PoC → Pilot)

• Design, build, and lead end-to-end demos and pilots, including environment setup, integration, performance validation, and customer walkthroughs

• Own pilot execution governance: scope control, success criteria, issue triage, and closure readiness-driving toward a decision point for scale.

• Enable customer and partner teams via structured technical training and practical handover artifacts.

Commercial Deployments & Post‑Sales Technical Success

• Support transition from pilot to production by defining deployment prerequisites (security, monitoring, lifecycle ops, integration hardening) and ensuring “production-ready” technical acceptance.

• Act as the technical escalation point during early deployments, driving issue resolution across internal teams and partners, and maintaining customer confidence through clear communication.

• Establish repeatable rollout playbooks (deployment patterns, validation checklists, operational handover templates) to support scale across multiple opportunities.

Required Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience).

• 10+ years in customer-facing engineering, solutions engineering, field applications, systems engineering, or technical BD roles spanning the full cycle(discovery → PoC/pilot → deployment/scale)

• Proven ability to build and support AI embeddededge deployments,includingecosystem integration experience across customer engineering, internal business units, and partners to drive aligned, end-to-end execution.

• Hands-on experiencedeliveringproducts/solutions in an applied setting (platform bring-up,systemintegration, performance validation, deployment support, and field issue resolution)

• Strong understanding of edge/IoT solution stacks: device + OS + middleware + connectivity + cloud/edge integration + security considerations.

• Strong experience with AI frameworks such asPyTorchand TensorFlow, including deploying and validating AI/ML and computer vision workloads on edge devices, with hands-on performance tuning, quality troubleshooting, and field validation.

• Experiencedeploying and optimizing inference on hardware accelerators(CPU/GPU/ASICNPU),with understanding ofinference systems and optimizationforedge AIand/or Data Center platforms

• Excellent C/C++/Python(or equivalent)programming and software design skills, including debugging,profiling,and performance analysis.

• Solid understanding of inference hardware constraintsandsystem-level performance bottlenecks(memory bandwidth, latency/throughput tradeoffs, thermal/power constraints).

• Hands-on expertise with Linux-based systems, low level software, drivers, and systems bring up.

• Experience developing/deploying Linux solutions using containers and orchestration (e.g., Docker/Kubernetes), plus source code/config management (Git).

• Excellent communication skills: ability to translate complex technical details into clear customer-facing narratives and executive-ready updates.

• Ability to serve as the in-timezone, on-the-ground technical owner across multiple concurrent programs, driving structured issue triage, escalations, andreducing delivery risk through tightcross-team alignment

Preferred Qualifications

• Experience enabling, deploying, or supporting solutions on Qualcomm SoC-based systems (IoT/edge/compute) and translatingcustomer requirements into actionable platform execution.

• Exposure to Qualcomm compute ecosystems (e.g., Snapdragon-class platforms), including subsystem-level understanding (CPU/GPU/NPU/modem/connectivity) and customer enablement patterns.

• Familiarity withQualcomm accelerated inference platforms (e.gAI100) or comparable data centeredge inference accelerators, with strong end-to-end architectureunderstandingacross edge-to-core environments.

• Understanding of wireless connectivity considerations for edge deployments, including cellular communication technologies andfiledintegration dependencies

Why Join Qualcomm

At Qualcomm, you’ll work at the intersection of AI silicon, system architecture, and real‑world deployment. You will engage directly with strategic customers, influence next‑generation AI data center platforms, and help define scalable, power‑efficient infrastructure for the AI era. This role provides a unique opportunity to shape both technology direction and customer outcomes, while working with world‑class engineering and product teams.