Physical AI Ops Tech Lead - Advanced Robotics Team
Listed on 2026-03-01
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IT/Tech
Robotics, AI Engineer, Systems Engineer
Company:Qualcomm Technologies, Inc.
Job Area:Engineering Group, Engineering Group >
Machine Learning Engineering
General
Summary:
**** Hiring in San Diego and Santa Clara
About Qualcomm Robotics
Qualcomm Advanced Robotics Team is building the AI first stack and platform for the next generation general purpose robots - from AMRs and cobots to emerging humanoids by pairing heterogeneous compute (CPU/GPU/DSP/NPU) with a full Robotics SDK and developer tooling for manipulation, perception, navigation, and fleet workflows. We are leveraging our success in automated driving product portfolio, advanced AI end to end development and tools, and safety architecture to accelerate the growth in this emerging market.
About the Role
We are seeking a Tech Lead to lead the technical design and implementation of AI Ops for a next-generation robotics platform. This role combines deep technical leadership and team management, centered on building robust data pipelines, training and evaluation orchestration, and model operations (MLOps/LLMOps) to support continuous learning and safe, scalable deployment across distributed robotic systems.
As the Tech Lead, you will design and oversee AI-driven operational capabilities—telemetry ingestion, observability, anomaly detection signals, and automated remediation—by building the pipelines and infrastructure that produce, validate, deploy, and monitor models. Your focus is on the systems that enable model development and iteration at scale.
What you’ll do
Lead the technical design of end-to-end data and ML pipelines spanning high-throughput telemetry ingestion, streaming/batch processing, curated training/eval datasets, and reliable lineage and data quality.
Build the platform for reproducible ML at scale: training orchestration, distributed jobs, experiment tracking, model registry, and gated promotion flows that enable continuous learning.
Operate real-time inference safely with robust serving, progressive rollouts (canary/blue-green/shadow), A/B experiments, automated rollback, and strict SLOs.
Own CI/CD pipelines and release processes for ML and robotics software, including automated builds, testing, deployment orchestration, and integration with observability and rollback mechanisms.
Establish comprehensive ML observability
—data/feature drift, performance monitoring, and incident automation with playbooks—integrated with robotics safety guardrails, auditability, and fail-safe design.Partner cross-functionally with robotics software/hardware, ML research, product, and operations to align interfaces, feature contracts, compliance needs, and reliability KPIs across cloud/edge.
Mentor and grow senior engineers across core platform services, data and model lifecycle tools, and externally facing SDKs setting crisp technical direction, code quality bars, and review culture.
Engage customers & partners as the senior technical face of the platform clarify requirements, guide integrations, and translate learnings into the roadmap.
How you’ll lead
Set the technical bar for platform correctness, reliability, and developer experience.
Coach senior ICs and tech leads, building a strong review culture and CI/CD discipline for robotics AI Ops pipelines.
Partner with product and silicon teams to align software roadmaps with SoC capabilities and internal and external customer needs.
Why Qualcomm
Shape the core platform that powers intelligent, safe, and scalable robotic operations.
Lead a high-impact team at the intersection of data engineering, MLOps, and robotics.
Build on a mature developer ecosystem (RBx/QRB platforms, on‑device AI tool chains) and ship at the edge, globally.
Minimum Qualifications:
• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.OR
Master's degree in Computer Science, Engineering, Information Systems, or related field and 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
OR
PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering,…
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