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About This Role
Role Overview
As a Robotics & Physical AI Architect, you will define the technical architecture for a large-scale robotics platform supporting heterogeneous robots, AI workloads, cloud services, edge environments, and enterprise integrations. You will establish reference architectures, canonical APIs, protocols, SDKs, and integration patterns that enable multiple engineering teams to build on a consistent platform foundation.
You will work across robotics software, distributed cloud platforms, AI systems, edge computing, networking, security, and developer platforms to build architectures capable of operating thousands of robots across diverse environments. The role requires strong systems thinking, deep software architecture experience, and the ability to influence technical direction across multiple engineering organizations.
Robotics Platform Scope
The architecture is expected to span cloud, edge, robot, AI, data, security, and enterprise integration layers. Representative platform capabilities include:
Architecture Layer
Representative Robotics Platforms / Capabilities
Cloud Fleet & Control Plane
Fleet management; mission planning and orchestration; digital twins; remote operations; telemetry and observability; highly available distributed services.
Robot, Edge & Middleware
Robot identity and provisioning; edge and on-device runtimes; ROS 2, DDS, MQTT, OPC-UA, VDA5050, OpenRMF, and similar middleware and protocols; secure robot-to-cloud communications.
Physical AI, Data & Simulation
Multimodal AI; Vision-Language-Action (VLA) models; embodied AI; reinforcement learning; world models; AI infrastructure and MLOps; robotics data platforms; Isaac Sim, Gazebo, replay, analytics, and benchmarking.
Security & Enterprise Integration
Zero Trust; authentication and authorization; secure communications; OTA updates; device lifecycle management; APIs, SDKs, protocols, and integration patterns for enterprise and industrial systems.
This list is representative, not exhaustive; the role is expected to create reusable architecture across heterogeneous robot platforms rather than own every robot implementation.
What You'll Do
- Define long-term architecture and technical strategy for robotics and physical AI software platforms spanning cloud, edge, and on-device environments.
- Design scalable platform architectures that support autonomous robot fleets, heterogeneous robot vendors and form factors, AI workloads, and enterprise integrations.
- Architect systems for:
– Fleet management, robot identity and provisioning, and device lifecycle management.
– Digital twins, mission planning and orchestration, and remote operations.
– Real-time telemetry, observability, and highly available, fault-tolerant distributed services.
– Software and model lifecycle management, including secure OTA updates and AI operationalization.
– Robotics data platforms supporting simulation, replay, analytics, benchmarking, and AI model improvement.
– Developer APIs, SDKs, protocols, and reusable integration patterns for robotics applications.
- Define the cloud-to-robot control-plane architecture and canonical APIs that provide a consistent platform across heterogeneous robot implementations.
- Design secure communication architectures between robots, edge devices, cloud services, and enterprise systems, including authentication, authorization, distributed identity, Zero Trust, and secure device lifecycle patterns.
- Define interoperability patterns for heterogeneous robotics platforms using ROS 2, DDS, MQTT, OPC-UA, VDA5050, OpenRMF, and related robotics middleware and industry standards.
- Partner with AI researchers to operationalize multimodal AI, Vision-Language-Action (VLA), embodied AI, reinforcement learning, world models, and future foundation models for physical AI systems.
- Define architecture for AI infrastructure, MLOps, and model lifecycle management across cloud, edge, and robot environments.
- Architect scalable data platforms for robotics workloads, including simulation, replay, analytics, benchmarking, telemetry, and iterative AI model improvement.
- Define clear architecture boundaries and interfaces across cloud fleet services, edge computing platforms, and on-device software so capabilities can be deployed consistently across diverse robotics environments.
- Establish reusable integration patterns across fleet orchestration, digital twins, telemetry and observability, robotics data platforms, AI infrastructure, and enterprise systems.
- Define fleet observability and remote-operations architecture that connects robot telemetry with cloud and edge services for operational visibility and lifecycle management.
- Architect the platform to support multiple robot classes, including autonomous mobile robots, humanoids, manipulators, drones, and industrial automation systems, without coupling the control plane to a single vendor or form factor.
- Partner across robotics software, cloud infrastructure, edge computing, networking, security, AI, and developer-platform teams to operationalize a coherent end-to-end architecture.
- Define security architecture for robots, including identity, secure communications, OTA updates, Zero Trust, and device lifecycle management.
- Establish architecture patterns for highly available, fault-tolerant distributed systems spanning robots, edge platforms, and cloud services.
- Evaluate robotics technologies, middleware, frameworks, simulation platforms, and industry standards to guide platform architecture decisions.
- Drive technical direction across multiple engineering teams and mentor senior engineers and architects.
Basic Qualifications
- BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, or a related technical field.
- 15+ years of software engineering experience, with significant experience architecting distributed systems operating at large scale.
- Deep expertise in cloud-native software architecture, APIs, event-driven systems, microservices, and distributed platform design.
- Strong programming background in C++, Go, Java, Python, Rust, or similar languages, with the ability to reason about architecture and implementation tradeoffs.
- Experience with Kubernetes, containers, Linux, networking, and modern cloud platforms.
- Experience designing highly available, fault-tolerant systems across distributed cloud and edge environments.
- Strong understanding of systems security, authentication, authorization, distributed identity, and secure communications.
- Strong systems thinking and the ability to work across robotics software, distributed systems, AI platforms, networking, and developer tooling.
- Experience defining APIs, SDKs, protocols, and integration patterns for heterogeneous distributed platforms.
- Experience reasoning across cloud, edge, and on-device architecture boundaries for large-scale systems.
- Excellent written and verbal communication skills, including the ability to communicate architecture decisions across senior engineering and product audiences.
Preferred Qualifications
Experience in several of the following areas:
- Robotics software platforms and fleet-scale robotics architecture.
- ROS 2, DDS, MQTT, OPC-UA, VDA5050, OpenRMF, or similar robotics middleware, communication protocols, and interoperability standards.
- Autonomous mobile robots, humanoids, manipulators, drones, or industrial automation systems.
- Edge computing platforms and cloud-to-edge software architecture.
- Real-time telemetry, observability, fleet management, and remote operations systems.
- Digital twins and mission planning or orchestration platforms.
- Distributed databases and robotics data platforms supporting simulation, replay, analytics, and benchmarking.
- AI infrastructure, MLOps, and production model lifecycle management.
- Reinforcement learning, Vision-Language-Action (VLA) models, world models, embodied AI, or multimodal AI systems.
- Simulation platforms such as Isaac Sim or Gazebo.
- Robot identity and provisioning, secure communications, OTA update systems, Zero Trust architecture, or device lifecycle management.
- Cloud-to-robot control planes, canonical robotics APIs, and multi-vendor robot support.
- Mission orchestration, remote operations, and lifecycle management for fleets operating across diverse environments.
- Enterprise system integration and industrial automation environments.
- Building reusable APIs, SDKs, protocols, and platform capabilities for heterogeneous robotics systems.
Ideal Candidate
The ideal candidate combines deep software architecture experience with a practical understanding of robotics and AI. You are comfortable working across operating systems, cloud infrastructure, edge computing, robotics software, distributed systems, AI platforms, networking, security, data platforms, and developer tooling.
You think in terms of cloud-to-robot platforms rather than isolated robot implementations. You can simplify a fragmented robotics landscape into coherent architecture patterns for fleet control, digital twins, mission orchestration, telemetry, data, security, middleware, AI operationalization, and enterprise integration, and influence engineering organizations through technical leadership rather than organizational authority.
You have a track record of building platforms that other engineers and product teams build products on.
Areas of Technical Ownership
- Robotics software architecture
- Distributed systems
- Cloud platforms
- Edge computing
- AI infrastructure and MLOps
- Robotics middleware and interoperability
- Fleet orchestration and remote operations
- Digital twins
- Security architecture and device lifecycle
- Robotics data platforms, simulation, and replay
- Developer platforms
- Platform APIs, SDKs, and protocols
- Enterprise integration
- System scalability and reliability
What Success Looks Like
- Defined Oracle's reference architecture for OCI Robotics Cloud across cloud, edge, robot, AI, data, security, and enterprise integration layers.
- Established the cloud-to-robot control-plane architecture and canonical APIs for heterogeneous robotics platforms.
- Led the architecture for digital twins, mission orchestration, telemetry and observability, remote operations, and robot lifecycle management.
- Built the architectural foundation for multi-vendor robot support using reusable middleware, protocol, API, and SDK integration patterns.
- Partnered with AI Science to operationalize Physical AI and multimodal AI models, including Vision-Language-Action and embodied AI capabilities.
- Established scalable robotics data architecture for simulation, replay, analytics, benchmarking, and AI model improvement.
- Defined security architecture for robot identity, secure communications, OTA updates, Zero Trust, and device lifecycle management.
- Influenced Oracle's long-term robotics platform strategy and product direction across engineering organizations.
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 9,653 AI roles we're tracking, AI Architect positions make up 1% of the market. At Oracle, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $230,000 based on 212 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Oracle AI Hiring
Oracle has 13 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Architect, Research Scientist. Positions span Seattle, WA, US, Redwood City, CA, US, Seattle, WA.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national median.
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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