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About This Role
Oracle is seeking a Principal Software Developer to help design and build an AI\-powered Digital Twin platform. The platform will combine real\-time operational data, digital models, simulation, graph technologies, and AI agents to help customers understand complex systems, predict outcomes, investigate issues, and automate actions safely.
This is a senior individual contributor role for an engineer who can lead complex technical initiatives, influence architecture across teams, and deliver production\-grade capabilities spanning distributed systems, data platforms, generative AI, and agentic workflows.
Responsibilities
- Design and build scalable services for digital models, telemetry, state management, relationships, simulation, and real\-time analytics.
- Develop AI agents that reason over digital twin data, enterprise knowledge, telemetry, and operational systems.
- Build capabilities for RAG, graph\-based retrieval, agent orchestration, tool use, memory, evaluation, and human approval.
- Define technical designs and influence architecture across multiple components and engineering teams.
- Develop secure, reliable, and observable distributed systems for enterprise cloud environments.
- Ensure AI\-generated recommendations and actions are grounded, explainable, auditable, and policy\-compliant.
- Partner with product managers, applied scientists, architects, and engineers to convert customer requirements into scalable platform capabilities.
- Lead complex feature development from design through implementation, testing, deployment, and production support.
- Improve engineering quality through design reviews, code reviews, automated testing, operational readiness, and technical mentoring.
Investigate and resolve difficult performance, reliability, data consistency, and production issues.
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Qualifications
- 8\+ years of software engineering experience building large\-scale production systems.
- Strong programming skills in Java, Python, Go, C\+\+, or a comparable language.
- Deep understanding of distributed systems, data structures, algorithms, APIs, databases, and cloud architecture.
- Experience designing and operating highly available, multi\-tenant cloud services.
- Experience with generative AI, large language models, RAG, AI agents, or agentic orchestration.
- Familiarity with graph databases, vector search, event streaming, time\-series data, or simulation systems.
- Experience delivering complex projects across multiple services or teams.
- Strong debugging, problem\-solving, and system\-design skills.
Ability to communicate technical decisions clearly and influence without direct authority.
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Preferred Qualifications
- Experience with digital twins, IoT, industrial systems, cloud operations, or simulation platforms.
- Experience with LangGraph, LangChain, LlamaIndex, Semantic Kernel, OCI Generative AI Agents, or similar technologies.
- Knowledge of LLMOps, agent evaluation, prompt and policy versioning, model monitoring, and AI governance.
- Experience building knowledge graphs, hybrid retrieval systems, or enterprise RAG solutions.
- Experience applying AI agents to root\-cause analysis, predictive maintenance, optimization, incident response, or automated remediation.
- Experience with OCI, Oracle Database, Kubernetes, graph databases, streaming platforms, or vector databases.
Experience mentoring engineers and providing technical leadership for major platform initiatives.
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Key Responsibilities
System Design \& Architecture \- System Scalability:
–Lead the development and implementation, and begin to architect, components of scalable distributed systems that support horizontal and vertical scaling to meet system demands, including leveraging distributed state management tools.
–Optimize code and/or systems for large\-scale data processing and high\-throughput requirements to support hyper\-scale systems.
–Define scalability requirements for owned components and ensure design and implementation requirements are met.
–Design systems to scale with elasticity (e.g., effectively scaling both up and down).
–Leverage data plane platforms to effectively handle large\-scale data retrieval, storage, and processing.
–Design performance and load testing.
System Design \& Architecture \- System Reliability Design:
–Build and design fault\-tolerant components and systems capable of withstanding in\-service updates by implementing redundancy, replication, and automatic failover mechanisms.
–Design systems to effectively handle service disruptions (e.g., network partitions) by prioritizing consistency, availability, or partition tolerance.
–Implement and optimize approaches to handle network unreliability, including load\-shedding, throttling, and rate\-limiting.
–Design components and systems that are durable and adhere to service level objectives (SLOs), setting expectations for availability and durability of other computing services within the department.
System Design \& Architecture \- System Reliability Performance:
–Define key performance indicators (KPIs) and telemetry to identify gaps or issues in running systems.
–Build and customize moderately complex dashboards, telemetry systems, and alerting mechanisms to proactively monitor components and system health.
System Design \& Architecture \- Correctness / Availability:
–Design and implement functional and correctness requirements for feature sets and/or systems in new or existing systems.
–Design complex test scenarios (e.g., fault\-injection, brown\-out) to evaluate system correctness.
–Implement data replication and synchronization techniques to maintain data integrity and availability.
Operational Troubleshooting \& Incident Management:
–Take a proactive role in diagnosing, debugging, and resolving issues in active components and systems to support ongoing operation, and mentor others in these processes.
–Implement strategies to prevent interruptions, ensuring no maintenance windows are required for customers and users when resolving issues.
–Maintain expertise in owned components and systems to ensure effective troubleshooting and performance.
–Meet operational readiness expectations through design and implementation.
–Serve in operational support rotations, providing guidance in incident response and root cause investigations.
Compliance \& Security:
–Implement robust security measures to protect data and applications in multi\-tenant environments, including encryption techniques and access controls.
–Execute remediation plans to address identified security gaps.
–Ensure cloud infrastructure is in compliance with industry standards and regulations and that documentation is up to date.
Automation \& Change Management:
–Develop and maintain automation scripts and tools (e.g., Infrastructure as Code (IaC)) to manage cloud infrastructure.
–Create and adhere to change management plans for patching, updating, and rolling back applications, and begin designing systems and components to allow for automation of these processes.
Core Responsibilities
Planning \& Execution:
–Manages and coordinates moderately complex tasks, monitoring timelines and deliverables to ensure timely completion and adherence to requirements for a moderately\-sized project or initiative. Efficiently delegates, monitors, and prioritizes work across multiple projects, providing technical oversight and adjusting plans to address shifts in resources or timelines.
Collaboration \& Partnership:
–Collaborates across the organization to align on expectations and achieve shared objectives. Leverages understanding of business leaders, stakeholders, and/or customers to ensure proposed solutions meet their needs. Supports inclusivity by actively seeking and listening to diverse perspectives, ensuring others feel heard and respected.
Problem Solving:
–Identifies and addresses moderately complex issues by analyzing a wide range of data and/or information to identify solutions in accordance with standard practices. Proactively escalates unresolved or critical issues with a thorough assessment and suggests potential solutions. Reviews, contributes to, and documents problem solving strategies.
Continuous Learning:
–Pursues learning opportunities to expand knowledge and skills and/or tools in new areas and stays abreast of the latest industry trends and best practices. Proactively seeks and leverages ongoing feedback and training to improve skills. Coaches and mentors junior team members, fostering continuous learning and knowledge sharing within and across teams.
Continuous Improvement:
–Develops ideas, recommends updates, and/or collaborates on the implementation of process improvements to increase the efficiency and effectiveness of processes, protocols, and workflows across teams, and evaluates the impact on key stakeholders. Solicits feedback from others on ideas for alternative approaches and methods for continued improvement.
Performance and Development:
–Contributes to the talent development pipeline by participating in candidate interviews, assessing candidates, and providing hiring recommendations.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Oracle, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Oracle AI Hiring
Oracle has 15 open AI roles right now. They're hiring across AI Agent Developer, AI Engineering Manager, AI/ML Engineer, AI Software Engineer. Positions span US, Seattle, WA, US, Redwood City, CA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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