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About This Role
Job Details:
Job Description:
The Opportunity:
For nearly a decade, Intel's Neuromorphic Computing Lab, together with a global ecosystem of 250+ research groups, has explored architectures, algorithms, and software inspired by the brain's extraordinary efficiency, scalability, and adaptability. Our Loihi series of research chips pioneered event-driven, sparse, and massively parallel neuro-inspired processing, fueling over 100 peer-reviewed publications that validate the promise of this novel approach.
Now, we're entering an exciting new chapter: transforming these breakthroughs into real-world products that will power the coming era of physical AI systems - beyond the reach of GPUs and mainstream AI accelerators.
If you are passionate about pushing the boundaries of computing, from transistor-level innovation to software abstractions, join us. Help define the next wave of AI technology that harnesses the proven advantages of Intel's neuromorphic computing technology with the versatility demanded by modern AI workloads.
Position Overview:
Join the team building Intel's neuromorphic AI execution stack targeting edge and robotic systems. In this role, you will lead the team and technical direction for our next-generation runtime stack, while remaining hands-on in architecture and implementation. You will set the roadmap from the first product generation onward, on a spatial, event-driven architecture that redraws the boundary between compiler and runtime.
Responsibilities:
- Define and drive the technical vision, architecture, and roadmap for the runtime stack, from firmware to framework integration, while contributing hands-on to critical design and implementation.
- Architect and develop the runtime and performance tooling, and define the execution behavior that the firmware and drivers must implement, whether the device is attached to a host or runs standalone.
- Own the contracts between the runtime stack and the compiler, as well as its integration with industry-standard AI and robotics frameworks.
- Own the measurable performance, real-time behavior, and production quality of the stack across silicon and simulators.
- Collaborate with compiler, hardware, and application teams, as well as customers and partners, to translate workload requirements into runtime capabilities and feed performance insights back into compiler and hardware design.
- Provide technical leadership by establishing engineering practices and mentoring engineers.
About the Organization:
As part of Intel's CTO Office, you will join a vertically integrated incubation effort dedicated to bringing Intel's neuromorphic technology innovations to market. Our diverse team of engineers and researchers has pioneered sparse, event-based, neuromorphic architecture over multiple generations and is now focused on commercializing the technology in future Intel and partner products. Qualifications:
Minimum qualifications
- BSc, MSc, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field.
- 5+ years of experience developing and shipping AI runtime software, such as OpenVINO, TensorRT, ONNX Runtime, Ryzen AI, or comparable runtimes, for accelerators, GPUs, or other complex hardware, with working knowledge of the underlying drivers and firmware.
- 5+ years of experience leading the architecture of complex software systems, including defining technical direction and the interfaces to adjacent components.
- 5+ years of experience owning the performance and production quality of accelerator software, including profiling, benchmarking, and debugging across hardware and software boundaries.
- 5+ years of experience providing technical leadership to engineering teams, including mentoring engineers and leading cross-functional delivery.
- 10+ years of hands-on experience developing production-grade systems software in C/C++, with proficiency in Python, using disciplined engineering practices such as code reviews, automated testing, and CI/CD.
- 1+ years of hands-on experience with agentic software engineering, routinely using AI agents to design, implement, test, and review production code.
Preferred qualifications
- Experience managing engineering teams, including hiring and developing engineers.
- Experience with the internals of OpenVINO, oneAPI Level Zero, or comparable accelerator software stacks.
- Experience with spatial, dataflow, or statically scheduled accelerators where the compiler determines placement and scheduling.
- Experience with robotics or real-time systems, including deterministic execution and ecosystems such as ROS 2 or Holoscan.
- Experience developing or integrating functional or performance simulators into accelerator software stacks.
- Familiarity with device driver and firmware development for embedded Linux or RTOS-based systems.
- Experience with HW/SW co-design or performance modeling.
Job Type:
Experienced Hire Shift:
Shift 1 (United States of America) Primary Location:
US, Oregon, Hillsboro Additional Locations:
US, California, Santa Clara Posting Statement:
All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance. Position of Trust
N/A Benefits
We offer a total compensation package that ranks among the best in the industry. It consists of competitive pay, stock bonuses, and benefit programs which include health, retirement, and vacation. Find out more about the benefits of working at Intel .
Annual Salary Range for jobs which could be performed in the US: $195,200.00-315,490.00 USD
The range displayed on this job posting reflects the minimum and maximum target compensation for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific compensation range for your preferred location during the hiring process.
Work Model for this Role
This role will be eligible for our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. * Job posting details (such as work model, location or time type) are subject to change. *
ADDITIONAL INFORMATION: Intel is committed to Responsible Business Alliance (RBA) compliance and ethical hiring practices. We do not charge any fees during our hiring process. Candidates should never be required to pay recruitment fees, medical examination fees, or any other charges as a condition of employment. If you are asked to pay any fees during our hiring process, please report this immediately to your recruiter.
Salary Context
This $195K-$315K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Intel Corporation, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($255K) sits 20% above the category median. Disclosed range: $195K to $315K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Intel Corporation AI Hiring
Intel Corporation has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Santa Clara, CA, US, Hillsboro, OR, US, Austin, TX, US. Compensation range: $170K - $315K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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).
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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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