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About This Role
Job ID 2026-17405 Date posted Oct. 12, 2026 Location Austin, Texas Category Software Engineering
Job Description:
We are building a central AI control plane to support the safe, scalable, and efficient use of AI across Arm’s engineering teams.
This hands-on platform engineering role will help deliver and be responsible for the runtime platforms that make AI services reliable, secure, observable and supportable at Arm scale. You will work across Kubernetes, cloud, identity, secrets, networking, telemetry, incident management and automation to provide the production foundation for Arm’s AI platform.
You will initially contribute in three key areas:
Production AI runtime platforms:
- Build/deploy and operate the infrastructure for centrally hosted AI platform services, including MCP server infrastructure, model gateway services and supporting control-plane components.
- Implement runtime patterns for isolation, scalability, secure execution, capacity management and cost-aware operation.
- Automate provisioning, configuration, upgrades and lifecycle management using infrastructure-as-code and GitOps patterns.
Reliability, observability and support:
- Implement service-level indicators, service-level objectives, alerting, dashboards, runbooks and support workflows.
- Incident response, post-incident review and vendor outage handling for AI services embedded in engineering workflows.
- Build telemetry that helps Arm understand AI platform health, usage, performance, cost and operational risk.
Secure operations by default:
- Ensure platform components meet production readiness, security and compliance expectations.
- Help make secure AI usage the default by providing reliable paved paths rather than manual or fragmented infrastructure.
Responsibilities:
- Build, operate and continuously improve the production infrastructure for Arm’s AI platform services, using both custom and third party solutions.
- Contribute to reliability, scalability, monitoring, alerting, incident response, runbooks and operational readiness for AI platform components.
- Develop automation for provisioning, deployment, configuration, backup, recovery, patching, upgrades and lifecycle management.
- Implement secure runtime patterns, including workload isolation, secrets management, identity integration, network controls and auditability.
- Participate in production support and our paid on-call rota for high impact incident response.
Required Skills and Experience:
- Familiarity with AI platform concepts such as model routing, MCP servers, agentic workflows, RAG systems or LLM observability.
- Experience deploying and operating production infrastructure or platform services in a Linux-based environment using Kubernetes, containers, cloud or private-cloud platforms, infrastructure-as-code and CI/CD/GitOps tooling.
- Automation and scripting skills, for example Terraform, Go, Python or similar.
“Nice To Have” Skills and Experience :
- Awareness of security fundamentals for production platforms, including identity, secrets, access control, network segmentation, vulnerability management and audit logging.
- Experience operating internal developer platforms, AI platforms, model gateways, MCP infrastructure or other shared engineering platforms.
- Experience with service mesh, policy-as-code, workload identity, sandboxing, secure runtime environments or multi-tenant platform designs.
- Experience with regulated or security-sensitive engineering environments.
- Working knowledge of Open AI tools, products and APIs
In Return:
This is a chance to help build a new AI Platform capability for Arm. In return, we'll welcome you into a collaborative, low-ego but high performing team, alongside a highly capable and deeply technical set of peers. We will give you room to grow technically, an interesting and challenging role, and an opportunity to improve the lives of thousands of engineers across Arm!
Salary Range:
$161,500-$218,500 per year We value people as individuals and our dedication is to reward people competitively and equitably for the work they do and the skills and experience they bring to Arm. Salary is only one component of Arm's offering. The total reward package will be shared with candidates during the recruitment and selection process.
Our 10x Mindset
At Arm, we believe progress happens when people are empowered to think bigger and push beyond what seems possible. Our 10x mindset is about curiosity, ambition and creating impact that will be used by millions. We learn fast, prioritise collaboration and turn bold ideas into real technology. We look for people who are inspired by this way of working and want to grow in an environment where bold ideas are welcomed. Discover how the 10x mindset shapes life at Arm: https://careers.arm.com/10x-mindset (opens in new window)
Use of AI in Your Application
At Arm, we embrace the benefits AI can bring, including in recruitment. You may use AI to support your preparation, such as improving the clarity of written responses, practicing interview questions, or learning more about our industry. However, your application and interview responses must reflect your own thinking, experience, and abilities. We want to understand your authentic perspective, creativity, and approach to problem solving. As such, you may not use AI to complete assigned tasks as part of any assessments. If you plan to use AI-supported or assistive technologies as part of a reasonable adjustment, please contact [email protected] so we can support you.
Recruitment Adjustments and Accommodations
At Arm, we want to build extraordinary teams. If you need an adjustment or an accommodation during the recruitment process, please email [email protected]. To note, by sending us the requested information, you consent to its use by Arm to arrange for appropriate accommodations. All accommodation or adjustment requests will be treated with confidentiality, and information concerning these requests will only be disclosed as necessary to provide the accommodation. Although this is not an exhaustive list, examples of support include breaks between interviews, having documents read aloud, or office accessibility. Please email us about anything we can do to accommodate you during the recruitment process.
Hybrid Working
Arm’s approach to hybrid working is designed to create a working environment that supports both high performance and personal wellbeing. We believe in bringing people together face to face to enable us to work at pace, whilst recognizing the value of flexibility. Within that framework, we empower groups/teams to determine their own hybrid working patterns, depending on the work and the team’s needs. Details of what this means for each role will be shared upon application. In some cases, the flexibility we can offer is limited by local legal, regulatory, tax, or other considerations, and where this is the case, we will collaborate with you to find the best solution. Please talk to us to find out more about what this could look like for you.
Equal Opportunities
Arm is an equal opportunity employer, committed to providing an environment of mutual respect where equal opportunities are available to all applicants and colleagues. We are a diverse organization of dedicated and innovative individuals, and don’t discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Salary Context
This $161K-$218K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Arm, 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($190K) sits 10% below the category median. Disclosed range: $161K to $218K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Arm AI Hiring
Arm has 9 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Engineer. Positions span San Jose, CA, US, Austin, TX, US, Seattle, WA. Compensation range: $218K - $351K.
Location Context
AI roles in Austin pay a median of $208,000 across 317 tracked positions. That's 3% below the national 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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