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About This Role
##### Persons in these roles are expected to work from our offices in Seattle. On\-site requirements vary based on position and team. If you have questions about on\-site work arrangements for this role, please ask your recruiter.
##### Our base salary range is $126,000 \- $189,000, and in addition we have generous bonus plans to provide a competitive compensation package.
Who You Are:
You are an expert systems engineer who occupies the space between high\-level software orchestration and low\-level system performance. You are motivated by the idea that world\-class infrastructure should be a catalyst for public good, not a proprietary secret. You are as comfortable designing a resource allocation algorithm in Go as you are debugging a NCCL timeout.
You lead by example, blending the rigor of a Senior Software Engineer with the pragmatic, hands\-on urgency of an HPC operator. Not only do you build systems, but you also ensure they thrive under the pressure of training world\-class AI models.
Who We Are:
While much of the AI industry has moved behind closed APIs, proprietary datasets, and "black box" infrastructure, Ai2 remains a lighthouse for Open Science. Founded by the late Paul Allen, we are a non\-profit research institute dedicated to building AI for the common good.
We don't have a stock price to defend or a walled garden to protect. Instead, we have a mission: to provide the global research community with the transparent, high\-performance foundations they need to achieve humanity\-enriching breakthroughs.
What Makes Us Different:
- Radical Transparency: We don't just release model weights; we release the data, the training code, and the infrastructure insights. We believe the "how" is just as important as the "what."
- Mission over Margin: Our "bottom line" is scientific impact. This gives us the unique freedom to prioritize technical elegance, long\-term stability, and open\-source contributions over quarterly profit targets.
- The Best of Both Worlds: We operate at the pace and scale of a world\-class tech startup but with the intellectual soul of a research lab.
- The Beaker Ecosystem: We build and operate systems like Beaker to coordinate the simultaneous training of frontier models (like OLMo) across massive GPU clusters. Our job is to ensure that the next great AI breakthrough isn't stalled by a resource bottleneck or a proprietary gatekeeper.
Your Next Challenge:
At Ai2, we believe that the most important AI breakthroughs should be transparent and accessible. Your challenge is to build the infrastructure that makes this possible. You will bridge the gap between our researchers and our GPU clusters.
You will be a senior technical contributor responsible for ensuring that when a researcher submits a job, the software schedules it intelligently and the hardware executes it flawlessly. This involves:
- Designing for Scale: Designing and scaling our orchestration layer to ensure that the highest value workloads receive GPU time.
- Operational Excellence: Moving our HPC operations from manual intervention to high\-level automation.
- Performance Engineering: Working directly with researchers to squeeze every bit of performance out of our GPU\-accelerated computing environment.
Your Responsibilities:
- Full\-Stack Ownership: Independently design and deliver critical systems that span the entire stack—from the Beaker job scheduler to the execution runtime.
- System Automation: Build innovative tooling and software\-defined infrastructure to accelerate researcher velocity and automate cluster health management.
- Performance Optimization: Conduct root\-cause analysis on complex distributed system failures and implement optimizations for distributed workloads.
- Technical Input \& Ownership: Provide valuable input into the roadmap for managing large\-scale HPC systems, including the deployment of compute, networking, and storage in partnership with leadership.
- Mentorship \& Culture: Foster a high\-performance culture by reviewing code/design docs, mentoring team members, and driving process improvements within the team.
- Collaboration: Effectively communicate and collaborate with internal research staff to share system designs, gather feedback, and support engineers on implementation tasks.
What You'll Need:
- 8\+ years of professional experience developing business\-critical software and operating large\-scale compute infrastructure. Proficiency in Go and/or Python preferred.
- Bachelor's degree in related field; relevant advanced degree may substitute for equivalent years of technical work experience.
- Linux Expertise: Expert\-level knowledge of Linux internals, and container runtimes like Docker.
- Distributed Systems Expertise: A proven track record of designing, debugging, and optimizing high\-scale distributed systems and databases.
- Communication: Exceptional writing skills and the ability to drive consensus across diverse groups of researchers and engineers.
- A principled approach to engineering: You care about how systems are built and are excited by the unique constraints and freedoms of a non\-profit research environment.
Bonus Qualifications:
- Applied experience with workload schedulers (like Kubernetes or Slurm) and high\-performance networking (NCCL and InfiniBand).
- Prior experience training or fine\-tuning frontier AI models.
- Deep systems administration expertise or "Site Reliability Engineering" (SRE) background in an HPC context.
- Experience contributing to open\-source infrastructure or orchestration projects.
- Familiarity with on\-prem storage systems like WEKA and Ceph.
Physical Demands and Work Environment:
The physical demands described here are representative of those that must be met by a team member to successfully perform the essential functions of this position. Reasonable accommodations may be made to enable individuals with disabilities to perform the functions.
- Must be able to remain in a stationary position for long periods of time.
- The ability to communicate information and ideas so others will understand. Must be able to exchange accurate information in these situations.
- The ability to observe details at close range.
- Can work under deadlines.
A Little More About Ai2:
Ai2 is a Seattle based non\-profit AI research institute founded in 2014 by the late Paul Allen. Our mission is building breakthrough AI to solve the world's biggest problems. We develop foundational AI research and innovation to deliver real\-world impact through large\-scale open models, data, robotics, conservation, and beyond.
In addition to Ai2's core mission, we also aim to contribute to humanity through our treatment of each member of the Ai2 Team. Some highlights are:
- We are a learning organization – because everything Ai2 does is ground\-breaking, we are learning every day. Similarly, through weekly Ai2 Academy lectures, a wide variety of world\-class AI experts as guest speakers, and our commitment to your personal on\-going education, Ai2 is a place where you will have opportunities to continue learning alongside your coworkers.
- We value diversity \- We seek to hire, support, and promote people from all genders, ethnicities, and all levels of experience regardless of age. We particularly encourage applications from women, non\-binary individuals, people of color, members of the LGBTQA\+ community, and people with disabilities of any kind.
- We value inclusion \- We understand the value that people's individual experiences and perspectives can bring to an organization, and we are building a culture in which all voices are heard, respected and considered.
- We emphasize a healthy work/life balance – we believe our team members are happiest and most productive when their work/life balance is optimized. While we value powerful research results which drive our mission forward, we also value dinner with family, weekend time, and vacation time. We offer generous paid vacation and sick leave as well as family leave.
- We are collaborative and transparent – we consider ourselves a team, all moving with a common purpose. We are quick to cheer our successes, and even quicker to share and jointly problem solve our failures.
- We are in Seattle – and our office is on the water! We have mountains, we have lakes, we have four seasons, we bike to work, we have a vibrant theater scene, and we have so much else. We even have kayaks for you to paddle right outside our front door. We welcome interest from applicants from outside of the United States.
- We are friendly– chances are you will like every one of the 200\+ (and growing) people who work here. We do.
Ai2 is proud to be an Equal Opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. You may view the related Know Your Rights compliance poster and the Pay Transparency Nondiscrimination Provision by clicking on their corresponding links.
This employer participates in E\-Verify and will provide the federal government with your Form I\-9 information to confirm that you are authorized to work in the U.S. If E\-Verify cannot confirm that you are authorized to work, this employer is required to give you written instructions and an opportunity to contact the Department of Homeland Security (DHS) or Social Security Administration (SSA) so you can begin to resolve the issue before the employer can take any action against you, including terminating your employment. Employers can only use E\-Verify once you have accepted a job offer and completed the Form I\-9\.
We are committed to providing reasonable accommodations to employees and applicants with disabilities to the full extent required by the Americans with Disabilities Act (ADA). If you feel you need a reasonable accommodation pursuant to the ADA, you are encouraged to contact us at [email protected].
Benefits:
- Team members and their families are covered by medical, dental, vision, and an employee assistance program.
- Team members are able to enroll in our health savings account plan, our healthcare reimbursement arrangement plan, and our health care and dependent care flexible spending account plans.
- Team members are able to enroll in our company's 401k plan.
- Team members will receive $125 per month to assist with commuting or internet expenses and will also receive $200 per month for fitness and wellbeing expenses.
- Team members will also receive up to ten sick days per year, up to seven personal days per year, up to 20 vacation days per year and twelve paid holidays throughout the calendar year.
- Team members will be able to receive annual bonuses and can participate in the long\-term incentive plan.
Note: This job description in no way states or implies that these are the only duties to be performed by the team members(s) of this position. Team members will be required to follow any other job\-related instructions and to perform any other job\-related duties requested by any person authorized to give instructions or assignments. All duties and responsibilities are essential functions and requirements and are subject to possible modification to reasonably accommodate individuals with disabilities. To perform this job successfully, the team member(s) will possess the skills, aptitudes, and abilities to perform each duty proficiently. Some requirements may exclude individuals who pose a direct threat or significant risk to the health or safety of themselves or others. The requirements listed in this document are the minimum levels of knowledge, skills, or abilities. This document does not create an employment contract, implied or otherwise, other than an at will relationship.
Salary Context
This $126K-$189K range is below the median for AI Software Engineer roles in our dataset (median: $190K across 219 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At The Allen Institute for Artificial Intelligence, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $232,000 based on 797 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($157K) sits 32% below the category median. Disclosed range: $126K to $189K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
The Allen Institute for Artificial Intelligence AI Hiring
The Allen Institute for Artificial Intelligence has 2 open AI roles right now. They're hiring across AI Software Engineer, Research Engineer. Based in Seattle, WA, US. Compensation range: $189K - $220K.
Location Context
AI roles in Seattle pay a median of $227,400 across 1,084 tracked positions. That's 14% above the national median.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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