Senior Engineering Manager, AI Infrastructure

$146K - $220K Seattle, WA, US Senior AI/ML Engineer

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Skills & Technologies

AwsGcpKubernetesPython

About This Role

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##### 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 $146,880 \- $220,320, and in addition we have generous bonus plans to provide a competitive compensation package.

Who You Are:

We are seeking a Senior Manager, AI Infrastructure to run the day\-to\-day operation of the systems that power our research. Reporting to the VP of Engineering, you will own the execution and reliability of our high\-performance computing (HPC) environment which includes on\-prem GPU clusters and the software orchestration layer that schedules workloads across a hybrid cloud environment. This is a hands\-on operational leadership role: your mandate is to keep the platform fast, reliable, and well\-utilized, and to deliver against the roadmap set with your PM counterpart.

Our ideal candidate is a:

  • Systems Expert: You have a deep, hands\-on understanding of the Linux kernel, container runtimes, and distributed systems. You understand the performance implications of InfiniBand topologies and NCCL optimizations.
  • Execution\-Focused Leader: You plan and deliver against near\-term operational goals, keep reliability and researcher velocity high, and turn priorities set with leadership into shipped, dependable systems.
  • Pragmatic Operator: You are comfortable making trade\-offs between technical elegance and operational necessity. You triage and mitigate immediate risks, and know when to handle something yourself versus escalate.

Who We Are:

Ai2 is a non\-profit research institute at the forefront of open\-source AI development. Unlike industry peers, our goal is to share our findings, data, code, and models with the global scientific community.

#### Why Ai2:

  • Open Science: Your work directly enables the release of open models like OLMo, providing the broader research community with tools they can't get elsewhere.
  • Mission\-Driven: We prioritize scientific impact over profit margins. This allows us to focus on building the "right" infrastructure for long\-term research goals.
  • Complexity at Scale: You will manage some of the most dense and high\-performance compute environments currently in operation.

Your Next Challenge:

  • Cluster Operations: Manage the availability, performance, and health of our dense on\-prem GPU clusters. Coordinate with hardware vendors and internal teams to keep physical infrastructure meeting the demands of frontier model training.
  • Orchestration \& Scheduling: Operate and improve Beaker, our internal orchestration platform by optimizing resource allocation and driving high utilization across on\-prem assets and elastic cloud resources (AWS/GCP).
  • Storage Operations: Execute and continuously improve our storage environment, balancing high\-throughput performance for active training against cost\-effective durability for petascale research data. Contribute to the longer\-term storage roadmap.
  • Resource Management: Manage GPU compute allocation against budget. Track utilization, surface the data, and recommend when to burst to the cloud versus investing in on\-prem capacity, escalating larger trade\-offs as needed.
  • User Support \& Velocity: Serve as the technical bridge to our research teams. Ensure infrastructure is an accelerator, not a bottleneck, for a diverse set of research objectives.
  • Team Leadership: Manage and grow a team of systems engineers, SREs, and software developers. Set the bar for operational rigor, engineering quality, and a collaborative culture, and keep the team unblocked and delivering.

What You'll Need:

  • Experience: 12\+ years in infrastructure, systems engineering, or HPC (or an advanced degree with 8\+ years), including 2\+ years supervising a small engineering team (5\+).
  • Bachelor's degree in a related field: a relevant advanced degree may substitute for equivalent years of technical work experience.
  • GPU/HPC Stack: Direct experience operating large\-scale NVIDIA GPU clusters and high\-performance networking (InfiniBand/RoCE).
  • Orchestration: Strong background in Kubernetes, Slurm, or similar orchestration frameworks, particularly in hybrid\-cloud configurations.
  • Storage: Hands\-on experience with distributed filesystems (e.g., WEKA, Ceph, Lustre) and cloud storage integration at scale.
  • Software Development: Proficient in designing and managing SDLC processes including sprint planning and technical design reviews. Proficient in Go or Python.

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 $146K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).

View full AI/ML Engineer salary data →

Role Details

Title Senior Engineering Manager, AI Infrastructure
Location Seattle, WA, US
Category AI/ML Engineer
Experience Senior
Salary $146K - $220K
Remote No

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 The Allen Institute for Artificial Intelligence, 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

Aws (30% of roles) Gcp (17% of roles) Kubernetes (12% of roles) Python (51% of roles)

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. This role's midpoint ($183K) sits 16% below the category median. Disclosed range: $146K to $220K.

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.

The Allen Institute for Artificial Intelligence AI Hiring

The Allen Institute for Artificial Intelligence has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $220K - $220K.

Location Context

AI roles in Seattle pay a median of $236,900 across 267 tracked positions. That's 9% above 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 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

Based on 3,817 roles with disclosed compensation, the median salary for AI/ML Engineer positions is $218,750. Actual compensation varies by seniority, location, and company stage.
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.
About 14% of the 3,708 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
The Allen Institute for Artificial Intelligence is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI/ML Engineer positions include ML Architect, AI Engineering Manager, Principal ML Engineer. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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