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About This Role
About the team
The AI Deployment Engineering (ADE) team is responsible for ensuring the safe and effective deployment of generative AI applications for developers and enterprises. We act as trusted advisors and technical partners to customers and ecosystem partners, helping identify high\-impact GenAI use cases and driving them into production through strong architectural guidance and hands\-on execution.
The Partner ADE organization works closely with strategic cloud providers, systems integrators, consultancies, and implementation partners to scale successful deployments of OpenAI technologies. As the leader of the AWS Partner ADE pod, you will manage a team of AI Deployment Engineers focused on enabling AWS\-aligned partners and their customers to build, deploy, and operationalize AI applications on OpenAI’s platform.
About the role
We are seeking a Manager, Partner AI Deployment Engineering – AWS to lead a team of AI Deployment Engineers supporting strategic AWS ecosystem partnerships. In this role, you will own the technical success strategy for AWS\-aligned partners and help scale repeatable deployment motions.
Your team will guide partners and customers through the full AI implementation lifecycle—from solution design and architecture reviews through production deployment, optimization, and adoption growth. You will work cross\-functionally with internal and external stakeholders across Sales, Partnerships, Product, Research, and Engineering teams to ensure the voice of partners and customers informs our platform roadmap and deployment strategy.
This role requires a blend of technical depth, customer leadership, operational rigor, and people management. Success will be measured through production deployments, partner technical maturity, API adoption growth, team development, and the overall impact of the AWS partner ecosystem.
This role is based in our San Francisco office. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.
In this role, you will:
Lead, mentor, and grow a team of AI Deployment Engineers supporting strategic AWS partner engagements and customer deployments.
Define the operating model, engagement strategy, and technical priorities for the AWS Partner ADE pod.
Partner closely with AWS partner leadership, solution architects, delivery organizations, and customer stakeholders to accelerate production adoption of OpenAI technologies.
Guide teams through complex generative AI and traditional ML deployments, including architecture reviews, implementation planning, security considerations, evaluation strategies, and operational readiness.
Serve as a senior technical escalation point for critical partner and customer engagements, helping teams navigate ambiguity and drive successful outcomes.
Collaborate with Product, Research, and Engineering teams to synthesize partner feedback into platform improvements, tooling enhancements, and deployment best practices.
Develop scalable enablement frameworks, reference architectures, and repeatable deployment patterns that improve partner effectiveness and reduce time\-to\-production.
Drive operational excellence across the team, including resource planning, prioritization, hiring, onboarding, performance management, and career development.
Act as an external thought leader on enterprise AI deployment, cloud\-native AI architectures, and responsible AI adoption within the AWS ecosystem.
You might thrive in this role if you:
Have 8\+ years of experience in technical customer\-facing roles, including managing executive\-level technical and business relationships with enterprise organizations and strategic partners.
Have 3\+ years of experience leading high\-performing technical teams in solutions engineering, deployment engineering, forward\-deployed engineering, customer engineering, or post\-sales environments.
Have hands\-on experience deploying Generative AI and traditional ML systems into production environments, including familiarity with LLM application architectures, evaluation methodologies, orchestration frameworks, and operational best practices.
Have strong knowledge of AWS cloud infrastructure and modern cloud\-native architectures, including networking, security, compute, storage, observability, and application deployment patterns.
Have experience working with cloud ecosystem partners, systems integrators, consultancies, or technical alliance organizations.
Have technical depth in software engineering or solution development using languages such as Python, JavaScript, or TypeScript.
Are comfortable balancing strategic leadership with hands\-on technical engagement and operational execution.
Are an effective communicator who can translate complex technical concepts into clear business outcomes for executives, partners, developers, and customers alike.
Have a strong sense of ownership, humility, and curiosity, with a willingness to learn quickly and help others succeed in ambiguous, fast\-moving environments.
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general\-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
For additional information, please see
OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement
.
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US\-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non\-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
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We are committed to providing reasonable accommodations to applicants with disabilities.
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At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.
Compensation
$251K – $335K \+ Offers Equity
Salary Context
This $251K-$335K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At OpenAI, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($293K) sits 64% above the category median. Disclosed range: $251K to $335K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
OpenAI AI Hiring
OpenAI has 8 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Engineer. Based in San Francisco, CA, US. Compensation range: $330K - $445K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 1,990 tracked positions. That's 26% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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