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About This Role
Our mission is to detect cancer early, when it can be cured. We are working to change the trajectory of cancer mortality and bring stakeholders together to adopt innovative, safe, and effective technologies that can transform cancer care.
We are a healthcare company, pioneering new technologies to advance early cancer detection. We have built a multi\-disciplinary organization of scientists, engineers, and physicians and we are using the power of next\-generation sequencing (NGS), population\-scale clinical studies, and state\-of\-the\-art computer science and data science to overcome one of medicine’s greatest challenges.
GRAIL is headquartered in the bay area of California, with locations in Washington, D.C., North Carolina, and the United Kingdom. It is supported by leading global investors and pharmaceutical, technology, and healthcare companies.
For more information, please visit grail.com
The Staff Software Development Engineer \- Enterprise AI Infrastructure is a senior technical role responsible for leading the design, development, and scaling of a centralized, highly governed enterprise AI platform. This position serves as a technical expert focused on AWS and Kubernetes\-based (EKS) AI infrastructure, agentic development, and multi\-agent orchestration operating within a regulated environment. The Staff Engineer partners closely with cross\-functional stakeholders across Software Engineering, Data Science, Security, Regulatory Affairs, and Product to build a unified control plane that securely connects large language models with enterprise tools and company knowledge.
This role is expected to drive technical excellence in cloud infrastructure, container orchestration, AI governance, identity\-scoped integrations, and agentic workflows while mentoring engineering teams and advancing the organization's enterprise AI strategy.
This role is based in Menlo Park, California, and will move to Sunnyvale, California in Fall 2026 OR Durham NC. It offers a flexible work arrangement, with the ability to work from GRAIL's office or from home. Our current flexible work arrangement policy requires that a minimum of 60%, or 24 hours, of your total work week be on\-site. Your specific schedule, determined in collaboration with your manager, will align with team and business needs and could exceed the 60% requirement for the site. At our Menlo Park campus, Tuesdays and Thursdays are the key days where we encourage on\-site presence to engage in events and on\-site activities.
### Responsibilities
- Lead the end\-to\-end design, development, deployment, and monitoring of a scalable, governed enterprise AI platform leveraging Amazon EKS and AWS native services (e.g., Bedrock, OpenSearch Serverless, KMS, VPC).
- Design and implement agentic AI workflows, specialized autonomous agents, and multi\-agent systems using advanced LLM orchestration techniques and agent frameworks.
- Architect and manage secure integrations using the Model Context Protocol (MCP) to connect the AI platform with internal systems, vector databases, and third\-party SaaS applications (e.g., Google Workspace, Slack).
- Build and enforce strict identity, authorization, and zero\-trust token brokering flows leveraging Okta, Auth0, and custom JWT authorizers to ensure secure, least\-privilege tool execution.
- Implement deterministic policy controls (e.g., Cedar policy engine) to enforce role\-based access, approval gates, and human\-in\-the\-loop checks at the API gateway level.
- Develop and maintain highly isolated, scalable containerized runtime environments (e.g., Kubernetes pods on Amazon EKS) for secure AI model execution, tool usage, and knowledge retrieval.
- Establish and maintain comprehensive audit trails and observability for all AI interactions, utilizing AWS CloudTrail and GenAI observability tools (e.g., OpenTelemetry) to track cost, latency, and tool calls.
- Collaborate with Product Management, Security, Regulatory, and business stakeholders to translate enterprise requirements into scalable, compliant AI infrastructure solutions.
- Troubleshoot and resolve complex technical issues involving cloud infrastructure, Kubernetes networking, network isolation (PrivateLink), and agentic workflows.
- Contribute to technology roadmaps, AI infrastructure strategy, and long\-term platform evolution initiatives.
- Mentor engineers, software developers, and technical teams while promoting engineering excellence, infrastructure\-as\-code (IaC) best practices, and continuous improvement.
- Partner with Quality, Regulatory, Privacy, Security, and Compliance functions to ensure software and AI systems operate in accordance with applicable regulatory requirements and company policies.
Adaptability and Growth Expectation
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As our organization continues to evolve and grow, this role may require flexibility in responsibilities and duties. Employees should expect that their role may expand, shift, or be modified to meet changing business needs, strategic priorities, and organizational objectives.
This may include:
- Taking on additional responsibilities.
- Participating in cross\-functional projects and initiatives.
- Adapting to new technologies, AI methodologies, software frameworks, processes, or engineering practices.
- Supporting other departments or teams during periods of high demand.
- Contributing to special projects or temporary assignments as needed.
These job duties are a summary of the primary duties and responsibilities of the position and are not intended to be a comprehensive or all\-inclusive listing of duties. Contents are subject to change at the Company's discretion.
### Required Qualifications
- Bachelor's degree or equivalent in Computer Science, Software Engineering, Artificial Intelligence, Cloud Computing, or related field; Master's or PhD preferred.
- 8\-12 years of relevant software development and cloud infrastructure experience with demonstrated technical leadership.
- Deep expertise in AWS cloud architecture and container orchestration, specifically with Amazon EKS, Kubernetes networking, network isolation (VPC, PrivateLink), IAM, KMS, and GenAI services (e.g., AWS Bedrock).
- Proven experience in agentic AI development, building autonomous agents, and orchestrating LLM tool\-calling workflows using frameworks like LangChain, LangGraph, AutoGen, or Claude Agent SDK.
- Hands\-on experience implementing the Model Context Protocol (MCP) or building robust, governed API/tool integrations for LLMs.
- Strong background in identity and access management (IAM), OAuth, JWT, and integrating with enterprise IdPs (Okta, Auth0\) for scoped, token\-based authorization.
- Advanced proficiency in programming languages such as Python, TypeScript, or Go, and infrastructure\-as\-code tools (Terraform, AWS CDK).
- Experience with vector databases, RAG (Retrieval\-Augmented Generation) architectures, and row\-level access controls (e.g., OpenSearch, FAISS, pgvector).
- Proficiency with CI/CD pipelines, MLOps practices, Kubernetes ecosystem tools (e.g., Helm), containerization, and modern observability stacks.
- Demonstrated level of knowledge regarding applicable regulatory standards commensurate with the position's complexity and scope, contributing to organizational regulatory compliance. Minimal applicable standards for this position include:
+ Cybersecurity principles, tools, and control frameworks (e.g., ISO 27001, NIST, SOC 2, HIPAA)
+ Operations within the regulated medical device environment (e.g., IVDD, IVDR, FDA 21 CFR 800 series, FDA 21 CFR Part 11\)
+ AI governance, software validation, data integrity, and risk management principles applicable to regulated environments
- Deep expertise in cloud infrastructure, containerized environments, agentic artificial intelligence, and secure distributed system design.
- Exceptional problem\-solving and analytical skills with the ability to address ambiguous, high\-impact technical challenges in AI orchestration and Kubernetes scaling.
- Strong leadership and influence skills, capable of driving alignment across engineering, security, regulatory, and business stakeholders.
- Excellent communication skills with the ability to explain complex LLM behaviors, infrastructure architectures, and security boundaries to technical and non\-technical audiences.
- Proven mentoring and coaching capabilities that elevate cloud engineering and AI talent.
- Strong understanding of AI safety, prompt injection defenses, secure tool execution, and deterministic policy enforcement.
- Strategic thinking with the ability to balance long\-term enterprise AI platform vision with near\-term business delivery.
- High adaptability and intellectual curiosity regarding emerging agentic AI frameworks, MCP specifications, and cloud computing trends.
### Physical Working Conditions
- Standard office or hybrid work environment depending on company policy.
- Frequent use of software development tools, AI/ML platforms, cloud infrastructure, data engineering tools, and collaboration systems.
- May require extended hours during major project deadlines, AI model deployments, production incidents, regulatory audits, or strategic initiatives.
- Operates with significant independence and responsibility and is expected to provide leadership on complex software, AI, technical, and organizational decisions.
The expected, full\-time, annual base pay scale for this position is $169k\-224k in Menlo Park, CA and $147K\-$195K in Durham NC.
This role may be eligible for other forms of compensation, including an annual bonus and/or incentives, subject to the terms of the applicable plans and Company discretion. This range reflects a good\-faith estimate of the range that the Company reasonably expects to pay for the position upon hire; the actual compensation offered may vary depending on factors such as the candidate’s qualifications. Employees in this role are also eligible for GRAIL’s comprehensive and competitive benefits package, offered in accordance with our applicable plans and policies. This package currently includes flexible time\-off or vacation; a 401(k) retirement plan with employer match; medical, dental, and vision coverage; and carefully selected mindfulness programs.
GRAIL is an equal employment opportunity employer, and we are committed to building a workplace where every individual can thrive, contribute, and grow. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, gender, gender identity, sexual orientation, age, disability, status as a protected veteran, , or any other class or characteristic protected by applicable federal, state, and local laws. Additionally, GRAIL will consider for employment qualified applicants with arrest and conviction records in a manner consistent with applicable law and provide reasonable accommodations to qualified individuals with disabilities. Please contact us at rc@grailbio.com if you require an accommodation to apply for an open position.
GRAIL maintains a drug\-free workplace. We welcome job\-seekers from all backgrounds to join us!
Salary Context
This $169K-$224K range is above the median for AI Product Manager roles in our dataset (median: $188K across 140 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At GRAIL, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $216,175 based on 270 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($196K) sits 9% below the category median. Disclosed range: $169K to $224K.
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.
GRAIL AI Hiring
GRAIL has 2 open AI roles right now. They're hiring across AI Product Manager. Positions span Durham, NC, US, Menlo Park, CA, US. Compensation range: $121K - $224K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
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).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
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
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