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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 Forward Deployed Engineer \- Software Development Engineer – Artificial Intelligence (AI) is an experienced software engineer responsible for designing, developing, testing, deploying, and supporting AI\-enabled software applications, machine learning solutions, and enterprise software platforms. Working under the guidance of senior technical leaders, this role contributes to the development of scalable, secure, and maintainable AI solutions that support GRAIL's scientific, clinical, laboratory, commercial, and enterprise initiatives.
The Forward Deployed Engineer \- Software Development Engineer partners with Software Engineering, Data Science, Product Management, Quality, Regulatory Affairs, Clinical Development, Laboratory Operations, Finance, Legal, People, and other cross\-functional teams to develop innovative AI\-powered solutions while ensuring software quality, operational reliability, and compliance with applicable standards. This role is expected to demonstrate technical ownership within assigned areas while continuing to develop expertise in AI engineering, software architecture, and cloud\-native technologies.
This role is based in Menlo Park, California, and will move to Sunnyvale, California in Fall 2026\. 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. We may also consider candidates in Durham NC but the bay area is preferred.
Please note, we have two internal roles open
### Responsibilities
- Design, develop, test, deploy, and maintain AI\-enabled software applications, machine learning services, APIs, and enterprise software solutions.
- Develop software that leverages artificial intelligence, machine learning, Generative AI, Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), and other modern AI capabilities to improve business processes and user experiences.
- Collaborate with Product Management, Data Science, Engineering, and business stakeholders to translate functional requirements into scalable technical solutions.
Implement high\-quality, maintainable, and secure software following established software engineering, AI development, and coding standards.
- Contribute to AI model integration, prompt engineering, model evaluation, testing, monitoring, and deployment activities.
- Participate in software architecture discussions, design reviews, code reviews, and technical planning sessions.
- Troubleshoot, analyze, and resolve moderately complex software, AI, integration, and platform issues.
- Develop automated tests, CI/CD pipelines, and deployment processes to improve software quality and delivery efficiency.
- Evaluate new AI frameworks, libraries, cloud services, and engineering tools to improve development productivity and product capabilities.
- Contribute to technical documentation, design specifications, user training, knowledge sharing, and engineering best practices.
- Partner with Quality, Regulatory, Security, Privacy, and Compliance teams to support secure, reliable, and compliant software development.
- Support production operations, incident response, defect resolution, and continuous improvement initiatives.
#### Adaptability and Growth Expectation
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:
- Participating in cross\-functional AI and software engineering initiatives.
- Learning and adopting emerging AI technologies, software frameworks, and engineering practices.
- Supporting enterprise modernization and automation initiatives.
- Contributing to special projects or temporary assignments as needed.
These job duties summarize the primary responsibilities of the position and are not intended to be a comprehensive listing of all duties. Responsibilities may change based on business needs.
### Required Qualifications
- Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Data Science, or a related field.
- Typically 3–5 years of professional software development experience, including experience developing enterprise applications or AI\-enabled software solutions.
- Experience with software development using Python and one or more programming languages such as Java, C\#, C\+\+, or JavaScript/TypeScript.
- Experience working with AI or machine learning frameworks such as PyTorch, TensorFlow, Scikit\-learn, Hugging Face, LangChain, OpenAI APIs, Azure OpenAI, Amazon Bedrock, or similar technologies is preferred.
- Working knowledge of cloud computing platforms (AWS, Azure, or GCP), APIs, microservices, containers, and modern software development practices.
- Experience with CI/CD pipelines, source control systems, Agile development methodologies, and automated testing.
- Familiarity with databases, data engineering concepts, REST APIs, and software integration.
- Experience working in healthcare, biotechnology, diagnostics, life sciences, or regulated environments is preferred.
Working knowledge of applicable security, privacy, and regulatory requirements relevant to software development, including:
- Cybersecurity fundamentals and secure software development practices
- HIPAA and protection of sensitive data
- FDA\-regulated software environments, software validation, and data integrity principles (preferred)
- Solid software engineering skills with the ability to develop scalable, maintainable, and reliable software solutions.
- Working knowledge of artificial intelligence, machine learning, Generative AI, and modern AI development techniques.
- Strong analytical and problem\-solving skills with the ability to troubleshoot technical issues independently.
- Effective collaboration skills with cross\-functional engineering, product, and business teams.
- Strong written and verbal communication skills with the ability to explain technical concepts clearly.
- Ability to learn new technologies quickly and adapt to changing business priorities.
- Demonstrates ownership, accountability, and commitment to delivering high\-quality software.
- Continuous learning mindset with interest in emerging AI technologies and engineering best practices.
### Working Conditions
- Standard office or hybrid work environment depending on company policy.
- Frequent use of software development environments, AI/ML frameworks, cloud platforms, collaboration tools, and engineering systems.
- May occasionally support production incidents, software releases, or project deadlines outside normal business hours.
- Works independently on assigned projects while receiving guidance and technical direction from senior engineers and engineering leadership.
The expected, full\-time, annual base pay scale for this position is $99\-121k in Menlo Park, CA and $86K\-$105K 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 \[email protected] 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 $86K-$121K range is in the lower quartile 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($103K) sits 52% below the category median. Disclosed range: $86K to $121K.
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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