Forward Deployed Engineer, MT Labs
Posted yesterday
The role in short
Embed with McKesson teams to identify high-value problems and build, deploy, and operate secure, production-ready full-stack solutions and AI agents.
McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve – we care.
What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow’s health today, we want to hear from you.
About the Role
McKesson Technology Labs is looking for a Forward Deployed Engineer who combines deep software engineering expertise with curiosity about how work happens in the real world. You will spend time with the McKesson teams you build for, uncover high-value problems, and turn ambiguous needs into secure, production-ready solutions.
This is a hands-on senior engineering role. You will design, code, test, deploy, and operate full-stack products that may include user interfaces, APIs, services, data pipelines, enterprise integrations, and applied AI capabilities. You will also provide technical direction, mentor engineers, and lead a delivery pod when the work requires it.
You will help determine when McKesson should use an existing product, compose available capabilities, or build a new solution based on quality, risk, cost, and measurable outcomes.
What You’ll Do
- Work directly with McKesson teams to observe workflows, identify edge cases, and understand where material cost, risk, and opportunity exist.
- Translate ambiguous business problems into clear technical plans and test the highest-risk assumptions through focused prototypes or minimum viable solutions.
- Design, build, integrate, test, deploy, and operate full-stack applications, APIs, services, data pipelines, and enterprise system integrations.
- AI where it improves outcomes, including large language models, retrieval-augmented generation, tool calling, AI agents, and evaluation frameworks.
- Build evaluation datasets from representative historical work, measure solution quality, and identify failure modes before production deployment.
- Engineer security, privacy, compliance, monitoring, observability, and incident response into solutions from the beginning.
- Own technical direction, make sound architecture decisions within enterprise guardrails, and use AI coding tools as part of daily engineering work.
- Mentor engineers, contribute reusable patterns and tools, and transfer proven solutions with the documentation and operational readiness needed for long-term support.
Basic Requirements
- 10+ years of relevant professional experience in software engineering, automation engineering, solutions engineering, or a related technical field; less experience may be considered with relevant advanced education.
- Demonstrated experience designing, coding, deploying, and supporting production software solutions.
- Full-stack engineering experience across user interfaces, backend services, APIs, data processing, and system integrations.
- Experience leading technical design or architecture for complex, cross-functional initiatives.
- Experience with cloud platforms, CI/CD, version control, automated testing, and production observability.
- Demonstrated ability to translate unclear business requirements into practical technical solutions.
- Experience incorporating security, privacy, reliability, and compliance requirements into software delivery.
- Bachelor’s degree in computer science, engineering, information systems, or a related field, or an equivalent combination of education and professional experience.
Preferred Skills/Experience
- Experience building AI-enabled products using LLMs, retrieval-augmented generation, tool calling, agents, or model evaluation techniques.
- Healthcare, pharmacy, or other regulated-industry experience, including exposure to PHI or FHIR, HL7, NCPDP, or X12.
- Forward-deployed, customer-facing, field, or solutions engineering experience.
- Startup, founding engineer, or zero-to-one product development experience.
- Depth in cloud architecture, DevSecOps, APIs, distributed systems, or data engineering.
- Experience creating evaluation datasets, quality metrics, monitoring, and feedback loops for AI-enabled systems.
- Experience mentoring senior engineers or providing technical leadership within a product or delivery pod.
- Experience evaluating build-versus-buy options using quality, risk, operating cost, and business outcomes.
Travel, Work Environment, and Physical Requirements
- Regular interaction with the McKesson teams and operational environments supported by this role is expected.
- Travel percentage and remote, hybrid, or on-site expectations must be confirmed by the hiring leader before publication.
- The role requires routine use of a computer and collaboration through in-person and virtual working sessions.
We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson,
McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson’s (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind:
McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application.
McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates.
McKesson job postings are posted on our career site: careers.mckesson.com.
McKesson is an Equal Opportunity Employer
McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson’s full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page.
McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us by sending an email to (United States) Disability_Accommodation@McKesson.com or (Canada) Accessibility@mckesson.ca. Resumes or CVs submitted to this email box will not be accepted.
Join us at McKesson!
Prep for this interview
5 questions likely to come up at McKesson, each matched to a line in this posting.
- 1Integrations and APIs
They wrote: “Design, build, integrate, test, deploy, and operate full-stack applications, APIs, services, data pipelines, and enterprise system integrations.”
Walk me through connecting our product to a customer's internal system that has no documentation.
How to answer
Why they ask: This happens weekly in the job. They want a calm, step-by-step method.
- Find who owns the system and get read access to a test environment
- Watch real traffic or logs to learn the data shapes
- Build a thin adapter with logging around every call
- Agree on how failures get reported, and who fixes what
Tip: Talk about finding the owner of the system first. The code is the easy part.
- 2Cloud, deployment and security
They wrote: “Engineer security, privacy, compliance, monitoring, observability, and incident response into solutions from the beginning.”
The customer's security team sends a 200-question review. How do you handle it?
How to answer
Why they ask: Security reviews often block deals. FDEs help get through them.
- Start from your company's standard answers (SOC 2 report, past reviews)
- Answer clearly; don't oversell
- Bring in your security team for anything you're unsure of
- Keep the new answers for the next customer
Tip: Reuse past answers, flag honest gaps, and offer a call for the hard questions.
- 3Discovery and scoping
They wrote: “Translate ambiguous business problems into clear technical plans and test the highest-risk assumptions through focused prototypes or minimum viable solutions.”
A customer says “we want AI in our claims process.” What do you ask in the first meeting?
How to answer
Why they ask: Most FDE work starts this vague. They want to hear you find the real problem before you pick a tool.
- Map the current process: who touches a claim, in what order, with which systems
- Find the slowest or most error-prone step and ask what it costs them
- Agree on one measurable goal (for example time per claim, or error rate)
- Ask what data you can access, and who signs off on security
Tip: Ask about the work, not the technology. Who does it today, how long it takes, what a mistake costs, and how they'd know it got better.
- 4LLMs, RAG and agents
They wrote: “AI where it improves outcomes, including large language models, retrieval-augmented generation, tool calling, AI agents, and evaluation frameworks.”
A customer wants a chatbot over their internal documents. How do you build it?
How to answer
Why they ask: RAG is the most common first project. They want the whole path, from messy files to answers people trust.
- Collect and clean the documents; handle permissions per user
- Split them into chunks, embed, and store with source links
- Retrieve, then answer with citations; say “I don't know” when retrieval is weak
- Build a test set of real questions with expected answers before launch
Tip: Spend more time on the data and on testing than on the model.
- 5Coding
They wrote: “10+ years of relevant professional experience in software engineering, automation engineering, solutions engineering, or a related technical field; less experience may be considered with relevant advanced education.”
Build a small in-memory cache. Then add expiry. Then a size limit.
How to answer
Why they ask: Staged exercises show how your code holds up when requirements change mid-interview, like at a customer.
- Start with get and set on a dictionary, plus tests
- Store an expiry time with each value and check it on read
- Track use order and evict the least recently used entry when full
- Say what you'd change for many threads or many machines
Tip: Keep the first version simple and well tested so the next stage is a small change.
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