Senior Applied AI Engineer
Posted yesterday
The role in short
Own the AI layer of production systems for public sector clients by building agents, designing evaluations, and managing client expectations on AI performance.
About the role
Scale's Global Public Sector team is focused on using AI to address critical challenges facing the public sector around the world. As a Senior Applied AI Engineer, you'll own the AI behaviour of production systems our clients depend on, including the agents, evaluations, and backend integration behind them. You'll lead delivery for your workstream and act as the technical point of contact for clients on how their AI performs on live data.
What you'll do
- Own the AI layer of production systems for public sector clients, including agents, model integration, and the backend services they run on
- Build production agents from established architectural patterns, with reliable tool calling, guardrails, and observability
- Select and adapt established evaluation methods, and design new evaluations for client-specific risks
- Apply context engineering, retrieval-augmented generation, model adaptation, or inference optimisation
- Investigate regressions in the systems you own, lead root-cause analysis, and implement preventative changes
- Lead project planning for your workstream and manage client expectations on scope, risk, and delivery
- Codify recurring client work into reusable tooling, evaluation components, or guidance that other engineers adopt
- Mentor engineers through code review, pairing, and delegated ownership
What we look for
- Backend engineering experience, with a track record of taking AI/ML systems into production and keeping them reliable
- Ownership of the AI behaviour of a production system end to end, rather than delivering a component scoped by another team
- Hands-on evaluation design that ties model behaviour to real-world outcomes, used to catch regressions
- Comfort working directly with clients, including explaining model behaviour and trade-offs to non-technical stakeholders
- Practical knowledge of handling regulated or sensitive data and designing for auditability; sovereign or on-premise deployment experience is valuable
- Experience leading root-cause analysis on production AI or ML systems
Where this role sits
Your primary accountability covers production AI behaviour, its backend integration, and the evaluation required for a specific customer. Senior Full-Stack Engineers own the complete user-facing application and its infrastructure. ML Research Engineers lead novel agent architecture and the benchmark methodology used across accounts. The roles work together on agents and evaluation, with accountability set by the scope of the problem.
***PLEASE NOTE:***Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.
About Us:
At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst& Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.
We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.
We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information.
We comply with the United States Department of Labor's Pay Transparency provision.
PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information.
Prep for this interview
5 questions likely to come up at Scale AI, each matched to a line in this posting.
- 1Discovery and scoping
They wrote: “Lead project planning for your workstream and manage client expectations on scope, risk, and delivery”
How do you decide what to build first when a customer wants ten things?
How to answer
Why they ask: FDEs have limited weeks per customer. This tests judgment about value, effort and risk.
- Score each request by business value and by effort, with the customer in the room
- Pick one item that proves value fast and unblocks later work
- Write down what's out of scope so it doesn't creep back in
- Revisit the list after the first release, with real usage data
Tip: Show a simple way to rank the list and name what you'd leave out on purpose.
- 2Integrations and APIs
They wrote: “Own the AI layer of production systems for public sector clients, including agents, model integration, and the backend services they run on”
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.
- 3Cloud, deployment and security
They wrote: “Practical knowledge of handling regulated or sensitive data and designing for auditability; sovereign or on-premise deployment experience is valuable”
A bank wants our product inside their own cloud account, with no data leaving it. How do you approach it?
How to answer
Why they ask: Self-hosted and private-cloud deployments are common in regulated industries.
- Learn their rules early: network limits, approved services, review process
- Package the product so it installs the same way each time (containers, Terraform)
- Plan how you'll get logs and updates without touching their data
- Run a test install with them before the real one
Tip: Talk about the conversation with their security team as much as the setup.
- 4Coding
They wrote: “Ownership of the AI behaviour of a production system end to end, rather than delivering a component scoped by another team”
How do you keep quality high when you ship fast at a customer site?
How to answer
Why they ask: Speed is the job, but broken code at a customer is costly. They want your guardrails.
- Tests for the parts that touch customer data or money
- Code review even when it's fast, ideally by someone on the product team
- Feature flags and an easy way to roll back
- Clean up the shortcuts in the next release, and write them down
Tip: Name the few checks you never skip, and what you skip on purpose.
- 5Ownership and working with customers
They wrote: “Mentor engineers through code review, pairing, and delegated ownership”
Tell me about something you shipped that broke in production.
How to answer
Why they ask: Everyone has broken production. They want to hear you own it and change something afterwards.
- What broke and who it affected
- How you found out and what you did in the first hour
- The root cause, said plainly
- What you changed so it can't happen the same way again
Tip: Pick a real one with a cost. Spend most of the answer on what you changed.
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