All remote jobs

AI Tooling Engineer

AI Summary
Qventus is seeking an early-career AI Tooling Engineer to build and deploy internal AI tools, agents, and workflow automations for business teams. The role focuses on rapidly discovering operational bottlenecks, integrating APIs and SaaS systems, and iterating from real user feedback rather than conducting AI research. The engineer will work with operations, customer success, finance, people, IT, and data teams, with direct exposure to the CEO and founders. Core technical requirements include TypeScript or Python, LLM application delivery, APIs, webhooks, and asynchronous workflows. Success is measured by shipping adopted internal tools quickly and establishing reliable LLM evaluation and deployment practices.
Remote from
USA
Salary
USD 120k–150k / yr
Employment
Full Time
Experience
Director
Published
Apply before
10 Dec 2026
Listing views
50
Application actions
2
Application toolkit

Make your next move.

Prepare your resume, explore your fit, and draft a cover letter for this opportunity.

Opportunity details

About this role.

On this journey for over 12 years, Qventus is leading the transformation of healthcare. We enable hospitals to focus on what matters most: patient care. Our innovative solutions harness the power of machine learning, generative AI, and behavioral science to deliver exceptional outcomes and empower care teams to anticipate and resolve issues before they arise.

Our success in rapid scale across the globe is backed by some of the world’s leading investors. At Qventus, you will have the opportunity to work with an exceptional, mission-driven team across the globe, and the ability to directly impact the lives of patients. We’re inspired to work with healthcare leaders on our founding vision and unlock world-class medicine through world-class operations. #LI-MB1

About the Role

We’re hiring Engineers dedicated to building AI-powered tools that change how work gets done inside the company. You’ll embed with teams across operations, customer success, and finance, find the work that’s slow and manual, and ship AI agents and automations that actually get adopted.

This is a build-fast, talk-to-humans, own-the-problem role – not a research role and not a pure-infrastructure role. You’ll define what “internal AI” looks like here from scratch.

Who this is for

You’re energized by ambiguity and ownership. You’d rather ship a rough tool this week, watch someone use it, and improve it than spend a month on a perfect spec. You like sitting with a non-technical teammate, understanding their actual workflow, and turning it into something that saves them hours. You’re comfortable being the only person who owns a problem end to end.

Who you will work with

  • You’ll partner closely with the CEO and founders on identifying the highest-leverage operational problems to solve and shaping where internal AI goes next. This is a rare amount of executive access to an engineering role.
  • You’ll work alongside functional leaders across the business to understand their priorities, surface opportunities , and align on what to build. Your work will be visible and leadership level from day one.
  • Operations, Customer Success, Finance, People/Talent Acquisition, Engineering, IT and Data teams, these are your internal customers and collaborators. You’ll embed with them, and learn their workflows and ship tools they actually use.

What you’ll actually build

Examples of the kind of work waiting for you:

  • An agent that drafts customer success replies from our knowledge base and ticket history
  • A Slack-based workflow that routes and pre-fills finance approvals
  • An internal eval harness so we can trust the LLM features we ship
  • A tool that lets the ops team query [internal system] in plain language

You won’t do all of these at once — you’ll pick the highest-impact one, ship it, and move to the next.

What we’re looking for

  • 1–3 years building software — backend services, internal tools, automations, or API-driven apps. Internships and co-ops count.
  • Strong in TypeScript and/or Python.
  • You’ve built something with LLMs — an agent, a RAG pipeline, a copilot, a prompt-driven tool — through work, a side project, or coursework. We care that you’ve shipped, not where.
  • Comfortable wiring systems together: APIs, webhooks, SaaS integrations, async workflows.
  • You can talk to non-technical stakeholders, figure out what they really need, and drive adoption — this matters as much as the code.
  • You move fast under ambiguity and iterate from real feedback.

Bonus, not required

  • Built copilots, agents, or workflow automations before
  • Used an orchestration framework (LangChain, Temporal, or similar)
  • Worked with vector databases or retrieval systems
  • Integrated with Salesforce, Slack, Zendesk, Notion, or Jira
  • Startup / high-growth experience

What success looks like

  • First few weeks: shipped your first internal tool and it’s in active daily use by a business team.
  • Within 2 months: 2–3 tools live, and stakeholders are coming to you with ideas.
  • By month 3: you’ve set the team’s baseline practices for evals, prompt management, and safe LLM deployment — the foundation the function grows on.

Compensation for this role is based on market data and takes into account a variety of factors, including location, skills, qualifications, and prior relevant experience. Salary is just one part of the total rewards package at Qventus. We also offer a range of benefits and perks, including Open Paid Time Off, paid parental leave, professional development, wellness and technology stipends, a generous employee referral bonus, and employee stock option awards.

Salary Range

$120,000—$150,000 USD

Qventus values diversity in its workforce and proudly upholds the principles of Equal Opportunity Employment . We welcome all qualified applicants and ensure fair consideration for employment without discrimination based on any legally protected characteristics, including, but not limited to: veteran status, uniformed service member status, race, color, religion, sex, sexual orientation, gender identity, age, pregnancy (including childbirth, lactation and related medical conditions), national origin or ancestry, citizenship or immigration status, physical or mental disability, genetic information (including testing and characteristics) or any other category protected by federal, state or local law (collectively, “protected characteristics”). Our commitment to equal opportunity employment applies to all persons involved in our operations and prohibits unlawful discrimination by any employee, including supervisors and co-workers.

Qventus participates in the E-Verify program as required by law and is committed to providing reasonable accommodations to individuals with disabilities in compliance with Americans with Disabilities Act (ADA). In compliance with the California Consumer Privacy Act (CCPA), Qventus provides transparency into how applicant data is processed during the application process. Candidate information will be treated in accordance with our candidate privacy notice.

*Benefits and perks are subject to plan documents and may change at the company’s discretion.

*Employment is contingent upon the satisfactory completion of our pre-employment background investigation and drug test.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

4/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThe position requires end-to-end delivery of production-oriented LLM tools amid high ambiguity, often as the sole owner of a problem. It also demands strong stakeholder discovery and rapid iteration across multiple business functions.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Highly competitive
$135,000
US market range (per year)$115k–$155k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer explicitly lists a yearly salary range of $120,000–$150,000 USD. The midpoint is $135,000 per year; the estimated US market range for an early-career engineer building internal AI tools is $115,000–$155,000 per year.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
Describe an LLM-powered tool you shipped and how you measured whether it was useful.

I would explain the user problem, the LLM workflow and integrations, and the safeguards I implemented. I would measure usefulness through adoption, task-completion time, user feedback, accuracy or acceptance rates, and a comparison with the prior manual process.

How would you approach building a Slack workflow for finance approvals?

I would first map the current approval process with finance users, identify required systems and decision points, then build a small Slack-based prototype using APIs and webhooks. I would add role-based access, validation, audit logging, error handling, and feedback loops before expanding its scope.

What practices would you use to evaluate an internal AI agent before production release?

I would create a representative evaluation dataset, define task-specific quality and safety criteria, and test expected, edge-case, and adversarial inputs. I would combine automated scoring with human review, log production outcomes, and continuously refine prompts, retrieval, and guardrails.

How do you handle ambiguous requests from non-technical stakeholders?

I start by observing the actual workflow and asking about decisions, inputs, exceptions, desired outcomes, and the cost of mistakes. I restate the problem in measurable terms, propose a narrow first version, and validate it with users quickly before investing in a larger solution.

When would you use retrieval-augmented generation instead of relying only on a model prompt?

I would use RAG when responses need grounded, current, or organization-specific information that is too large or changeable for a prompt. I would ensure retrieval quality through curated documents, metadata, access controls, citations where useful, and evaluations of answer accuracy and relevance.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Next step

Apply now.

Follow the employer’s application method and review Jobicy’s safety guidance before sharing personal information.

Did you apply?Let us know, and we’ll help you track your application.

Continue on the employer website

Protect your personal information and never pay to secure an interview or job offer. .

Log in to save
One quick step before you apply

Sign in to continue.

Sign in or create a free account to continue to the employer's application.

Add alert
Jobs Talent AI Tools Salaries
Menu