Principal Product Manager, Agentic AI

3 дн. назад

СШАLeadRemote

Lead product work for building AI agents in workforce management, focusing on operational research, specification, prototyping, evaluation, and enterprise deployment with a person-in-the-loop approach.

О компании

Join 's mission to turn hourly jobs into good jobs. We're a remote, mission-driven team seeking exceptional talent to propel this vision. Embrace a culture that's collaborative, fast-paced, and entrepreneurial. With us, you'll grow your skills, work closely with experienced executives, and contribute significantly to our mission.
Technologies delivers the industry’s most innovative workforce management platform. It enables businesses to maximize labor efficiency and employee engagement simultaneously. The award-winning, AI-driven WFM platform is intelligent, automated, and employee-centric. It’s proven to deliver 13x ROI through schedule optimization, reduced attrition, increased productivity, and increased operational efficiency. delivers cutting-edge technology in an easy-to-use platform and mobile app that employees love.
If you're ready to make an impact and grow your career, is where you belong. Join us in making hourly work rewarding and fulfilling.

Обязанности

Agent definition. Determine what an agent is responsible for, which tools it may call, what it must escalate, what it must never do, and what a manager sees when it acts. These decisions affect employee pay and legal compliance. Much of this work is pre-launch, so you will shape the direction rather than inherit it.
Problem selection. The hardest judgment in agentic product work is identifying which workflows suit an agent and which do not. You will spend substantial time inside customer operations — district managers, store managers, labor operations, frontline employees — and return with a ranked, defensible view of where an agent creates measurable value and where it does not.
The human-in-the-loop contract. Scheduling decisions affect income, childcare arrangements, and legal protections. For each agent action you will define where autonomy is appropriate, the escalation thresholds, how a manager overrides the system, what is logged, and how a compliance team audits the outcome. treats this as a first-class design problem.
Build quality. You are accountable for what ships, not for what was specified. That means staying engaged through implementation: reviewing the build against the specification as it comes together, using the product yourself before anyone else is asked to, joining bug triage and design review, catching drift from intent while it is still cheap to correct, and holding a release when the quality is not there. Our customers experience the build, not the document. A well-written specification that produced a product managers do not trust is failed product work, and we assess it that way.
Evaluation. Build and maintain the evaluation sets that determine whether an agent is ready to ship and whether it is degrading in production — writing the cases, reviewing failure traces, setting the pass criteria. You will be the person who holds a release when the demonstration is persuasive and the evaluation is not.
Specifications. uses Functional Requirements Documents as its ground-truth specification layer; release notes, enablement material, and sales content are generated from maintained specifications rather than reconstructed from code or institutional memory. Write FRDs that meet that standard and keep them accurate after release. A specification is where the work begins, not where your responsibility ends.
Commercialization. Partner with go-to-market on packaging, metering, pricing inputs, and field readiness. Agentic products raise difficult commercial questions — what unit to meter, what a customer is paying for when software performs work a person previously did — and you will be expected to hold a considered position on them.
Design-partner delivery. Lead agent deployments with large enterprise design partners: scoping the pilot, establishing success criteria before it begins, joining failure reviews, and determining what becomes generally available and what remains bespoke.

Требования

7+ years in product management (Senior) or 10+ years (Principal), including at least 2 years shipping LLM-backed features to external, paying users — not internal tools, proofs of concept, or hackathon projects
Demonstrated hands-on capability of the kind described above. Expect to show your work.
Substantial enterprise B2B SaaS experience: multi-tenant architecture, role-based access control, audit trails, single sign-on, data residency, security review, and enterprise procurement. You should be able to anticipate what a customer's security questionnaire will ask about an LLM feature.
A record of taking an ambiguous, operationally complex domain problem through to a shipped product with demonstrated adoption — not only launch
Evidence of staying with a product through implementation and owning its quality at release. We will ask about a release you delayed or stopped, what you found, and how you found it.
The judgment to recognize what current models cannot do reliably, and the willingness to say so when a room expects a different answer
Comfort working inside a codebase. An engineering background is not required, but you should be able to read the code behind what you are specifying, run it locally, and open a pull request.
Strong written communication. runs on documents, and candidates are evaluated substantially on how they write.

Будет плюсом

Direct experience in workforce management, HCM, ERP, supply chain, or another operationally complex enterprise domain with compliance exposure
Familiarity with labor compliance regimes — predictive scheduling ordinances, meal and rest break rules, union work rules — or demonstrated ability to become fluent in a comparable regulatory domain quickly
Background in demand forecasting, optimization, or decision systems
Experience building platform or authoring surfaces that customers configure themselves
An engineering or technical degree, or prior experience as an engineer or data scientist
Experience with products sold into frontline-heavy industries: retail, hospitality, healthcare, or manufacturing

Условия

At , we offer competitive compensation and benefits packages to all employees. As a fully remote employer, pay for positions is determined using local, national, and industry-specific survey data.
Our posted salary range is done so in good faith based on national data and may be refined for a candidate's region/town/cost of living. We strive to make competitive offers that allow employees room for future growth. Salaries will be based on the applicant’s location, level of experience, education, and specialized knowledge and skills. Additionally, we consider the external market rate, the amount we have budgeted internally, and the internal equity for the same position within the company.
Benefits include, but are not limited to:
$0 monthly premium and other flexible medical, dental, and vision plans effective on the first day of employment
401k plan
Discretionary Paid Time Off and Paid Holidays
Parental Leave
Equity
Monthly Wellness Reimbursement
Monthly Lunch on

Другое

is building AI agents that carry out workforce management work, not agents that describe it.
Much of what is presented as AI in workforce management today is a conversational layer over an existing scheduling screen. The more substantial problem — software that reasons over demand, labor regulation, and employee preferences, then acts, with a person in the loop wherever the stakes require one — remains unsolved across the category. intends to solve it, and this role leads the product work to get there.
You will take an agent surface from operational research through specification, prototype, evaluation, and enterprise deployment, working hands-on throughout. You will work closely with 's AI Lab engineers and with our CEO, who is engaged in this work daily.
is looking for a product manager who builds, rather than one who directs others who build. In a typical week, this role involves:
Writing and iterating on prompts and system prompts directly, and explaining why a change improved a behavior
Standing up a working prototype of a proposed agent — tool definitions, structured outputs, retries, guardrails — before engineering is asked to build anything
Using agentic coding tools (Claude Code or equivalent) daily: prototyping, data analysis, specification work, and reading the codebase behind your specifications
Reviewing an execution trace and diagnosing whether a failure originates in a tool description, a retrieval gap, a context-window constraint, an under-specified output schema, or a genuine model limitation
Building and running evaluation sets, and interpreting the results without a data scientist to translate them
Holding an informed position on retrieval design, tool-use patterns, structured output, model routing, latency and cost trade-offs, and multi-tenant safety in enterprise SaaS
Opening pull requests against our codebase — a prototype, a prompt change, an evaluation fixture, a specification stub. Pushing code is permitted and actively encouraged for product managers who want to work that way. It is not a requirement of the role, and the same review standards apply to everyone.
Following model releases, agent frameworks, and protocol changes, and translating them into roadmap implications within days rather than quarters
We recognize that this is a demanding technical bar for a product role. It reflects what the work actually requires.
So that candidates can assess fit early:
This is an individual contributor role. There is no team to inherit and no management responsibility at present.
The role is accountable for what ships, not for what is specified. Writing the specification is a portion of the work, not the whole of it.
This is not an AI strategy or vendor-evaluation role. The work is product definition, prototyping, specification, evaluation, and delivery.
The role requires forming an independent point of view from operational research. Direction will not be handed down in detail.
The environment carries genuine ambiguity. The category is being defined as we work in it, and model capabilities shift underneath the roadmap.
Our process includes a practical exercise: a sanitized workforce management problem, for which candidates produce an agent design — scope, tools, failure modes, human-in-the-loop contract, and evaluation plan — together with a working prototype they built themselves, using any model provider they prefer. The exercise is then discussed with the people the candidate would work alongside. We weigh it heavily, and we compensate candidates for the time.
There are almost 75 million hourly workers in the United States, representing more than half of the entire workforce. Historically, managing hourly employees has been difficult due to high attrition (average of 60%) and high replacement costs (average of $3,200 per employee in retail). The ongoing labor shortage and competition from the gig economy make it more difficult to attract and retain hourly employees. The top reasons hourly employees leave their jobs are a lack of schedule empowerment, poor communication with employers, and an inability to get paid early. Gen Z and the millennial workforce demand gig-like flexibility, modern technology, and compelling work options. ’s mission is to turn hourly jobs into good jobs, serving the hourly workers who make up the majority of the US workforce. We believe in empowering employees and helping employers be efficient and innovative by enabling intelligent automation powered by ’s Workforce Management platform to optimize labor efficiency and enhance the employee experience simultaneously. WFM was built for the cloud with AI at the core and designed to handle the complexity of modern businesses and meet the needs of today’s hourly employees. Our team is comprised of dedicated individuals from all backgrounds and experiences, globally distributed across all time zones.
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As a global employer, determines pay for positions using local, national, and industry-specific survey data. We evaluate external equity and the cost of labor/prevailing wage index in the relative marketplace for jobs directly comparable to jobs within our company. Our posted salary range is based on national data and may be refined for a candidate's region/town/cost of living. For new hires, we strive to make competitive offers allowing the new employee room for future growth. Salaries will be based on the applicant’s location, level of experience, education, and specialized knowledge and skills. Additionally, we consider the external market rate, the amount we have budgeted internally, and internal equity within the company for the same position. An employee/candidate with a stronger skill set will receive higher pay.
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