Careers

Build the operating system for AI answer visibility.

Haynechi is a small product team designing the evidence, workflow, and proof layer marketers will need when AI is the front door to discovery.

Team System

The first team is defined by judgment under ambiguity.

These are open application tracks rather than formal job postings. The bar is the ability to turn a young category into exact product primitives, honest proof, and a surface customers can trust.

Introduce yourself

Application TracksOpen signal

Product engineer

Build prompt, answer, source, agent, approval, and proof primitives that can move from static demo to governed workspace.

workspace

Design engineer

Turn dense operating models into calm product surfaces with exact state language and mobile-safe layouts.

surface

Applied data scientist

Model prompt clusters, answer deltas, citation strength, crawler confidence, and evidence quality without overclaiming causality.

evidence

Customer strategist

Translate buyer urgency into Pilot Maps, source plans, implementation work, and executive readouts.

operator

How We BuildOperating loop

01 Find the object

Start every feature from a real object: prompt, answer, source, crawler event, agent run, approval, or proof event.

02 Design the loop

Define who reviews the evidence, what work gets assigned, and how movement is checked after shipping.

03 Label the state

Keep sample, scoped, implemented, and future-app boundaries visible so ambition stays evidence-led.

04 Tighten the surface

Compress copy, remove filler, test mobile fit, and make the page feel inspectable under executive scrutiny.

PrinciplesWorking style

Calm intensity

Move quickly, but keep the surface measured, technical, and legible.

Evidence before narrative

A sharp category story is earned by prompts, answers, sources, data, and proof.

Taste as a system

Premium design comes from repeated judgment, not decorative effects.

Human approval

Agents accelerate work, but customers keep authority over publication and interpretation.

Interview LoopEvidence based

Signal review

Show how you would improve one answer-operations surface or remove one credibility risk.

Work sample

Pair on a product primitive, source model, proof readout, or pilot workflow.

Founder conversation

Discuss judgment, ambition, pace, and how you think about category creation.

Build Room System

Careers reveal how the company gets built.

This recruiting surface shows the first-builder operating room: weekly build cadence, product surfaces that need ownership, candidate evidence, and current company boundaries.

Inspect architecture

Weekly CadenceFounder-led

Mon Signal review

Read prompt snapshots, citation sources, buyer notes, and route-quality gaps before choosing the week of work.

Tue Primitive build

Ship one product object or page system that makes Answer Map, Agent Runtime, Proof Console, or pilot workflow more real.

Wed Evidence pass

Test the surface against sample data, mobile constraints, executive questions, and explicit proof boundaries.

Thu Operator review

Turn the week's work into a field note, backlog item, pilot artifact, or customer-facing readout.

Fri Quality bar

Remove vague language, check links and generated output, and decide what must be rebuilt before it becomes public proof.

Surface OwnershipProduct primitives

Answer Map

Prompt, answer, competitor, source, sentiment, region, and owner states that explain what AI systems say.

map

Agent Runtime

Supervised runs with inputs, approvals, output artifacts, rollback notes, and human review boundaries.

agent

Proof Console

Observed movement, evidence quality, crawler context, source changes, and confidence labels separated from causality claims.

proof

Pilot Room

Scope packet, buyer goals, action backlog, weekly readouts, export path, and procurement-ready caveats.

pilot

Hiring ReadinessCurrent truth

Open tracks

Application tracks are open for signal, but they are not formal employment postings until scope, budget, and role packet are set.

signal

Role packets

Each future role carries mission, ownership surface, operating cadence, scorecard, compensation philosophy, and interview evidence.

draft

Team shape

The first hires compress product engineering, design taste, data judgment, and customer operating work into a small team.

scoped

Offer readiness

Offers follow legal docs, payroll path, security expectations, and clear manager capacity.

gated

Candidate PacketEvidence over pedigree

Surface critique

Pick a Haynechi page or product primitive and explain what is specific, what feels unsupported, and what deserves rebuild.

Object model

Sketch the prompt, answer, source, agent run, approval, or proof object you would own first.

Proof judgment

Separate observed evidence from inferred influence and name the credibility boundary you would protect.

Build sample

Ship a small artifact, analysis, mock, or workflow that shows taste under constraints rather than a generic portfolio claim.

Team BoundariesTrust-first

Small team reality

The company is still early. The page attracts builders who can operate before a large recruiting machine exists.

Signal before posting

Tracks are signals for future fit and collaboration; formal postings appear when a role is funded and ready.

Focused ambition

Ambition shows up as focus, crisp work, and honest proof rather than chaos, performative hours, or vague intensity.

Customer trust first

Every team member is expected to protect data boundaries, sample labels, approval gates, and measured claims.

Open application tracks

Show the judgment you would bring to answer operations.

Introduce yourself