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NEW · 7D+0
Top hiring location경기도 성남시 분당구 정자일로 239 아이파크분당 102동 801호 (정자동, 아이파크분당)
Last checkedOct 1, 2026
HIRING TELEMETRY · CURRENT OBSERVATION
Read current hiring through measured values
01CURRENT HIRING
5Open jobs
DIRECT4
ADJACENT1
NEW · 7D+0
This is the current job count. It does not by itself establish hiring expansion, contraction or company growth.
02ANALYSIS COVERAGE
This company's postings have not been analyzed yet. Only the current job count and observation time are shown.
03OBSERVATION WINDOW
33DObserved window
Monitoring startedAug 29, 2026
Latest observedOct 1, 2026
COMPARABLE HISTORICAL TRENDNOT YET AVAILABLE
NEW · 7D jobs: 0 How to read confirmed / not confirmed / NEW · 7D
Confirmed means directly stated in current postings. Not confirmed means the item has not been verified in the postings reviewed. NEW · 7D means first observed by Banseog within the last seven days.
WHAT THEY ARE HIRING FOR
Start with your role
CURRENT OPEN JOBS
Current jobs you can inspect now
SystemsRobot System Engineer경기도 성남시 분당구 정자일로 239 아이파크분당 102동 801호 (정자동, 아이파크분당)
Official apply ↗MechanicalMechanical Engineer (R&D)경기도 성남시 분당구 정자일로 239 아이파크분당 102동 801호 (정자동, 아이파크분당)
Official apply ↗Field & IntegrationForward Deployed Robotics Engineer경기도 성남시 분당구 정자일로 239 아이파크분당 102동 801호 (정자동, 아이파크분당)
Official apply ↗AI & MLReinforcement Learning Engineer (전문연구요원 가능)경기도 성남시 분당구 정자일로 239 아이파크분당 102동 801호 (정자동, 아이파크분당)
Official apply ↗Field & Integration[울산지사] Forward Deployed Robotics Engineer대한민국 울산광역시 남구 옥현로 129
Official apply ↗Recurring work experience
Experience repeated in current postings
These are recurring work requirements or experience areas in current job postings.
BANSEOG OUTLOOK
PLAiF always picks the moment one part touches another
Since its founding in 2020, PLAiF has changed the robot body it works with several times, but not the kind of job it chooses. In early 2024 it was attaching an AI controller to existing robots to trial connector insertion on steering parts for a carmaker, and that September it named assembly on electronics lines as its top goal while delivering Rainbow Robotics RB-Y1 robots fitted with its AI. In September 2025 it showed two collaborative robots working as a pair to fasten the connector and dust cover on a car headlamp, and in March 2026 it raised 13.9 billion won to prepare production of its V2 dual-arm robot, built for assembly and fastening. Banseog reads the company's habit as choosing contact work: jobs that sight alone cannot finish and that need fingertip feel and experience of failure. That is why Banseog sees turning failure records from customer lines into training material for the next process as the next talent challenge.
Questions before joining
- How far can failure records from a customer line be used to train the next process?
- How do you measure the on-site time it takes to set up one new assembly process?
- When you move to V2, which of the learned skills from the earlier model must be retrained?
The company says it is developing V2 with Rainbow Robotics, with the arms and waist axis redesigned for the work environment. When the body changes, how much of the earlier model's learned feel can carry over may become important.
The company says its solution can move to other assembly and fastening processes with minimal training and fine tuning, and it expects up to 80% lower setup and reprogramming costs. Measuring how much failure records from one line actually shorten preparation for the next may grow as work.
The company names cars, electronics and home appliances as targets. Criteria that separate learning that carries across industries from learning each industry needs afresh may become important. These are conditional stages, not confirmed dates or hiring plans.
Capabilities gaining importance
Force-control learning for contact workBreaking down assembly and fastening processesTurning failure records into training materialSetting up dual-arm robots on siteDesigning separation of customer line data
Talent markets to explore
Robot reinforcement and imitation learningForce control and tactile sensingAutomotive electrical and electronics assemblyRobot system integrationManufacturing equipment and production engineering
Current hiring comes from the existing verified data pipeline. This is a conditional talent and organization outlook, not a company hiring plan, personal-fit assessment or hiring-probability forecast. · 2026-11-01
FUTURE TALENT SHIFT
How responsibilities may expand
Follow the responsibility that runs from a failure at the moment two parts meet to training material for the next process. Check individual postings for actual duties and qualifications.
Improving task success in demo settings→Defining real line misalignment and error as learning problems
Running paths and motions reliably→Designing force and feel at the moment of contact as inputs to decisions
Confirming customer process conditions→Splitting work into hard contact stages and deciding what to train
Installing and commissioning equipment→Running the path that sorts field failures and returns them to development
Collecting and storing training data→Keeping customer records separate while identifying what can be learned in common
Installing software on a partner's robot→Coordinating what touch-sensing AI requires at the V2 design stage
CAREER LENS
Experience turning a failed assembly into material for the next one
Beyond success rates, look for times you found out why parts did not mate and carried that into the next attempt. Adjacent experience suggests connections, not eligibility or a personal-fit verdict.
Experience to show in your career history
✓
Automating contact work such as connector insertion or screw fastening
✓
Using force sensor or tactile data in control or learning
✓
Sorting failure cases on real equipment and finding their causes
✓
Moving simulation-trained motions to a real robot and closing the gap
✓
Coordinating commissioning schedules around customer line conditions
✓
Making the output of one process reusable in another
Where adjacent experience can lead
Process experience with connectors and wiring can connect to defining assembly problems.
Dealing with defect causes in small, flexible parts is the connection.
Having brought robots up on customer lines can connect to field application roles.
Experience with tolerances, stiffness and gripper design.
Validating reinforcement or imitation learning research on real equipment.
Sorting defects by type and tracing causes can connect to designing failure records.
Questions before joining
- How far can failure records from a customer line be used to train the next process?
- How do you measure the on-site time it takes to set up one new assembly process?
- When you move to V2, which of the learned skills from the earlier model must be retrained?
- Is this role evaluated on demo success rates or on line operating results?
- How often do field engineers and AI researchers review failure cases together?
PLACE × TALENT INTELLIGENCE
Work connected across locations
These interpretations use publicly reported locations and applications. They do not describe regional hiring plans or current staffing.
The company's address is in Jeongja-dong, Bundang-gu, Seongnam. This reads as where its touch-sensing AI and learning system are built, with no estimate of current team structure or size.
Robot learningControl softwareProduct design
Steering part connector insertion trials and a headlamp connector and dust cover solution are public. Customer names are not disclosed; check postings for actual placement.
Connector fasteningCommissioningProcess analysis
In 2024 the company was reported to have named electronics assembly as its top goal, with trials at large companies. This reads as where learning across industries is tested, without presuming results.
Fine assemblyDual-arm workHandling line variation
The partnership runs from a 2024 agreement through the 2025 headlamp solution to V2 in 2026. This reads as the point where touch-sensing AI's needs become design conditions, without presuming how roles are divided.
Hardware coordinationWaist axis designAI integration
Contact work on customer lines → failure records → touch learning → V2 dual-arm robot → setup of the next process