AI Engineer (Personalized & Proactive Agent)

42dot
South Korea
Workplace: HybridFull timeFunction: Data Science & Machine LearningExperience: 4+ yearsEducation: mastersSkills: ["Ownership","Collaboration","Problem-solving","Experimentation"]

Build LLM-based personalized and proactive in-vehicle assistant experiences. Design long-term memory, retrieval, personalization, and context engineering under latency and context-window constraints. Create proactive agent behaviors (recommendations, reminders, briefings) using context signals like location and driving state. Improve quality via closed-loop offline evaluation and online A/B testing, and implement production-ready AI/ML systems using Python or Kotlin.

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FursaFursa
42dot
42dot
1 day ago

AI Engineer (Personalized & Proactive Agent)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 3 hours agoStatus: Live

Job Summary

Build LLM-based personalized and proactive in-vehicle assistant experiences. Design long-term memory, retrieval, personalization, and context engineering under latency and context-window constraints. Create proactive agent behaviors (recommendations, reminders, briefings) using context signals like location and driving state. Improve quality via closed-loop offline evaluation and online A/B testing, and implement production-ready AI/ML systems using Python or Kotlin.
Location: South Korea
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Build long-term memory, retrieval, personalization, and a learning loop that adapts using user experience, preferences, and context.
  • •Design context engineering and context summarization strategies to select and compress memory/context within limited context windows and latency constraints.
  • •Develop proactive agent experience that generates and ranks candidate places/media from preference, routines, schedules, location, and driving context, and provides dialogue, notifications, reminders, and briefings at the right time.
  • •Create closed-loop evaluation using offline metrics and online A/B experiments to validate personalization and proactive interventions, define quality/impact metrics, and continuously improve memory/recommendation/intervention policies.
  • •Experiment with recent research and combine LLMs, ML/statistical models, and rule-based systems to deliver stable production capabilities.

Key Requirements

  • •M.S. or higher in Computer Science, AI, or related fields, with 4+ years of hands-on experience in AI/ML or software engineering.
  • •Experience applying core principles of LLMs and agentic AI to real-world services.
  • •Analyze real user logs and data to discover problems and define improvement direction and priorities.
  • •Ability to structure uncertain problems through hypothesis, experiments, and evaluation.
  • •Strong software engineering skills to design, implement, and operate production-grade AI/ML systems (Python preferred; Kotlin preferred).
Experience:4+ yearsAI/MLLLMAgentic AI
Education:Master's
Skills:OwnershipCollaborationProblem-solvingExperimentation
Tech Stack:LLMAgentic AIPythonKotlinMachine learningContext engineeringA/B testingContextual banditRankingRetrievalRecommendation systems

Company Brief

42dot
Develops autonomous driving and mobility software platforms, including AI-based perception, mapping, routing, and connected-vehicle technologies. It works on next-generation transportation systems and self-driving vehicle capabilities for automotive applications.
Industry: Autonomous Vehicles
Company Size: Large (251 to 1,000 employees)
Growth: Scaleup
Headquarters: Seoul, South Korea
Founded: 2019
WebsiteLinkedIn