Machine Learning Engineerr | (TokyoDev)

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Machine Learning Engineerr | (TokyoDev)

The Problem Job matching is still keyword-based. "Python engineer" "Python job." Were building something fundamentally different: AI that matches people based on values, aspirations, and behavioral fit, not just skills. Our system explains why someone would thrive at a company andwhy a company should hire them-not just that requirements are met. Not recommendations. Meaning-based matching.

We have 200 paying companies and thousands of active users. The matching works. Now we need to make it exceptional.

What Youll Build

### 1. Memory Layer for Continuous Learning Users have conversations with our AI over weeks and months. Youll build a system that:

• Extracts meaningful information from unstructured conversations (goals, values, preferences, work history)
• Stores both structured facts and semantic understanding
• Retrieves relevant context in <100ms to power personalized matching
• Handles evolution: People change jobs, preferences shift-the system should track this Challenge: This is novel territory. Memory extraction from conversational AI at scale hasnt been solved. Youll design it from scratch.

### 2. Explainable Matching Engine Generate recommendations with real explanations: "Company X fits because they prioritize remote work (you mentioned this last week) and have a strong mentorship culture (aligns with your leadership style)." Challenge: Make LLM explanations trustworthy. No hallucinations, grounded in actual data, consistent across users.

### 3. Hybrid Matching Architecture Design and implement matching that combines LLMs, rule-based logic, and traditional ML where each excels. Not everything needs a neural net. Challenge: Know when to use deterministic rules vs. learned models. Optimize for explainability and cost, not just accuracy.

### 4. Company Knowledge Structures Build semantic representations of company information-business model, culture, hiring requirements, and implicit knowledge that doesnt appear in job descriptions. Challenge: Scale from 200 partner companies to 1,500+ with quality data sourcing, fast retrieval, and cost optimization.

### 5. Training Data & Evaluation Design what to label, how to label it, and what "good" means. Build evaluation frameworks for subjective quality (Is this match good? Is this explanation helpful?). Run A/B tests. Benchmark models. Challenge: Extract value from small datasets. You have 200 companies, not 200,000-design learning strategies that work with limited examples.

How Youll Work

• Cross-functional collaboration: Youll work directly with Product Managers and Business Development in rapid hypothesis implementation verification cycles.
• Two-sided optimization: Your models must satisfy both job seekers AND hiring companies. User satisfaction and company hiring decisions are both success metrics.
• LLM strategy design: Beyond calling APIs, youll design prompt strategies and learning approaches based on inference results. Meta-level thinking about how to use LLMs effectively.

Welcome Skills

• Total ownership: You define the ML vision, architecture, and roadmap
• Build the team: Hire and lead future ML engineers as we scale
• Founding engineer impact: Significant equity and organizational influence
• Solo execution initially: Can self-direct without needing ML peers (for now)

Reality check:

• Youll be the only ML engineer for ~6 months until we hire #2
• No ML peers to review your code or validate decisions
• CTO provides infrastructure support but isnt an ML specialist
• Small data environment: 200 companies, not 200,000
• Two-sided marketplace: optimizing for users AND companies
• This is high autonomy + high responsibility
• If you thrive with ML collaboration, this isnt the right fit yet

Application Overview

• Salary Range: ¥10,000,000 to ¥15,000,000 Negotiable
• Location: Japan: 7F, Tohshin Aoyama Building, 2-10-13 Shibuya, Shibuya-ku, Tokyo, Japan; US: 651 N. BROAD ST., SUITE 201, MIDDLETOWN DE 19709
• Employment Conditions:
• Full-time ()
• Original working hours
• Holidays:
• Annual Holidays: 2 days off per week (Saturdays and Sundays), national holidays, year-end and New Year holidays
• Paid Annual Leave: 10 or more days per year (varies depending on the month of joining)
• Probation Period: 3 months
• Benefits:
• Health Insurance
• Welfare (Pension) Insurance
• Employment Insurance
• Workers Compensation Insurance
• Commute allowance: up to ¥30,000 per month
• Stock Option Rewards System: According to company regulations
• Visa Support