Machine Learning Engineerr | 機械学習 エンジニア (TokyoDev)

Machine Learning Engineerr | 機械学習 エンジニア (TokyoDev) Problem Job matching is still keyword-based. "Python engineer" → "Python job." We are 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 and why 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 You'll Build 1. Memory Layer for Continuous Learning Users have conversations with our AI over weeks and months. You'll 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 Handles evolution: People change jobs, preferences shift—the system should track this Challenge: This is novel territory. Memory extraction from conversational AI at scale hasn't been solved. You'll 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 doesn't 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 You'll Work Cross-functional collaboration: You'll work directly with Product Managers and Business Development in rapid hypothesis → implementation → verification cycles. This isn't isolated research—every model change ties to real outcomes. 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, you'll design prompt strategies and learning approaches based on inference results. Meta-level thinking about how to use LLMs effectively. Preferred Experiences / We are looking for You want: 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: You'll 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 isn't 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 isn't the right fit yet Salary Salary Range: ¥10,000,000 to ¥15,000,000 Negotiable

1000〜1,500万円 ※応相談

Location Japan: 7F, Tohshin Aoyama Building, 2-10-13 Shibuya, Shibuya-ku, Tokyo, Japan

US: 651 N BROAD ST., SUITE 201, MIDDLETOWN DE 19709

日本：東京都渋谷区渋谷2-10-13 東京建物青山通りビル7F

米国：651 N BROAD ST., SUITE 201, MIDDLETOWN DE 19709

勤務条件 原則出社

Job Type Full time（正社員） Work 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 あり (3ヶ月) 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 健康保険 厚生年金 雇用保険 労災保険

通勤費月3万円まで

ストックオプション報酬制度：当社規定による

VISAサポート

#J-18808-Ljbffr Tier4