Backend Engineer (Open to Global Talent) — Corporate Data Infrastructure for the AI Era ｜ 100M-Document Search × LLM Features in Production ｜ Full Remote, Full Flex
仕事概要
※This position is also open to non-Japanese candidates who are able to work in Japan as full-time employees.
## ＜About SalesNow＞
SalesNow upholds 「誰もが活躍できる仕組みをつくる。」 (Create systems where everyone can thrive) and is taking on the challenge of fundamentally changing how people work. We rebuild how B2B work gets done, using corporate data and AI so that each person can create more value. SalesNow is the corporate data infrastructure for that work. Our database covers more than 16 million corporate and organizational profiles, structured down to offices and departments, and built on 8 billion records.
SalesNow Data Network, our own collection network, combines three routes. Our platform updates web information daily using AI. A partner network of tens of thousands of data researchers, working alongside our own in-house team, collects offline — from on-site surveys to reading paper documents. Our own corporate-information media attracts 7 million page views a month, and that user network also takes on part of the offline collection. Together these sources provide information unavailable through web search, including org charts and more than 7.5 million department contacts. You gain experience turning this proprietary company and department data into backend models that serve both search and LLM features.
Our own JC code assigns a unique ID to each site — headquarters, plants, branches, and stores — not only to the company. Records are matched on corporate number, name, address, phone, URL, and email, through fifteen levels of priority. When a match stays inconclusive, an AI workflow supplements the company information from the latest web sources. Sales teams use signals such as a rise in hiring, a funding round, or a new office to see why a company is worth approaching now. You design the backend models and APIs that keep company, site, and department data usable for search and LLM features as those records change.
Customers use search filters in SalesNow to find target companies. Through the Salesforce and HubSpot integrations, they enrich CRM records with company data and identify records that refer to the same company. Customers reach the same data from Claude and Cursor through SalesNow MCP. They connect their own systems through our Data API (beta), which ships as four APIs: Company, News, Recruit, and Organization. The Organization API covers departments, org charts, and sites. Our product, the CRM integrations, MCP, and the Data API put corporate data into different workflows. You improve the underlying APIs and data models, gaining experience representing companies, departments, and sites consistently across CRM integrations, customer applications, and LLM workflows.
SalesNow ranks No.1 in corporate database record count (企業データベース収録件数No.1; survey period: October 2025; research by Japan Marketing Research Organization). Companies that have adopted SalesNow include Yamato Transport, Panasonic, LY Corporation, SMBC Nikko Securities, PERSOL CAREER, JCB, and GMO Payment Gateway. You develop the APIs and data models behind a product with adoption by major enterprises, which is where you build judgment about backend reliability and performance at that scale.
**The era when revenue grew in proportion to headcount is over.** As Japan's workforce shrinks, each person needs to create more value. We have set a goal, starting from November 2025, of increasing ARR per employee tenfold within three years. As a backend engineer, you help shape how the team builds and verifies changes with AI, connecting architecture and review practices to the productivity of the whole engineering team.
## ＜Why We Are Hiring＞
At the center of the product is our Python (FastAPI) product API. 780 pull requests were merged into it in the last 30 days (measured on 2026-09-12). You will help design how the team verifies changes at this pace. SalesNow is approaching Series B, and you can shape the backend architecture and engineering practices as the product enters its next growth phase.
Working with AI has increased how much code we can ship. Engineers exercise judgment by checking whether each change fits the architecture and handles the underlying data correctly. We are looking for a backend engineer who can improve search performance, redesign the model layer, and improve the quality of LLM features.
## ＜Problems You Will Work On＞
As of 2026-09-12, these are open:
- Reduce full reindexing time for our OpenSearch platform, which holds 105 million documents (about 460 GB) and supports searches across companies, offices, and departments. Source-side DB queries and the ingestion process are the candidates for improvement, and you will redesign them
- Break up a 2,800-line model layer without stopping the service. A four-phase plan is in place to resolve circular imports and clarify layer boundaries, and you will refine and execute it. It uses the Strangler Fig pattern, feature flags, and API versioning to preserve backward compatibility. This work runs alongside incident response
## ＜Responsibilities＞
You will be responsible for backend development at the core of SalesNow. The scope will be adjusted to your experience and interests.
- Backend development of generative AI and data features using Python (FastAPI) and TypeScript
- Design, PoC, and production implementation of new LLM-based features
- Structuring, summarizing, and interpreting company data with LLMs
- Using LLMs to prepare company information and suggested next steps for salespeople to review and act on
- Fast product iteration with PdMs: hypothesis, implementation, user validation, and improvement
- Improving and refactoring existing APIs and data models
- Improving the performance of features that handle large volumes of data
- Evaluating edge-case behavior and verifying quality and reproducibility
※Scope of changes to duties: yes (従事すべき業務の変更の範囲：有り)
## ＜Tech Stack＞
- Languages: Python (FastAPI / SQLAlchemy), TypeScript (Next.js), SQL
- Databases / Search: PostgreSQL (Aurora), Elasticsearch / OpenSearch
- Infrastructure: AWS (ECS and others), GCP, Fastly
- AI coding and review: Claude Code, Codex, Cursor, CodeRabbit
- Workflow: n8n
- Internal tools: GitHub, Slack, Notion, Asana, Google Workspace
## ＜How We Work＞
We run Scrum-based agile development in small teams formed per mission, with daily scrums, sprint planning, sprint reviews, and postmortems. You also discuss the product with board and business members almost every week, so customer problems reach your design decisions directly.
This is how we work with AI:
- AI tool spending averages more than JPY 100,000 per person per month (company-paid). Everyone can use Claude and Gemini, and we also use Codex. You can compare models in implementation and review, and build the judgment to choose which tools fit each backend task and how to verify their output
- We ask multiple LLMs to review our work, and people check the evidence before deciding what to adopt. CodeRabbit does the first pass on pull requests, so engineers can focus on design validity, data assumptions, and impact
- Engineers use AI to prepare candidate outputs. They then verify the evidence and make the final decision. We do not build AI features as black boxes, so you design features whose reasoning engineers can inspect and verify
- Once the goal and budget are agreed, the person in charge decides how to get there. Our leadership team also writes code
## ＜Why Join Us＞
### Ship LLM features that businesses use in their daily work
You design and ship LLM features that structure, summarize, and interpret company data to support business decisions. You build them on SalesNow's database of more than 16 million corporate and organizational profiles, backed by 8 billion records. You own their accuracy and cost in production, which is how you build the judgment to say when an LLM feature is ready to ship and when it is not.
### Shape architecture for an API with 780 PRs merged in 30 days
As measured on 2026-09-12, 780 pull requests had been merged into the product API over the preceding 30 days. You design checks for backward compatibility, data assumptions, and LLM outputs across this volume of changes. You gain experience designing a release process that supports AI-assisted development while keeping engineers responsible for production quality. Product roles here work fully remotely, and several of our employees live in Kyushu, so you can do this from anywhere in Japan.
### Design how engineers build and verify with AI
One of our engineers runs six Claude Code sessions in parallel to carry work from investigation through implementation, verification, and release. Instructions for AI (.claude/skills/) go through pull request review like code. AI tool spending averages more than JPY 100,000 per person per month (company-paid), so you can test Claude, Gemini, and Codex against real backend work and decide which one fits each task. You decide which implementation tasks to accelerate with AI and where engineers must verify the results. You design the development process to handle a growing volume of changes while keeping quality decisions in engineers' hands.
In two to three years, using AI will be a given. What will set people apart is the ability to redesign workflows and organizations around AI. At SalesNow, we do that every day.
## ＜Hiring Process＞
1. Application screening
2. Interviews (multiple rounds)
3. Reference check
4. Offer meeting (job offer)
## ＜Links＞
- Interview with an engineer on our team, who joined as an intern and now develops SalesNow MCP and our API (Japanese): https://note.com/salesnow/n/na813feacc9cb
- Full job details (Japanese): https://herp.careers/v1/salesnow0801/HEBpyxeUCisz
- Recruiting site: https://recruit.salesnow.co.jp/
必須スキル
※This position is also open to non-Japanese candidates who are able to work in Japan as full-time employees.
## ＜About SalesNow＞
SalesNow upholds 「誰もが活躍できる仕組みをつくる。」 (Create systems where everyone can thrive) and is taking on the challenge of fundamentally changing how people work. We rebuild how B2B work gets done, using corporate data and AI so that each person can create more value. SalesNow is the corporate data infrastructure for that work. Our database covers more than 16 million corporate and organizational profiles, structured down to offices and departments, and built on 8 billion records.
SalesNow Data Network, our own collection network, combines three routes. Our platform updates web information daily using AI. A partner network of tens of thousands of data researchers, working alongside our own in-house team, collects offline — from on-site surveys to reading paper documents. Our own corporate-information media attracts 7 million page views a month, and that user network also takes on part of the offline collection. Together these sources provide information unavailable through web search, including org charts and more than 7.5 million department contacts. You gain experience turning this proprietary company and department data into backend models that serve both search and LLM features.
Our own JC code assigns a unique ID to each site — headquarters, plants, branches, and stores — not only to the company. Records are matched on corporate number, name, address, phone, URL, and email, through fifteen levels of priority. When a match stays inconclusive, an AI workflow supplements the company information from the latest web sources. Sales teams use signals such as a rise in hiring, a funding round, or a new office to see why a company is worth approaching now. You design the backend models and APIs that keep company, site, and department data usable for search and LLM features as those records change.
Customers use search filters in SalesNow to find target companies. Through the Salesforce and HubSpot integrations, they enrich CRM records with company data and identify records that refer to the same company. Customers reach the same data from Claude and Cursor through SalesNow MCP. They connect their own systems through our Data API (beta), which ships as four APIs: Company, News, Recruit, and Organization. The Organization API covers departments, org charts, and sites. Our product, the CRM integrations, MCP, and the Data API put corporate data into different workflows. You improve the underlying APIs and data models, gaining experience representing companies, departments, and sites consistently across CRM integrations, customer applications, and LLM workflows.
SalesNow ranks No.1 in corporate database record count (企業データベース収録件数No.1; survey period: October 2025; research by Japan Marketing Research Organization). Companies that have adopted SalesNow include Yamato Transport, Panasonic, LY Corporation, SMBC Nikko Securities, PERSOL CAREER, JCB, and GMO Payment Gateway. You develop the APIs and data models behind a product with adoption by major enterprises, which is where you build judgment about backend reliability and performance at that scale.
**The era when revenue grew in proportion to headcount is over.** As Japan's workforce shrinks, each person needs to create more value. We have set a goal, starting from November 2025, of increasing ARR per employee tenfold within three years. As a backend engineer, you help shape how the team builds and verifies changes with AI, connecting architecture and review practices to the productivity of the whole engineering team.
## ＜Why We Are Hiring＞
At the center of the product is our Python (FastAPI) product API. 780 pull requests were merged into it in the last 30 days (measured on 2026-09-12). You will help design how the team verifies changes at this pace. SalesNow is approaching Series B, and you can shape the backend architecture and engineering practices as the product enters its next growth phase.
Working with AI has increased how much code we can ship. Engineers exercise judgment by checking whether each change fits the architecture and handles the underlying data correctly. We are looking for a backend engineer who can improve search performance, redesign the model layer, and improve the quality of LLM features.
## ＜Problems You Will Work On＞
As of 2026-09-12, these are open:
- Reduce full reindexing time for our OpenSearch platform, which holds 105 million documents (about 460 GB) and supports searches across companies, offices, and departments. Source-side DB queries and the ingestion process are the candidates for improvement, and you will redesign them
- Break up a 2,800-line model layer without stopping the service. A four-phase plan is in place to resolve circular imports and clarify layer boundaries, and you will refine and execute it. It uses the Strangler Fig pattern, feature flags, and API versioning to preserve backward compatibility. This work runs alongside incident response
## ＜Responsibilities＞
You will be responsible for backend development at the core of SalesNow. The scope will be adjusted to your experience and interests.
- Backend development of generative AI and data features using Python (FastAPI) and TypeScript
- Design, PoC, and production implementation of new LLM-based features
- Structuring, summarizing, and interpreting company data with LLMs
- Using LLMs to prepare company information and suggested next steps for salespeople to review and act on
- Fast product iteration with PdMs: hypothesis, implementation, user validation, and improvement
- Improving and refactoring existing APIs and data models
- Improving the performance of features that handle large volumes of data
- Evaluating edge-case behavior and verifying quality and reproducibility
※Scope of changes to duties: yes (従事すべき業務の変更の範囲：有り)
## ＜Tech Stack＞
- Languages: Python (FastAPI / SQLAlchemy), TypeScript (Next.js), SQL
- Databases / Search: PostgreSQL (Aurora), Elasticsearch / OpenSearch
- Infrastructure: AWS (ECS and others), GCP, Fastly
- AI coding and review: Claude Code, Codex, Cursor, CodeRabbit
- Workflow: n8n
- Internal tools: GitHub, Slack, Notion, Asana, Google Workspace
## ＜How We Work＞
We run Scrum-based agile development in small teams formed per mission, with daily scrums, sprint planning, sprint reviews, and postmortems. You also discuss the product with board and business members almost every week, so customer problems reach your design decisions directly.
This is how we work with AI:
- AI tool spending averages more than JPY 100,000 per person per month (company-paid). Everyone can use Claude and Gemini, and we also use Codex. You can compare models in implementation and review, and build the judgment to choose which tools fit each backend task and how to verify their output
- We ask multiple LLMs to review our work, and people check the evidence before deciding what to adopt. CodeRabbit does the first pass on pull requests, so engineers can focus on design validity, data assumptions, and impact
- Engineers use AI to prepare candidate outputs. They then verify the evidence and make the final decision. We do not build AI features as black boxes, so you design features whose reasoning engineers can inspect and verify
- Once the goal and budget are agreed, the person in charge decides how to get there. Our leadership team also writes code
## ＜Why Join Us＞
### Ship LLM features that businesses use in their daily work
You design and ship LLM features that structure, summarize, and interpret company data to support business decisions. You build them on SalesNow's database of more than 16 million corporate and organizational profiles, backed by 8 billion records. You own their accuracy and cost in production, which is how you build the judgment to say when an LLM feature is ready to ship and when it is not.
### Shape architecture for an API with 780 PRs merged in 30 days
As measured on 2026-09-12, 780 pull requests had been merged into the product API over the preceding 30 days. You design checks for backward compatibility, data assumptions, and LLM outputs across this volume of changes. You gain experience designing a release process that supports AI-assisted development while keeping engineers responsible for production quality. Product roles here work fully remotely, and several of our employees live in Kyushu, so you can do this from anywhere in Japan.
### Design how engineers build and verify with AI
One of our engineers runs six Claude Code sessions in parallel to carry work from investigation through implementation, verification, and release. Instructions for AI (.claude/skills/) go through pull request review like code. AI tool spending averages more than JPY 100,000 per person per month (company-paid), so you can test Claude, Gemini, and Codex against real backend work and decide which one fits each task. You decide which implementation tasks to accelerate with AI and where engineers must verify the results. You design the development process to handle a growing volume of changes while keeping quality decisions in engineers' hands.
In two to three years, using AI will be a given. What will set people apart is the ability to redesign workflows and organizations around AI. At SalesNow, we do that every day.
## ＜Hiring Process＞
1. Application screening
2. Interviews (multiple rounds)
3. Reference check
4. Offer meeting (job offer)
## ＜Links＞
- Interview with an engineer on our team, who joined as an intern and now develops SalesNow MCP and our API (Japanese): https://note.com/salesnow/n/na813feacc9cb
- Full job details (Japanese): https://herp.careers/v1/salesnow0801/HEBpyxeUCisz
- Recruiting site: https://recruit.salesnow.co.jp/
歓迎スキル
- Experience leading technology selection and architecture design
- Experience processing data with generative AI
- Experience working with stakeholders to define and agree on requirements
- Experience at a startup or in 0→1 product development
- Experience building and operating data platforms or cloud infrastructure
求める人物像
- You share SalesNow's mission and our three values (AI Native, Kotoshikou — being driven by purpose, not by tasks — and Highest Quality), and you are determined to create value by making full use of AI
- You enjoy moving fast. Even as things change, you start from the purpose, find the problem, and see it through to a solution
- You work with your team and customers with integrity and ownership
- You absorb new technologies and knowledge, and take on product development that drives the business forward
- You take the voices of customers and business teams into account, and solve fundamental problems through the product
- You check logs and data before deciding, instead of trusting assumptions
See our Culture Deck for more: https://speakerdeck.com/salesnow/culture-deck
応募概要
給与
JPY 4,000,000 – 15,000,000 per year
勤務地
- Within Japan (fully remote). We have an office in Shibuya
- ※Scope of changes to place of work: none (就業場所の変更の範囲：無し)
雇用形態
Full-time
勤務体系
### Work style
- Fully remote within Japan
- For employees in the Tokyo area, one office day per month is recommended
- Employees outside the Tokyo area attend company-wide events once or twice a year
### Working hours
- Full flex-time system (no core hours)
- Standard working hours: 8 hours per day
### Holidays and leave
- Two days off every week (Saturdays, Sundays, and national holidays)
- Year-end and New Year holidays
- Summer vacation
- Paid leave (10 days granted after 6 months of employment; up to 20 days can be carried over)
- Female leave (5 days per year)
- Congratulatory and condolence leave
- Maternity leave and childcare leave
- Family care leave
試用期間
Yes (3 months)
福利厚生
### Allowances
- Full transportation expenses
- Book allowance (up to JPY 5,000 per month)
- Skill development and productivity support (up to JPY 10,000 per month)
- Nominication allowance (飲みニケーション手当; team dining support)
- Referral hiring allowance
### Health and security
- Social insurance (health insurance, employees' pension, workers' accident compensation insurance, employment insurance)
- Babysitter subsidy
- Comprehensive medical checkup (every other year, for employees aged 40 and over)
### Other
- Stock option plan