Quality

Quality Engineer (AI-Native QA)

Shanghai · Onsite Full-time ¥350K+ base + bonus + equity

About Alva

Alva is your AI investing agent. Research a thesis. Monitor a narrative. Backtest an idea. Automate a strategy. Alva turns any of it into a live Playbook — with a team of agents watching the market 24/7 and alerting you the moment it matters. Powered by institutional-grade data from 100+ sources and a growing library of community skills and playbooks you can follow, remix, and learn from, Alva is your personal edge in all markets.

Alva was founded by a team of proven serial entrepreneurs. The founding team includes co-founders from Galxe, a major Web3 growth platform with over 30 million active users, as well as a co-founder from MiniMax, a leading frontier AI lab.

About the role

Most QA job posts in 2026 still read like 2018: write the cases, maintain the regression suite, file the bugs. This is the opposite. Writing cases, running exploratory passes, patrolling production — most of that work goes to agents. You set the strategy and hold the gate.

You own product-feature quality: test strategy, the regression system, and automation — built from scratch and kept running.

What you'll do

Quality process — test strategy, regression checklists and bug flow across three surfaces: Web, Mobile App, IM Bot
AI-native automation — an automation base (Playwright + CI) with agents wired into daily testing: cases generated from specs, exploratory passes on PR previews that come back with screenshots and recordings, auto-triage of failures
Production agent patrols — scheduled journeys walked as a real user (sign up → create an Automation → build a Playbook → receive an Alert), checking availability, alert delivery and financial-data correctness. If a daily change is computed off the previous close instead of the adjusted price, the patrol should catch it — before any user does
Metrics and the release gate — define regression pass rate and escape rate so “ship or not” is an evidence-based call
Bad-case loop — trace quality issues from user feedback and monitoring, drive the fix, and turn it into a regression case or eval case. Agent output quality belongs to the eval system; you own the last mile on the product side

What we're looking for

3+ years in software testing with strong fundamentals: case design, edge cases, and bug reports an engineer reads once and understands
Fluent in one automation stack (Playwright / Cypress + TypeScript, or Python), including building a framework from scratch and wiring it into CI
AI First — Claude Code, Cursor, Playwright MCP in your daily workflow, with concrete examples of how AI changed the way you test. LLM / eval experience is a bonus, not a requirement
Genuine interest in investing — you read charts, filings and the usual metrics; ideally you've traded yourself. You're testing an investing product, so how much you understand decides how deep your bugs go
Hands-on and evidence-driven: never satisfied with “looks fine” — you reproduce it yourself and argue with data
Founder mindset: you build a process where none exists, and you want the bandwidth of working onsite
Bonus: testing AI / agent products, RAG or agent traces; eval tooling (Promptfoo, DeepEval, LangSmith); quality work in fintech, brokerage, trading, or market-data products; synthetic monitoring, CI release gates, API or data-quality testing; a public repo, tool or write-up

Why this seat is rare

Most “AI QA” roles don't really exist yet — QA postings at frontier AI companies are still Playwright + Datadog, with no LLM or agent testing in sight. Here the method is deliberately undefined: you decide what it looks like
AI × finance crossover — eval design, agent testing and financial-data quality are scarce experience anywhere
You build the quality system 0 → 1 and grow into the quality lead as the team scales
The take-home is honest work, not a brainteaser: half a day to a day actually using Alva, finding real bugs, then automating one journey — with AI doing most of the typing and you telling us which of its output you overrode, and why

Interested?

One email, one thing you built. I read every application myself.

Apply → hr@alva.xyz

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Research & Partnerships

AI Investment Research & Partnerships Manager

Shanghai on-site or US remote Full-time · reports to CEO Up to $200K + equity

About Alva

Alva is your AI investing agent. Research a thesis. Monitor a narrative. Backtest an idea. Automate a strategy. Alva turns any of it into a live Playbook — with a team of agents watching the market 24/7 and alerting you the moment it matters. Powered by institutional-grade data from 100+ sources and a growing library of community skills and playbooks you can follow, remix, and learn from, Alva is your personal edge in all markets.

Alva was founded by a team of proven serial entrepreneurs. The founding team includes co-founders from Galxe, a major Web3 growth platform with over 30 million active users, as well as a co-founder from MiniMax, a leading frontier AI lab.

About the role

The investors we want on Alva don't respond to sales pitches. They respond to research that is better than their own. So our go-to-market isn't a pitch deck — it's the work itself.

Which makes this one person doing two jobs usually split across two teams: the research is the ammunition, and you are the one who pulls the trigger. Traditional BD can't earn credibility with this audience; traditional analysts won't go out and close. If you've been doing both — for yourself — this is the scaled-up version.

What you'll do

Research (~50%) — produce public-market research across stocks, sectors, themes, macro and relevant crypto; use AI agents and LLMs to speed up sourcing, extraction and synthesis while personally verifying key data and conclusions; turn strong research into Alva Playbooks for investors, creators and teams; own the quality of everything published under your name and Alva's
Investor / creator / small-B2B partnerships (~40%) — build and manage a target list across individual investors, investing creators, Substack, X, YouTube, investment communities, and small funds, firms and research teams; lead demos and partnerships from first contact through launch and follow-up; build long-term relationships, make sure customers actually use the product, and own a clear, realistic pipeline
Product feedback (~10%) — be a heavy product user. Report agent errors, hallucinations, data issues and anything that slows down your work, with clear feedback for product and engineering

What we're looking for

You actually invest — real positions, your own views, and some calls that went wrong
AI is part of how you work — you can explain your research workflow and where AI has made you faster
You have experience closing partnerships, sales, sponsorships, or similar deals
Strong research judgment and the ability to defend your methodology
Clear, direct writing and strong English communication
Comfortable working independently in a small, fast-moving team; Mandarin preferred

Bonus

Your own audience on Substack, X, YouTube, Xueqiu, RED, or similar
Experience across fintech, crypto, sell-side research, investing, or financial data products
Existing relationships with investors, creators, or financial professionals
Self-built research tools or automated workflows
Experience with products such as Substack, Seeking Alpha, terminals, or screeners

Interested?

One email, one thing you built. I read every application myself.

Apply → hr@alva.xyz

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Product

Product Manager

Shanghai · Onsite Full-time ~$100K + equity

About Alva

Alva is your AI investing agent. Research a thesis. Monitor a narrative. Backtest an idea. Automate a strategy. Alva turns any of it into a live Playbook — with a team of agents watching the market 24/7 and alerting you the moment it matters. Powered by institutional-grade data from 100+ sources and a growing library of community skills and playbooks you can follow, remix, and learn from, Alva is your personal edge in all markets.

Alva was founded by a team of proven serial entrepreneurs. The founding team includes co-founders from Galxe, a major Web3 growth platform with over 30 million active users, as well as a co-founder from MiniMax, a leading frontier AI lab.

About the role

Make Alva the best AI product in the finance vertical. You'll end-to-end own key product modules — including the AI harness & agent orchestration layer, trading execution, the Playbook / Remix ecosystem, and content distribution — and ship them with engineering and design.

What you'll do

Own the roadmap and make prioritization calls across every surface — power vs. simplicity, flexibility vs. reliability, depth vs. breadth
Shape how natural language becomes a reliable financial agent harness — from agent orchestration to data retrieval, visualization, backtesting, and execution
Design the Playbook system as a composable content and distribution engine
Find and fix friction through deep personal usage, user research, and data

What we're looking for

Product management experience; background in AI agent products, fintech, trading systems, or UGC platforms preferred
Strong product intuition and systems thinking — see the whole product, spot what matters most
Hands-on: you prototype, write specs, test, and ship — not just coordinate
Power user of AI-native tools (Claude Code, Cursor, etc.) — you use AI to build, not just talk about it
Real technical understanding of agents: the LLM + tool-calling + memory + planning loop, the tradeoffs between RAG and fine-tuning, context-window cost and latency, MCP, and how to design evals
Exceptionally self-driven with strong learning ability and problem-solving instincts
Clear communication in both English and Chinese; genuine curiosity about financial markets
Bonus: quant finance or macro background; startup, OPC, or indie developer experience

Interested?

One email, one thing you built. I read every application myself.

Apply → hr@alva.xyz

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Engineering

Quant / Algo Trading System Engineer

Shanghai Full-time $150K–$200K + equity

About Alva

Alva is your AI investing agent. Research a thesis. Monitor a narrative. Backtest an idea. Automate a strategy. Alva turns any of it into a live Playbook — with a team of agents watching the market 24/7 and alerting you the moment it matters. Powered by institutional-grade data from 100+ sources and a growing library of community skills and playbooks you can follow, remix, and learn from, Alva is your personal edge in all markets.

Alva was founded by a team of proven serial entrepreneurs. The founding team includes co-founders from Galxe, a major Web3 growth platform with over 30 million active users, as well as a co-founder from MiniMax, a leading frontier AI lab.

About the role

We're building the core trading system that turns strategies and signals into real, reliable execution in live markets. We are looking for a top-tier systems engineer to build the core trading engine behind Alva — production-grade, similar in scope to QuantConnect Lean.

This is not a strategy or quant research role. Your focus is systems, infrastructure, correctness, and reliability across the full lifecycle: research → backtesting → paper trading → live trading → execution.

What you'll do

Build and evolve the core trading engine — backtesting engine (event-driven simulation, data alignment), strategy runtime (user logic, lifecycle, state), and a paper-trading environment
Design and implement live trading systems — signal → order → execution pipelines, broker/exchange integrations (REST / WebSocket), order lifecycle management (fills, partial fills, reconciliation)
Solve real-world trading problems — state consistency across backtest / paper / live, idempotency and duplicate-order prevention, latency, slippage, and execution edge cases

What we're looking for

You've built or worked on real trading systems in production
You understand how systems fail in the real world — not just in backtests
You care deeply about correctness, edge cases, and failure modes
You can reason about complex systems end-to-end
AI-fluent: you build with AI coding tools daily and can show what that changed about how you work
Strong signals (all optional): Lean / NautilusTrader / Hummingbot; broker & exchange integrations; event-driven or distributed systems in production; strong backend / infra background (Python / Go / Rust); backtesting, market-data, or real-time streaming systems

Why join Alva

Build a system where bugs translate directly into financial loss — correctness matters
Work on one of the hardest problems: making simulated systems match reality
Define the core execution layer of an AI-driven investing platform
High ownership and meaningful equity (options); small, elite team; high autonomy, low bureaucracy

Interested?

One email, one thing you built. I read every application myself.

Apply → hr@alva.xyz

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Engineering

AI Agent Engineer

Shanghai or US West Coast Full-time $100K–$200K + equity

About Alva

Alva is your AI investing agent. Research a thesis. Monitor a narrative. Backtest an idea. Automate a strategy. Alva turns any of it into a live Playbook — with a team of agents watching the market 24/7 and alerting you the moment it matters. Powered by institutional-grade data from 100+ sources and a growing library of community skills and playbooks you can follow, remix, and learn from, Alva is your personal edge in all markets.

Alva was founded by a team of proven serial entrepreneurs. The founding team includes co-founders from Galxe, a major Web3 growth platform with over 30 million active users, as well as a co-founder from MiniMax, a leading frontier AI lab.

About the role

Alva is building AI agents that automate the investment research workflow — from idea generation and data analytics to trend monitoring, strategy development, and backtesting — while enabling users to build, share, and refine their strategies together.

You will build and ship AI agents that reliably complete investment-related tasks. This is an agent-builder role, not model research and not script writing — and evals are a first-class citizen, not an afterthought.

What you'll do

Design, build, and deploy AI agents
Equip agents with specialized tools and knowledge
Implement safety guardrails
Create evals, monitor performance, and iterate quickly
Collaborate with product, operations, and quant researchers

What we're looking for

Strong engineering fundamentals
Can own systems end-to-end
Excited and thoughtful about AI — you can reason about why an agent failed, not just that it did
Curious about markets / investing (deep expertise not required)

Interested?

One email, one thing you built. I read every application myself.

Apply → hr@alva.xyz

We only hire builders.

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