Alva Hiring Playbook

AI Investing Video Host (On-Camera)

The role

We're looking for one person to run Alva's short-form video on Instagram, TikTok, YouTube Shorts and X. You'll be on camera explaining what's happening in the market, usually on the day it happens: earnings, buybacks, Fed decisions, a stock that moved 20% before lunch.

You'll use Alva for your research, so the product will show up in a lot of your videos. We'd rather it show up because it actually helped you than because you were asked to mention it.

Nobody else works on these videos. You come up with the idea, research it, write it, shoot it, edit it and post it. That only works at the pace we need if AI tools already handle a lot of the work for you.

What you'll do

• Decide what's worth covering each day. Be ready to post the same day when something big happens
• Research with Alva and your own sources, and get the numbers right
• Write, film, edit, caption and post everything yourself, including covers and carousels
• Use AI for whatever it's good at: first drafts, rough cuts, captions, B-roll, thumbnails, turning one video into a version for each platform
• Watch the numbers (views, watch time, follows, sign-ups), try new hooks and formats, and drop what doesn't work
• Read the comments and use good questions as ideas for the next video
• Tell the product team when something in Alva is confusing, slow or wrong, with an example

What I'm looking for

• At least six months of posting short-form video regularly, on your own channel or as the host of a brand's channel. We care more about your typical views than your follower count
• You invest your own money. You hold positions, you've been wrong before, and you can explain an earnings report, a buyback or a valuation without mistakes
• You make your videos on your own today, and you can show us how: which tools you use, how long each step takes, and how you get from idea to posted in half a day or less
• Native or near-native English, for a US or global English-speaking audience
• You check your facts. Everything you post goes out under Alva's name
• Probably not a fit if your channel is built on stock tips, "this will 10x" calls, guaranteed returns or meme coins, if someone else edits your videos today, or if you only make long-form videos

Nice to have

• You've hosted short-form videos for a fintech or financial media brand
• You already use AI tools to research stocks or manage your own money
• You can make static posts, like carousels and news cards, as well as videos
• You have your own audience on TikTok, Instagram, YouTube, X or Substack. We're open to talking about how your channel and Alva's can work together
• How to apply: email jemma@alva.xyz with links to three short videos you're proud of, a screenshot of your last 90 days of analytics, and a few lines on how you make a video. If you'd rather, send a 60-second video about any market story from this week instead

Standalone job posting →

Quality Engineer (AI-Native QA)

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 I'm 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

AI Investment Research & Partnerships Manager

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 I'm 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

Product Manager

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 I'm 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

Quant / Algo Trading System Engineer

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 I'm 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

• 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

AI Agent Engineer

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 I'm 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)