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Location: Remote Type: Full-time / Part-time Company: Dante Labs(dantelabs.us) -About the Role We're building a platform similar to TradingView, enhanced with advanced AI-driven capabilities. Our goal is to empower users with intelligent market predictions, sophisticated analytical tools, and next-level charting functionalities to support smarter trading decisions. The platform is being developed with a strong emphasis on clean architecture, scalability, and seamless performance across frontend and backend systems. We're looking for talented engineers who are excited about building high-performance, data-intensive applications at the intersection of finance, AI, and Web3. What You'll Do - Design and build scalable backend services and APIs - Develop responsive, high-performance frontend interfaces - Integrate AI-driven analytics and prediction models - Ensure reliability, security, and low-latency performance - Collaborate with product, design, and AI teams to ship features Who You Are - Strong experience in backend development (Node.js, Python, Go, or Rust) - Solid frontend skills (React, Next.js, TypeScript) - Understanding of financial data, trading platforms, or market analytics (a plus) - Experience with blockchain or Web3 (a plus) - Passion for clean, testable, and scalable code - Comfortable working in a fully remote, fast-paced environment What We Offer - Competitive compensation (USD or crypto) - 100% remote – work from anywhere - Flexible hours - Opportunity to shape a high-impact product from the ground up - Work with a global, talented, and supportive team How to Apply To apply for this role, please complete the following steps: -Complete the home task Visit the following GitHub repository and follow the instructions in the README file(blockchain task only): https://github.com/dantelabs-team-8/fullstackblockchain -Record a Loom video Once you've completed the task, record a short Loom video walking through your solution and results. -Send your submission *Email the following to hr[at]dantelabs[dot]us: *Your CV *A link to your Loom video *(Optional) Any additional notes or context about your solution -Next steps Our tech team will review your submission. If it meets our expectations, we'll invite you for a technical interview and move forward in the hiring process.

We are looking for a specialist in Solana security programs to conduct an audit of the smart contract before deployment on the mainnet. Contract stack: Rust + Anchor framework SPL Token (transfer/storage of tokens in PDA) Mechanics: timing rounds, escrow of bets, resolver of final price, calculation and distribution of payouts, claim instructions, emergency pause + refund, fee configuration through admin authority Check for common vulnerabilities in Solana/Anchor: missing ownership/signer checks, account validation, integer overflow, PDA seeds collisions, reentrancy patterns, authority key privileges Analysis of payout distribution logic (rounding, edge cases with multiple participants) Verification of emergency mechanisms (pause, refund) — whether they can be bypassed or abused Report with classification of findings by criticality (Critical/High/Medium/Low) and recommendations Re-check after fixes (re-audit fixes) Requirements for the performer: Proven experience in auditing or developing Solana programs (Rust/Anchor) — please provide links to GitHub, previous audits, or examples of found vulnerabilities Understanding of SPL Token and PDA-escrow patterns Experience with static analysis tools (Soteria, Sec3, cargo-audit) will be a plus In your response, please indicate: Experience specifically with Solana (not EVM) — specific projects Estimated cost and timeline for a contract of this scope Report format (example, if available)

What we are calculating in the project: Realized PnL, ROI, and Win Rate for cryptocurrency wallets - how profitable the wallet traded a certain token over the selected period. Based on what data: the history of on-chain transactions of the wallet (swaps, token transfers) + the market price of the token at the time of each transaction. The main data source is Moralis: two calls during the initial loading of the wallet - native ETH transfers and all ERC-20 token transfers. What we are comparing with: Nansen.io - we take it as a benchmark, comparing our calculated metrics with what Nansen shows for the same wallets over the same period. Problem: our figures significantly differ from Nansen, and we do not fully understand the rules by which some actions of the wallet should be classified for PnL purposes. We need to fix the calculation of Realized PnL, ROI, and Win Rate to match Nansen. In your application, please write: - experience with similar tasks - experience with Nansen - experience with Moralis - experience with DeFiLlama - experience with articles on crypto transactions - approximate cost and timeline for the fix

It is important to start - TODAY I need a person who has experience in writing trading bots on DEXs (needed on the BSC network) with an understanding of transaction costs, gas, etc. I want to test several trading strategies on a real trading agent I need to quickly develop an autonomous trading agent that uses the Trust Wallet and CoinMarketCap APIs to execute trades on the BNB Chain (BSC) based on 3–5 embedded strategies. Technology stack (mandatory) - Trust Wallet Agent Kit (TWAK) — for self-custodial signing and executing transactions (https://portal.trustwallet.com) - CoinMarketCap AI Agent Hub — for obtaining market data and signals (https://coinmarketcap.com/api/agent) - BNB AI Agent SDK — for quick integration (https://github.com/bnb-chain/bnbagent-sdk) Functional requirements Data (CMC AI Agent Hub) - Data retrieval via MCP or x402 (https://coinmarketcap.com/api/agent) - Use of ready-made Skills for RSI, MACD, Fear & Greed, volumes Execution (Trust Wallet Agent Kit) - Local signing of transactions — keys remain with the user (https://portal.trustwallet.com) - Autonomous mode: the agent signs and sends trades by itself - Limitations: drawdown, daily limit, stop-loss, token whitelist - Use of x402 for payment of data/computations Strategies - Implement 3–5 described strategies with the ability to switch between them - Examples: combination of funding rates + Fear & Greed, DCA based on sentiments with filters Technical requirements - Language: Python (preferred) or TypeScript/JavaScript - Tools: TWAK CLI, CMC CLI, BNB AI Agent SDK Who can take this on?