Forstærkningslæring Jobs

I am rolling out a trio of inter-linked prototypes that tackle pressing urban and environmental challenges while showcasing cutting-edge AI, blockchain, and IoT engineering. First, the AI-driven Smart Traffic System must ingest multi-sensor feeds, apply real-time reinforcement learning, and demonstrate measurable gains in a simulation such as SUMO—specifically a double-digit reduction in congestion and wait times across a 20-intersection grid. Second, the Blockchain Voting System needs an end-to-end verifiable ballot flow. I expect audited Solidity or Substrate contracts, mobile-friendly front-end, zero-knowledge vote secrecy, and a testnet demo with at least fifty dummy voters that passes a public audit script. Third, the Solar-Powered Smart Watering System will run on a low-powe...

I’m expanding the Bespoke Labs review team and need a seasoned software engineer to judge the quality of code-centric tasks, agentic benchmarks, and reinforcement-learning environments that feed directly into frontier AI research. Your main job will be to open up the model’s work, think like a senior engineer, and decide whether the solution is correct, efficient, and reproducible. Day-to-day you’ll pull assignments from our queue, spin up the provided repo or Colab, and work through automated and manual checks. When something fails, you’ll pinpoint the issue, add concise reviewer notes, and push a resolution verdict that downstream researchers can trust. Most of the work happens in Python with a healthy dose of shell tooling, git, and containerised test harnesses,...

I’m expanding the Bespoke Labs review team and need a seasoned software engineer to judge the quality of code-centric tasks, agentic benchmarks, and reinforcement-learning environments that feed directly into frontier AI research. Your main job will be to open up the model’s work, think like a senior engineer, and decide whether the solution is correct, efficient, and reproducible. Day-to-day you’ll pull assignments from our queue, spin up the provided repo or Colab, and work through automated and manual checks. When something fails, you’ll pinpoint the issue, add concise reviewer notes, and push a resolution verdict that downstream researchers can trust. Most of the work happens in Python with a healthy dose of shell tooling, git, and containerised test harnesses,...