🛠 Tech Stack

💼 About This Role

You'll advance research methods in probabilistic programming and causal inference, packaging them into reusable modules on Basis's platform. Your work will directly shape core technology components that enable both commercial and societal impact. You'll collaborate with a team that values rigorous science and engineering excellence.

🎯 What You'll Do

• Advance research methods in probabilistic programming and causal inference
• Build core technology modules for reusable algorithmic components
• Package experimental techniques into production-ready code
• Contribute research-driven capabilities to platform offerings

📋 Requirements

• Publications at top-tier conferences like NeurIPS, ICML, or PLDI
• Proficiency in Python, PyTorch/JAX for research engineering
• Deep knowledge of probabilistic programming or causal inference
• Ability to drive software projects from start to finish

✨ Nice to Have

• PhD in Computer Science or related field
• Experience at top research labs (e.g., Google Research, DeepMind)
• Experience with Bayesian methods or causal modeling

🎁 Benefits & Perks

• 💰 Competitive compensation and equity
• 🏖️ Flexible PTO policy
• 🏥 Health, dental, and vision insurance
• 🚀 Opportunity to shape cutting-edge AI research
• 📚 Learning and development budget

🚩 Heads Up

• Role combines research and engineering expectations that may be overly demanding
• Requires both publications and production shipping experience, potentially filtering out strong candidates