Research Engineer, Operations

Responsibilities

• Build researcher productivity tools including experiment tracking, literature research automation, computational environment management, and integration with existing research workflows.
• Develop GTM and business operations automation including CRM systems, pipeline management, proposal generation, contract tracking, and customer communication workflows.
• Create analytics and measurement infrastructure providing visibility into research progress, operational metrics, resource utilization, and progress toward strategic objectives.
• Automate operational processes for recruiting (candidate tracking, scheduling, communication), marketing (content generation, social media, events), and cross-functional workflows that currently require manual coordination.
• Build internal APIs and integrations connecting various systems (Notion, Linear, email, calendars, Slack) to create seamless workflows and eliminate manual data transfer.
• Implement AI/LLM-powered tools leveraging latest capabilities in language models, retrieval-augmented generation (RAG), and agent orchestration to create intelligent automation.
• Translate research ideas into correct, robust, and scalable high-quality code, engaging in performance engineering and scaling research code.
• Contribute to the culture and direction of Basis by modeling technical excellence in internal tools, continuous improvement mindset, and focus on enabling high-leverage work.

Qualifications

• Possess excellent programming and software engineering skills, especially in Julia, Python, C++, ML-family languages.
• Have demonstrated the ability to drive software projects from start to finish.
• Be comfortable digesting research from PL and/or ML venues, such as PLDI, POPL, NeurIPS, or ICML.
• Progress with a high degree of autonomy and under uncertainty.
• Have experience with LLM-powered automation, including using language models (OpenAI API, Anthropic Claude, LangChain) for intelligent automation, workflow orchestration, and agent-based systems.
• Have demonstrated significant technical achievements within ML engineering. Examples include: implementing variants of newly published techniques from scratch, building systems and workflows for training large models distributed across many machines, building systems that span all levels of the programming stack from high-level API infrastructure to close-to-the-metal code.
• Understand research and operational workflows well enough to identify high-impact automation opportunities. You might have worked in research environments, built productivity tools, or supported technical teams through infrastructure.
• Be skilled at user-centric design for internal tools. You naturally seek user feedback, iterate based on real usage, and measure success by adoption and time saved rather than technical sophistication.
• Be excited about force-multiplying impact. The tools you build might not be externally visible, but they enable Basis to solve intractable problems at scales we couldn’t otherwise reach.