Software Engineer, LLM & Automation

Position Overview

• Location Type: Remote
• Employment Type: Contract
• Salary: Not specified

Basis is a nonprofit applied AI research organization with two goals: to understand and build intelligence and to advance society’s ability to solve intractable problems. They are building a new technological foundation inspired by human reasoning and a new type of collaborative organization.

This role involves developing scalable systems that integrate large language models (LLMs) with operational workflows, designing and deploying automated “pipelines” for tasks like recruiting, finance, and project management, often powered by GPT, Claude, or similar LLMs. They are flexible on the arrangement – hiring contractors, part-time, or full-time teammates.

Responsibilities

• Architect and maintain automation pipelines combining internal tools with GPT/Claude and other LLMs.
• Integrate data across third-party APIs (e.g., ATS platforms, Slack, Google) into unified, automated workflows.
• Leverage structured generation (JSON schemas, function calling, etc.) to ensure robust, correct LLM outputs.
• Collaborate with Ops and R&D to identify high-impact automation opportunities.
• Write production-grade code, with emphasis on modularity, reliability, and error handling.
• Deploy, scale, and optimize your solutions in a secure, cloud-based environment.
• Document solutions for both technical and non-technical audiences, ensuring easy updates and maintenance.

Requirements

• Programming Experience: 3+ years of production-level coding in Julia and/or Python.
• LLM Integration: Experience integrating LLMs using structured prompts, scaffolding frameworks (LangChain-like), or advanced text generation approaches.
• API Orchestration: Comfortable with complex API orchestration – dealing with rate limits, OAuth, error retries, etc.
• Concurrency & Scaling: Understanding of concurrency, scaling, and performance optimizations in real-world deployments.
• Collaboration & Communication: Good collaboration and communication skills, especially when gathering requirements from non-technical staff.
• Problem Solving: Flexible and creative problem solvers, comfortable with iterative, experimental development cycles.
• Data Security: Embrace data-security best practices (e.g., handling PII, encryption, secrets management).

Technical Skills (Preferred)

• Julia: Type-driven or multiple-dispatch approaches; performance tuning, HPC, or scientific computing workflows.
• Python: Building microservices.