Salary / Hourly Rate: $180,000 - $250,000 per year
Benefits: US citizen/visa only
Role Information
Role Overview: N/A
Responsibilities: Design and implement agentic workflows powered by LLMs for real-world task execution., Build customization layers tailored to organization-specific logic., Develop voice-based and message-based learning agents that drive engagement., Architect AI pipelines with evaluation, feedback loops, and continuous monitoring., Manage multi-agent orchestration and core AI system infrastructure.
Qualifications: Machine Learning Expertise5+ years experience in ML (PyTorch, TensorFlow).2+ years experience with LLMs (Hugging Face, OpenAI, Anthropic)., 5+ years experience in ML (PyTorch, TensorFlow)., 2+ years experience with LLMs (Hugging Face, OpenAI, Anthropic)., AI System DevelopmentExperience in building and deploying production AI systems, including RAG and vector search.Knowledge of prompt engineering, AI safety, and content filtering best practices., Experience in building and deploying production AI systems, including RAG and vector search., Knowledge of prompt engineering, AI safety, and content filtering best practices., Technical ProficiencyExpert-level Rails experience (3+ years) with REST APIs, PostgreSQL, ActiveRecord, and RSpec.Understanding of LangChain, LlamaIndex, or similar frameworks, or ability to ramp up quickly., Expert-level Rails experience (3+ years) with REST APIs, PostgreSQL, ActiveRecord, and RSpec., Understanding of LangChain, LlamaIndex, or similar frameworks, or ability to ramp up quickly.
Minimum Requirements: 5+ years of experience in Machine Learning (PyTorch, TensorFlow).,2+ years of experience working with LLMs (Hugging Face, OpenAI, Anthropic).,Expert-level Rails development experience.,Proven experience building and deploying production AI systems (including RAG and vector search).,Strong knowledge of AI safety and content filtering best practices.
Screening Questions: Please share a 2–3 minute video (screen share + voice) walking us through something you’ve built or worked on recently — ideally related to AI, LLMs, or system integration.,Describe a real-world AI system you’ve designed or deployed (e.g., LLM, RAG, multi-agent, or production ML system). What was your approach, and what were the biggest technical challenges you solved?
Company Information
About Company: N/A
Culture: N/A
Additional Information
Interview Process: Initial Call (30 min) – Introductory conversation with a recruiter or founder to review background, motivations, and role fit.Technical Deep Dive (60–90 min) – Live discussion with senior engineers covering LLM design scenarios, Rails integration, and system-level problem solving.Practical Exercise / Case Study – Short take-home or live coding assignment focused on building or evaluating an AI workflow.Final Round (60 min) – Cultural and cross-functional interviews with founders/team to assess collaboration, communication, and alignment with mission.
Day to day: You’ll spend your time designing, building, and shipping AI systems that have immediate real-world impact. Expect to split your focus between coding (Rails, ML/LLM workflows), collaborating with founders and engineers on product direction, and iterating quickly on prototypes. Most days balance deep technical work with fast feedback cycles from internal stakeholders and enterprise clients.
Team: You’ll join a lean, highly technical founding team, reporting directly to the CTO and collaborating closely with the CEO and product leads. The team is composed of engineers with backgrounds in AI/ML, backend systems, and enterprise software, all focused on building production-grade AI enablement tools. You’ll have significant ownership and autonomy, with the ability to shape architecture, standards, and culture.
Growth: Own core technical systems from day one., Mentor and grow future engineers as the team scales., Influence product strategy at both the technical and business levels., Build a track record of shipping AI systems at scale — positioning yourself for leadership roles within Arist or in future ventures.
Ideal Candidate Profile: Innovative Builder — Able to design and optimize sophisticated AI workflows that solve real enterprise challenges., Strong Collaborator — Communicates effectively across technical and non-technical teams, driving AI integration in complex, full-stack environments., Technically Excellent — Expert in Rails (REST APIs, PostgreSQL, ActiveRecord, RSpec) with proven experience in deploying scalable AI systems., Agile and Adaptive — Thrives in ambiguity, learns new frameworks (LangChain, LlamaIndex, etc.) quickly, and delivers results at startup speed., Examples of Candidates (do not contact)
Yutong Xue
Andrew R. Reed
Zion Badash
Ryan Rishi, Yutong Xue, Andrew R. Reed, Zion Badash, Ryan Rishi
