RentSpree is looking to solve business problems by developing, evaluating, and fine-tuning cutting-edge machine learning and large language models (LLMs) to create impactful AI-powered features and internal tools.
Requirements
- Expert proficiency in Python modern ML/LLM frameworks (PyTorch, Hugging Face, LangChain, LlamaIndex), and strong SQL/data pipeline skills.
- Proven hands-on experience fine-tuning LLMs, building RAG pipelines, and creating agentic AI workflows (e.g., multi-step reasoning, autonomous agents, internal AI copilots).
- Experience defining and running LLM evaluation frameworks and integrating human feedback loops.
- 6+ years in applied ML, NLP, or AI engineering, with demonstrated experience in shipping production AI/ML features.
- Bachelor’s or Master’s degree in computer science, data science, machine learning, or a related quantitative field.
Responsibilities
- Strategic AI Alignment: Collaborate with Product, Engineering, and Data leaders to define and refine RentSpree’s ML/LLM strategy, ensuring alignment with business priorities, roadmap milestones, and measurable success criteria.
- AI Use Case Translation: Partner with analytics teams to turn business challenges into well-defined, testable LLM/ML use cases, accelerating time-to-production for AI solutions.
- Mentorship & Skill Uplift: Coach data scientists, analysts, and engineers in LLM concepts, evaluation design, RAG patterns, and applied AI/ML best practices.
- Full AI Lifecycle Ownership: Design, fine-tune, evaluate, and deploy ML/LLM models for both product-facing features and internal AI assistants, with a focus on scalability, safety, and maintainability.
- Agentic AI Infrastructure: Architect and implement workflows for agentic AI systems — including multi-agent coordination, tool integration, memory management, and retrieval-augmented generation (RAG) — enabling autonomous and semi-autonomous AI-powered decisioning.
Other
- Take full ownership and accountability: Drives projects from idea to execution, with pride, urgency, and a “do what it takes” mentality.
- Collaborate with clarity, candor, and respect: Communicate openly and courageously, offer and receive feedback well, and build trust through transparency and reliable partnership.
- Strong communication skills to bridge technical and non-technical teams, with the ability to influence AI product direction.
- 2 days per week in-office
- Bachelor’s or Master’s degree in computer science, data science, machine learning, or a related quantitative field.
