• Attend and complete the Flatiron School Data Science Bootcamp as part of the apprenticeship • Contribute to software projects for your host company • Write clean, maintainable and testable code using modern frameworks and tools • Collaborate in sprint-based workflows and participate in code reviews • Support feature development, debugging, documentation, and QA processes • Build your portfolio of production-level work while growing your technical and collaborative skills • Receive ongoing mentorship from senior developers and product leaders

• Attend and complete the part-time Data Science Bootcamp as part of the apprenticeship • Contribute to software projects for your host company • Write clean, maintainable and testable code using modern frameworks and tools • Collaborate in sprint-based workflows and participate in code reviews • Support feature development, debugging, documentation, and QA processes • Build your portfolio of production-level work while growing your technical and collaborative skills • Receive ongoing mentorship from senior developers and product leaders

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role supports high-impact collaborations with leading AI teams focused on improving how conversational AI models reason about complex engineering concepts across multiple disciplines. - Write and refine prompts that guide AI models in engineering-related scenarios - Evaluate AI-generated responses for technical accuracy and applied reasoning - Verify engineering claims using domain expertise and authoritative sources - Annotate responses by identifying strengths, gaps, and conceptual inaccuracies - Assess clarity, structure, and appropriateness of explanations for different audiences - Apply structured evaluation guidelines and benchmarking standards - Ensure model responses align with expected system behavior and engineering logic Qualifications - PhD in Engineering or a closely related technical discipline - Expertise in one or more of the following areas: - Mechanical & Physical Systems Engineering - Electrical, Electronic & Computer Engineering - Chemical, Materials & Process Engineering - Civil, Environmental & Infrastructure Engineering - Strong experience using large language models (LLMs) - Excellent technical writing and explanation skills - Strong analytical reasoning and attention to detail - Ability to identify subtle technical inaccuracies in model outputs Requirements - Applied research or industry engineering experience - Experience with RLHF, model evaluation, or AI data annotation - Experience teaching or mentoring technical concepts - Familiarity with evaluation rubrics or structured review frameworks What Success Looks Like - Identifying technical inaccuracies or flawed reasoning in engineering outputs - Improving the rigor and clarity of AI explanations - Producing consistent evaluation artifacts that strengthen model performance - Helping ensure AI systems provide reliable engineering guidance Why This Opportunity - Contribute directly to improving the reasoning capabilities of advanced AI systems - Apply deep engineering expertise to real-world AI development - Work with cutting-edge AI labs and research teams - Flexible remote work with competitive compensation Contract Details - Independent contractor role - Fully remote with flexible scheduling - Compensation: $73.29/hr - Weekly payments via Stripe or Wise - Projects may expand depending on performance and project needs About the Platform This opportunity is available through a leading AI-driven work platform.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role focuses on designing, implementing, and operationalizing AI systems with a strong emphasis on structured evaluation, measurable quality, and production-grade reliability. - Build and maintain evaluation frameworks (LLM-as-Judge, rubric-based scoring, regression test suites) to measure output quality, reliability, and drift. - Architect and implement multi-agent workflows with clear coordination, tool usage, and failure handling patterns. - Build structured observability into AI systems (tracing, prompt/version tracking, evaluation logging, cost and latency monitoring). - Define and enforce quality gates for AI features using automated evaluations prior to production release. - Optimize inference performance (latency, token usage, caching, batching, routing across models). - Collaborate with product and engineering teams to translate business requirements into testable AI system designs. - Contribute to code reviews, architectural discussions, and internal standards for AI development. - Design and implement Retrieval-Augmented Generation (RAG) systems and Model Context Protocol (MCP) servers using structured and unstructured enterprise data. - Develop and manage fine-tuning workflows (SFT, preference optimization, or related techniques) including dataset preparation, versioning, and validation. Qualifications - 3-5 years of full stack engineering experience with strong fundamentals in object-oriented programming, applicable design patterns, and AI-focused system design. - Professional experience in Python, C#, Java, or a similar language used in production systems. - Experience with LLM evaluation and observability tooling (e.g. Langfuse, LangSmith, OpenTelemetry-based tracing, custom evaluation harnesses). - Experience implementing guardrails, policy enforcement, and safety layers in AI-driven systems while leveraging LLM-as-Judge for validation and continuous improvement. Requirements - Familiarity with performance optimization techniques for LLM-based systems (latency, caching, routing, batching). - Experience building production-grade RAG systems (retrieval pipelines, chunking strategies, embeddings, reranking, context construction). - Experience contributing to internal AI standards, reusable frameworks, or platform-level tooling. - Experience deploying AI systems in cloud environments (AWS, Azure, GCP). - Experience in Databricks (model serving endpoints, ML Flow). Company Description Risepoint is an education technology company that provides world-class support and trusted expertise to more than 100 universities and colleges. We primarily work with regional universities, helping them develop and grow their high-ROI, workforce-focused online degree programs in critical areas such as nursing, teaching, business, and public service. Risepoint is dedicated to increasing access to affordable education so that more students, especially working adults, can improve their careers and meet employer and community needs.