• Define and own the AI platform architecture: retrieval infrastructure, model lifecycle, evals framework, guardrails, and GenAI product feature design. • Lead the build of the shared AI platform layer consumed by all AI product features and reusable by internal business teams. • Hands-on contributor: build production AI systems, write proofs of concept, and validate architecture through working software. • Set and enforce technical standards for the AI Platform team; drive architecture reviews and model quality reviews. • Coordinate with the Personalization team to define clear boundaries between the GenAI product layer and existing ML personalization infrastructure. • Contribute AI platform-specific patterns and lessons into iHerb's shared AI-driven SDLC golden path. • Drive the hardest cross-cutting technical decisions across multiple teams and shared platform services. • Establish and evolve iHerb's AI-driven SDLC golden path: shared standards, Claude Code skills, guardrails, and automation patterns. • Lead complex multi-team technical efforts by coordinating architecture reviews, aligning peer Principals and EMs, and resolving competing approaches. • Mentor and raise the technical bar across the engineering organization through code review, architecture review, and direct coaching of senior engineers. • Represent engineering in cross-functional conversations with product, data science, security, and infrastructure. • Feed architectural decisions into the shared knowledge base so institutional knowledge compounds across the organization.

• Play a critical role in bridging data science and software engineering • Work closely with data scientists, software engineers, and product teams • Deliver robust, scalable AI solutions • Ensure seamless integration of AI models into production systems • Optimize performance and maintain reliability of AI-driven features

Role Description Mercor connects elite creative and technical talent with leading AI research labs. The position is for a Human Baseliner for Open-Ended ML Research Tasks. - Attempt open-ended machine learning research tasks under a fixed time and compute budget. - Work independently in a sandboxed Linux environment with internet access. - Use preferred tools, including IDEs and AI coding assistants like Cursor, Claude Code, and ChatGPT. - Record full working sessions via screen recording. - Complete pre-task and post-task questionnaires. - Submit final work product, screen recording, and completed questionnaires for evaluation. Qualifications - 3+ years of machine learning experience. Time in a PhD program counts. - Attended a top-100 university or worked at FAANG or a comparable company. - Experience with PyTorch, JAX, or TensorFlow. - Deep expertise in at least one focus area: pretraining, PPO, reward shaping, fine-tuning, LoRA, RLHF, architecture design, contrastive training, generative modeling, multilingual experience, or data pipelines. Requirements - Practical experience in Pretraining, Reinforcement learning, Post-training, Dataset curation, or Model architecture. - One baseline attempt per contractor per task. - Each task may only be attempted once. - All work is confidential and covered by NDA. - Compute and environment are provided; no personal GPU required. Application Process - Upload resume - AI interview based on your resume - Submit form Resources & Support - For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome - For any help or support, reach out to: support@mercor.com - Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.

Role Description Onpoint Healthcare Partners builds Iris, a Medical Agent AI platform that takes administrative work off the plates of providers and care teams. Our agents handle charting, coding, care gap closure, and care coordination across the full patient journey, combining AI automation with expert clinical oversight. More than 2,000 providers across 35+ specialties rely on the platform every day, which means the systems behind it have to be accurate, reliable, and auditable without exception. We are seeking a Staff AI Engineer to design, build, and scale the agent systems behind Iris. This role combines strong software engineering fundamentals with deep experience building production AI systems. The work you ship directly determines how much time providers get back for patient care. The ideal candidate has shipped AI solutions to production that deliver measurable business value, and will evolve and be a good steward of the agent platform architecture. Key Responsibilities - Design and develop AI applications and agent workflows. - Build scalable backend services, APIs, and integrations supporting AI solutions. - Evolve and steward the agent platform architecture, including orchestration, runtime safety, and prompt governance. - Treat prompts and tool schemas as versioned code with staged rollouts and rollback. - Develop and maintain evaluation frameworks for AI agents and models, with CI gates that block bad changes before release. - Design retrieval, memory, and context management strategies. - Build observability, monitoring, and debugging capabilities, including full trace and replay for incident resolution. - Build the MLOps foundation the team is currently missing: training and retraining pipelines, model versioning, model registry, and feature stores, so model work stops being reactive. - Stand up A/B testing infrastructure and automated drift detection that triggers retraining, so model quality is monitored and maintained without manual firefighting. - Track token and tool costs per run, workflow, and tenant, and keep costs predictable as usage scales. - Improve reliability, performance, safety, and cost efficiency of production AI systems. - Partner with Product, Clinical, Data Science, and Engineering teams to deliver AI capabilities. - Own AI deployment, monitoring, and operational excellence. Qualifications - 8+ years of software engineering experience. - 3+ years building AI and ML applications. - Strong Python and/or C# development experience. - Experience deploying AI systems into production environments. - Experience with LLMs, RAG architectures, agent frameworks, and AI evaluation. - Experience with AWS and distributed systems. - Hands-on experience building MLOps infrastructure such as training pipelines, model registries, feature stores, A/B testing, and drift detection. - Strong debugging, observability, and operational skills. Preferred Qualifications - Healthcare industry experience. - Experience with HIPAA, PHI, and regulated environments. - Experience with vector databases, embeddings, and knowledge graphs. - Experience building AI systems at large scale. Success Metrics - Reliable, scalable, and cost efficient AI systems running in production. - Incidents get resolved fast because tracing and replay make root cause obvious. - Cost per workflow stays predictable as volume grows. - Model training, versioning, and retraining run as a managed pipeline rather than a reactive, manual effort. - Strong adoption and measurable business impact. - High trust in AI quality, safety, and operational performance. Physical Demands The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. While performing the duties of this job, the employee is regularly required to speak, hear, read, and type. This is largely a sedentary role. This position requires the ability to occasionally lift office products and supplies up to 40 pounds. Work Environment To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed above are representative of the knowledge, skill and/or ability required. Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities that are required of the employee for this job. Duties, responsibilities, and activities may change at any time, with or without notice.