Requisition Number: 2347560 Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together. We are seeking a highly skilled AI/ML Engineer with expertise in delivering Natural Language Processing (NLP), Optical Character Recognition (OCR), Machine Learning (ML), and cloud-based AI solutions to join our dynamic team. The ideal candidate will play a pivotal role in transforming our workflows through AI-driven automation and efficiency, particularly in the modernization of fax processes and telephony systems. This role requires a strong technical foundation, critical thinking, ethical judgment, and collaborative skills to drive impactful solutions aligned with organizational goals and societal values. You'll enjoy the flexibility to work remotely * from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week. Primary Responsibilities: - Architect, deliver hands-on, mentor & review other team members and deliver on timelines with all dependencies and innovative needs thought out, coordinated with cross-team stakeholders - Work with product management, design, support and other dev teams members to create high-quality solutions - Solve difficult and sometimes ambiguous problems - Be a team player, show due diligence, your own creativity, take team input, own & design deliverables with deep security expertise You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in. Required Qualifications: - Undergraduate degree or equivalent experience - 3+ years of experience working with Natural Language processing - 3+ years of experience with Deploying and manage ML models on cloud platforms such as Microsoft Azure, AWS, Google Cloud Platform (GCP) - 3+ years of experience leveraging cloud-native tools like Azure Machine Learning, AWS SageMaker, GCP AI Platform for model training, deployment, and monitoring - 2+ years of experience working with Retrieval-Augmented Generation Preferred Qualifications: - 8+ years of AI/ML Engineering experience - Experience with Optical Character Recognition (OCR) for document digitization and data extraction - Experience with Intelligent document processing - Experience working in accordance with Dev/Sec/Ops principles, and familiarity with industry deployment best practices using CI/CD tools, MLOps, LLMOps and infrastructure as code (Jenkins, Docker, Kubernetes, Git etc.) - Deep experience with additional server-side frameworks and technologies such as FastAPI, Node.js, Flask, Django etc. - Expert knowledge of Python/SQL and AI/ML libraries (tensorflow/PyTorch/SciKit/Seaborn etc.) Familiarity with telephony systems, including voice processing, speech recognition, and platform integration - Proven track record of shipping and maintaining production, enterprise scale AI applications and data science products - Proven solid computer science fundaments in data structures, algorithms, automated testing, object-oriented programming, performance complexity, and implications of computer architecture on software performance *All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far-reaching choice of benefits and incentives. The salary for this role will range from $91,700 to $163,700 annually based on full-time employment. We comply with all minimum wage laws as applicable. Application Deadline: This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants. At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission. UnitedHealth Group is an Equal Employment Opportunity employer under applicable law and qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations. UnitedHealth Group is a drug - free workplace. Candidates are required to pass a drug test before beginning employment.

AI alignment isn't bottlenecked by capability. It's bottlenecked by trust. Current AI fails in high-stakes contexts because it was never trained for relational competence. Models see knowledge, essays, code, and engagement-optimized social media — but never longitudinal healthy relating. And the objective function optimizes for engagement and utility, not relational integrity. The result? Addiction, sycophancy, psychosis, and suicide lawsuits. Plus, it's doing nothing to improve our social fabric, the strength of our communities, or the health of our relationships. Mental health apps see single-digit AI conversion despite massive demand. Robotics companies can't deploy into homes because LLMs can't handle human nuance. Defense and healthcare won't adopt AI that fails under stress. The opportunity: Whoever solves relational coherence wins consumer social AI, robotics, mental health, defense, education — and any domain where humans are vulnerable, the pressure is real, and mistakes matter. What we're pioneering: An orchestration engine based on research-backed Cumulative Prospect Theory and a model trained on empirical signals of human thriving — what we're calling Relational Reinforcement Learning (RRL). Your role: Build the execution layer that translates relational intelligence into natural conversation. This isn't a better chatbot. It's infrastructure for trustworthy AI in domains where the stakes run high and LLMs aren't good enough. What You’ll Build Better Half's decision layer (built by our CTO, former Distinguished Engineer at IBM) determines when users need pushback, softening, or repair. But decisions mean nothing without execution. You'll build the system that translates those decisions into LLM behavior that feels natural while optimizing for user thriving, not engagement.​ Affect Analysis Pipeline - Real-time emotion detection that feeds the decision layer - Multi-modal sentiment analysis (text, voice, timing, interaction patterns) - Track escalation/de-escalation signals across conversations Memory Systems - Storage and retrieval of relational context across sessions - Consistency checking to maintain relational coherence - Long-term modeling of user growth trajectories and relational capacity Prompt Engineering & Constrained Generation - Translate high-level decisions ("user needs reality-checking without triggering defensiveness") into effective prompts - Constrained generation that balances warmth with necessary friction - Template systems that adapt to user state and relationship phase LLM Integration & Orchestration - Multi-model orchestration (Mistral, Claude, others as needed) - Latency optimization for real-time conversation - Fallback strategies and graceful degradation ​Cloud Infrastructure - AWS or GCP architecture that scales - PostgreSQL and vector stores for memory - Privacy-preserving processing pipelines (on-device where possible) Evaluation & Monitoring - Build eval harnesses that measure relational outcomes, not just fluency - Tracing and feedback loops to ensure decisions land as intended - A/B testing framework for relational interventions Your Core Challenge ​​Turning complex relational intelligence into natural conversation that makes people feel understood while actually helping them grow — without drifting into sycophancy or breaking immersion with safety theater. Skills You'll Need​ - Production-grade Python with async and clean architecture - LLM integration including prompt engineering, constrained generation and orchestration - Transformer models and sentiment/emotion classifiers - Cloud infrastructure on AWS or GCP, PostgreSQL, vector stores - Eval and monitoring with harnesses, tracing, and feedback loops - Implementing technical specs from academic papers - Multimodal inputs - Self-starter comfortable with 0-1 startup chaos Also Valuable​ - Utility theory and behavioral economics - Reinforcement learning - Game AI or dialogue systems experience - Rust Apply Competitive contractor compensation and meaningful founding engineer equity. Send your resume and something you've built to jobs@better-half.ai. A link to code, a project, a writeup. Show us how you think.

💡 MissionThe US healthcare system is complex, error-prone, and financially draining. Medical bills and insurance coverage shouldn’t be this hard to navigate. At Granted, we’re building the one solution every American can turn to for help. Thanks to AI and new regulations, Granted can fight claim denials, correct billing errors, negotiate bills, and make coverage easier to understand—saving people time, money, and stress. Our goal is simple: to be the #1 platform that empowers all Americans to take charge of their healthcare. 🩺 About UsFounded by a former Oscar Health leader, we’re a seed-stage company with $17M in funding. We’re lucky to be backed by the founders and investors at Hugging Face, Rocket Money, Oscar Health, CaseText, Forerunner Ventures, RRE Ventures, and more. We are well-funded for the next few years. 👉 Curious about our engineering culture + tech stack? Check out this write-up. Location: While parts of our engineering team operate remotely elsewhere in the US, we strongly encourage New York–based applicants who are excited to engage in a more in-person hybrid dynamic. Local employees typically work from our Chelsea office Monday through Thursday, with Fridays working from home. 🔎 About the RoleThe Automations pod turns complex healthcare case work into scalable, agentic workflows — driving automation coverage, lowering cost to serve, and ensuring users get fast, accurate outcomes without needing to navigate the system themselves. As an engineer on this team, you’ll help build the intelligence layer behind these workflows — designing distributed systems, integrating AI models, and ensuring everything runs reliably at scale. 🚀 What You’ll Do - Design and build scalable systems and APIs that power agentic workflows from case activation through resolution. - Push the frontier of AI-driven engineering, working on prompt orchestration, agent frameworks, and model evaluation loops. - Collaborate closely with Product, Engineering, and domain experts to translate complex healthcare processes into reliable automations. - Architect systems for scale and reliability, ensuring human escalation pathways are transparent and seamless. - Play a key role in shaping the pod’s technical strategy, and mentor others as the team grows. 🧪 What You Might Work On - Creating and optimizing AI pipelines to identify, categorize, and resolve errors in medical billing and insurance claims. - Building automated agents and backend services that handle complex case flows with minimal human intervention. - Integrating domain-specific healthcare knowledge into models and decision engines to improve accuracy. - Developing tooling and infrastructure to measure and improve AI performance in real-world scenarios. 👩‍💻 We'll be most excited if you - Bring 9+ years of backend engineering experience, ideally building production systems at scale. - Are deeply curious about AI — from prompt engineering and agent orchestration to model evaluation. - Excel at designing pragmatic solutions to complex technical challenges. - Communicate clearly and collaborate effectively across disciplines. - Thrive in high-autonomy, fast-moving environments. - (Bonus) Have experience with healthcare data, product engineering, ML systems, or web crawling. - (Extra bonus) Have 10+ years of GPT-5 experience — or, failing that, a good sense of humor. 📢 You don't need to check every single box—if you're passionate, driven, and excited about the impact AI can make on the healthcare system, we'd love to hear from you! Granted is proud to be an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity or expression, age, protected veteran status, disability, or any other legally protected characteristics. We are also committed to providing reasonable accommodations to qualified individuals with disabilities. If you need assistance or an accommodation during the application or interview process, please contact us.

• Design, build, and deploy AI agents tailored to the needs of all internal teams (Ops, Partner Management, SEA, Finance, and more) • Take ownership of our AI-driven lead qualification processes, including existing Voice AI Agents used to qualify inbound leads • Continuously improve qualification performance through systematic A/B testing and iteration • Use tools like Lovable to build, extend, and improve internal tools — fast and pragmatically • Turn manual processes into slick, automated solutions • Manage and evolve our internal AI stack • Enable every team member to leverage AI effectively in their daily work — through documentation, training, and hands-on support • Build and maintain automation workflows using Zapier and Make to eliminate bottlenecks across the company • Set up and manage webhooks and API integrations to ensure seamless system communication