• Work closely with data science, ML/DevOps, and engineering teams to enhance AI infrastructure • Improve and enhance predictive models • Test hypotheses and present findings to management • Develop understanding of Sift’s data and provide analysis • Stay up to date with current ML and GenAI trends • Collaborate with Data Scientists, ML & Dev Ops, Engineers, and Product teams to integrate capabilities

Location: Remote (Global) Reports to: Head of Product Company: Supernal Type: EOR FTE or Contractor Rate: $50/hr About Supernal At Supernal, we help SMBs hire their first AI employee. Our AI teammates are built with intelligent, agentic workflows and deployed on our proprietary platform. We don't build tools — we deliver working, value-generating AI Employees. Our AI Platform Engineers, known internally as Masons, are the builders behind these systems. Now, we're looking for a Senior Mason to help lead this craft. The Role As a Senior AI Platform Engineer, you'll be on the frontlines of our most critical customer implementations, with a strong focus on conversational AI agents deployed in real business environments. You'll design, build, and deliver agentic systems that handle live users, multi-turn conversations, real-time constraints, and complex integrations. These are not demos or experiments — they are production systems that customers rely on. Beyond hands-on engineering, you will act as a technical owner for client delivery. You'll translate customer requirements and SOWs into working systems, own delivery timelines, manage technical tradeoffs, and ensure successful outcomes in production. This is a hands-on role. You're not just reviewing PRs or sitting in meetings — you're in the weeds, building systems, debugging failures, and showing others how it's done. Responsibilities - Build advanced AI agent workflows on n8n and Supernal's proprietary platform - Design, implement, and deploy conversational agents, including multi-turn flows, state management, and tool usage - Own end-to-end technical delivery for high-priority customer implementations, from architecture through production launch - Translate customer requirements and SOWs into clear technical designs, execution plans, and deliverables - Make and own architectural decisions across LLM orchestration, RAG design, API integrations, and workflow decomposition - Handle real-world voice system challenges including latency, interruptions, fallbacks, error handling, and failure recovery - Write automated tests — unit tests for isolated logic and end-to-end tests for full workflow and user journey validation - Apply solid error handling: distinguish retryable vs. fatal failures, surface meaningful error messages, and avoid silent failures - Actively debug complex production issues across agent logic, prompts, integrations, and external dependencies - Partner with delivery and product leadership to manage timelines, scope, and technical tradeoffs during implementation - Review technical work for quality, scalability, and maintainability, setting a high bar for engineering excellence - Define, document, and evolve best practices for building and delivering reliable AI Employees You Might Be a Fit If You... - Have 4+ years of experience as a software engineer, automation engineer, or systems builder shipping production systems - Have hands-on experience deploying voice agents using leading platforms (e.g., ElevenLabs, Retell, Nextiva), including telephony and streaming audio integration patterns - Understand multi-turn conversation design: state management, context windows, interruption handling, and graceful recovery - Have tackled real-time constraints in production: latency budgets, streaming audio, fallback paths, and API chaos - Write automated tests as a matter of course — unit tests, integration tests, and end-to-end workflow validation — and treat testing as part of shipping, not an afterthought - Apply solid engineering fundamentals: error handling, retry strategies, separation of concerns, and clean interfaces between components - Are comfortable owning delivery outcomes end-to-end — not just writing code — including timelines, reliability, and client success - Have deep experience with agentic architectures, workflow automation platforms (n8n, Zapier, Make), and APIs - Understand LLM orchestration, prompt engineering, function calling, and retrieval-augmented generation (RAG) - Can prototype fast and finish the job to production quality — with tests, error handling, monitoring, and runbooks - Are an elite debugger who can reason through edge cases, flaky agents, and real-world API failures - Communicate clearly and fluently in English — both in writing and verbally — especially when articulating technical decisions, tradeoffs, and implementation choices to technical and non-technical stakeholders alike - Can run meetings, drive decisions, write crisp updates, and ask the right questions early — without needing heavy process - Thrive in fast-paced, ambiguous startup environments and take ownership without being asked - Bring a low-ego, high-integrity approach to collaboration and leadership What Success Looks Like - Voice-first AI Employees are delivered on time, meet customer requirements, and perform reliably in production - Client implementations are predictable, well-architected, and resilient under real-world conditions - Complex conversational and voice workflows behave consistently and recover gracefully from failure - Code is well-tested, well-documented, and maintainable — not just functional - Technical decisions are communicated clearly and proactively to stakeholders, with tradeoffs explained and risks surfaced early - Engineering best practices reflect real production learnings and are widely adopted across the Mason team - Delivery artifacts — runbooks, SOPs, reusable components — raise the bar for the whole team

Sift Healthcare is seeking an AI Engineer to join our highly collaborative, growing analytics group. This hire will work closely with our data science, ML/DevOPs, and engineering teams to enhance Sift’s machine learning and AI infrastructure, techniques, and practices. Sift is transforming healthcare payments by equipping healthcare organizations to fully leverage their payments data to work smarter, protect their margins and accelerate cash flow. Large health systems, leading HCIT vendors, payers and outsourced revenue management agencies use Sift's Payments Intelligence Platform, ML integrations and advanced analytics to optimize payment outcomes and enable data-driven decision-making within the revenue cycle. The AI Engineer is a key member of the Data Science team, responsible for improving and enhancing Sift’s suite of predictive models and operations that work to solve complex healthcare payment problems. We are seeking a candidate with 5+ years of experience in an AI or deep learning role, with an advanced degree (MS or higher) in a related field. A key attribute of the employee will be their ability to test hypotheses and present findings to upper management, both internally and externally, and help other team members do the same. They must be collaborative and have strong communication skills. The employee must develop a deep understanding of the data Sift utilizes, provide advanced analysis, and suggest how Sift Healthcare can improve current AI and deep learning techniques and infrastructure. The employee is expected to remain up to date with current ML and GenAI trends. Experience working with PII/PHI/HIPAA data is required. Experience working with healthcare data is required. A Wisconsin resident is preferred for this role. RESPONSIBILITIES - Generative AI & LLMs, Monitoring and Observability: Utilization and deployment of LLMs for clinical decision support, and revenue-cycle automation. Implement systematic LLM evaluation and monitoring, including task-level metrics, LLM-as-a-judge scoring, and detection of input, embedding, and output drift to ensure stable production performance. - Retrieval: Build and optimize RAG pipelines, including document ingestion, chunking, embeddings, and retrieval. Implement evaluation and monitoring frameworks to assess retrieval and generation quality and detect drift in data, embeddings, and outputs. - Deep Learning: Research, develop, and optimize deep learning models for healthcare revenue-cycle applications, from experimentation and training through evaluation and assisting with deployment. Leverage embedding architectures such as TitanV2 and ClinicalBERT variants, along with other transformer-based models, to understand clinical documentation, denials, and payer communications. - Unsupervised & deep clustering: Design deep clustering pipelines, leveraging techniques such as HDBSCAN for scalable density-based clustering and IDEC (Improved Deep Embedded Clustering) for joint representation learning and clustering. - Best practices & compliance: Uphold rigorous code quality standards, conduct peer reviews, adhere to Git workflows, and ensure HIPAA-compliant handling of PII/PHI data. - Cross-functional collaboration: Partner with Data Scientists, ML & Dev Ops, Engineers, and Product teams to integrate deep learning capabilities into our platform and drive data-driven features. QUALIFICATIONS - Educational background: Bachelor’s or Master’s in Computer Science, Engineering, or related field; advanced degree strongly preferred. - Hands-on Deep Learning and GenAI experience: 5+ years building and deploying GenAI or deep learning models (TensorFlow, PyTorch) in production settings. - LLM & NLP expertise: Practical experience with transformer architectures, prompt engineering, and retrieval-augmented generation workflows. - ML Ops proficiency: Familiarity with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines. - Healthcare data knowledge: Working understanding of claims, clinical documentation, and HIPAA requirements for PII/PHI data. - Coding skills: Strong Python proficiency, debugging skills, Git-based version control, and collaborative code review. - Communication & teamwork: Excellent written and verbal communication, with the ability to translate complex models into business impact. - Management experience a plus. COMPENSATION Compensation will be based on skills, experience, and performance. ABOUT SIFT HEALTHCARE Sift is a data science company working to improve payments operations and outcomes in the healthcare industry. We are a growing and dynamic team that is serious about technology. Based in Milwaukee, Wisconsin, Sift is thriving and looking for motivated team members who will help shape our culture. Sift offers competitive salaries and benefits. Learn more about Sift at www.sifthealthcare.com.

About the Job As Splitero's Applied AI Engineer, you will own the identification, scoping, and delivery of AI-powered capabilities across our product and operations. You'll sit within the engineering organization but operate cross-functionally, partnering with product, operations, and business teams to identify where AI creates real leverage, then build it. Splitero sits at the intersection of financial services and property technology, and our product and engineering teams are at the center of that innovation every day. In this role, you'll work directly with our sales, processing, underwriting, closing, and servicing teams to identify, scope, and execute opportunities to streamline and automate their workflows as we scale. On the engineering side, you'll champion AI-first development, educate the team on emerging tooling, and lead technical initiatives that drive the organization forward. You'll spend your time shipping working AI solutions into production by automating workflows, augmenting our product experience, and helping Splitero move faster and smarter as a business. You will have a strong understanding of when supervised AI outweighs the risk of full automation. You're as comfortable standing up a low-code n8n automation as you are writing clean code, and you know when each approach is the right one. At Splitero, we move fast, and the ideal candidate thrives in that environment. You default to the simplest path to a working solution, prototype quickly using every tool available to you, and have the engineering discipline to know when something needs to be rebuilt properly before it goes to production. Responsibilities: - AI Discovery & Strategy - Socialize AI capabilities, limitations, and roadmap across the org, acting as an internal resource and thought leader on what's possible. - Maintain a prioritized backlog of AI opportunities across the business, triaging ideas from leadership, product, and operations into scoped, deliverable workstreams. - Define AI initiatives end-to-end: problem framing, data requirements, build vs. buy decisions, and measurable success criteria. - Stay current on the AI tooling landscape and recommend adoption where it meaningfully improves velocity, quality, or competitive positioning. - Building & Delivery - Build, integrate, and maintain AI-powered features and workflows, leveraging LLM APIs (OpenAI, Anthropic, etc.), RAG pipelines, and agentic frameworks. - Develop automation workflows using low/no-code orchestration tools (n8n, Make) to deliver AI solutions rapidly without requiring full engineering cycles. - Integrate AI capabilities into existing systems and codebases in close partnership with the product and engineering teams. - Own the full AI development lifecycle, including prototyping, evaluation, deployment, monitoring, and iteration, with support from engineering, product, and operational teams. - Cross-Functional Partnership - Partner with product to translate user problems into AI-driven features and experiences. - Embed within operations and business teams to identify workflow automation and efficiency opportunities. - Collaborate with engineering to ensure AI integrations meet production standards for reliability, maintainability, and security. - Document and communicate AI implementations clearly so the broader team can support and build on them. Reporting Structure - Reports directly to the VP of Product and Engineering - Embedded within the product engineering team, works cross-functionally across Salesforce, product, operations, and business functions The salary range for this position is $135,000 - $175,000. Within the range, individual pay is determined by job-related skills, experience, and relevant education or training. About You - 6+ years in software engineering with at least 2 years of focused experience building production AI or LLM-powered applications - Hands-on experience with LLM APIs, prompt engineering, retrieval-augmented generation (RAG), and agentic workflow design. You are on the bleeding edge of the latest releases and advancements in AI platforms. - Demonstrated experience evaluating, advocating, and operationalizing AI tooling across an organization - Proficiency with low/no-code orchestration platforms (n8n, Make, Zapier) to prototype and deliver AI workflows rapidly - Comfortable with AI-assisted development tools (Claude Code, Cursor, GitHub Copilot, etc.) to accelerate build cycles, with the judgment to write clean, maintainable code when it matters. You are not a vibe coder; you leverage your engineering background to balance speed with quality using AI-based tooling. - Strong backend or full-stack engineering fundamentals; you integrate AI into real systems, not just prototypes - Fluency in Python, TypeScript/Node.js, or similar; familiarity with PostgreSQL and cloud deployment platforms (AWS, Vercel, or similar) - Experience with multi-agent frameworks (LangChain, LlamaIndex, or similar) - Familiarity with vector databases, embedding pipelines, and evaluation frameworks for LLM outputs - Self-directed and comfortable operating as a team of one. You run your own backlog and don't need a fully defined spec to get started and validate business use cases - Strong communicator who can translate technical AI concepts for non-technical stakeholders across the org Preferred: - Experience in fintech, proptech, or real estate technology — particularly products handling sensitive financial data or consumer lending. - Knowledge of SOC 2 or data privacy frameworks relevant to financial and personal data. About Us Millions of homeowners in today’s economy are sitting on substantial home equity they are unable to access due to rising interest rates and strict qualification requirements of traditional lenders. That means millions of homeowners can’t convert their hard-earned equity into cash to pay off debt, start a home renovation, or pad their retirement to better their lives. Splitero co-founders Michael Gifford and David Zvaifler witnessed the problem firsthand throughout their real estate careers. They set out to create a win-win product that could help those homeowners access their home equity but not make monthly payments like traditional financing options. And thus, Splitero was born. As part of our team, you will join our quest to revolutionize the real estate industry while helping families in need. We offer a suite of benefits designed to help you thrive in all areas of life. - Fully Remote Team - Our remote-first culture allows us to hire the best people, regardless of location. - OneTeam Culture - We foster a culture of transparency, innovation, and inclusivity, where every voice is heard and every team member is valued. We believe in celebrating our successes, doing the right thing, and helping each other so we grow as a team and as individuals. - Generous Resources - We want to ensure you are set up to do your best work. In addition to a solid tech stack, and access to any WeWork location, you will receive a monthly cell and internet stipend. - Premium Healthcare - We offer comprehensive healthcare plans for you and your family, which employees are eligible for on their first day. - Flexible PTO and Parental Leave - We offer a generous PTO and encourage employees to take time off to recharge. We're proud to offer a parental leave policy that allows you to welcome your new addition without worry. We are a workforce composed of individuals with diverse backgrounds, abilities, minds, and identities. In keeping with our commitment to inclusion, we will ensure that people with disabilities are provided reasonable accommodations. If reasonable accommodations are required when applying, interviewing, or performing the essential functions of this position, please contact People Operations, at peopleops@splitero.com.