• Build AI systems that power a scientific discovery platform for our federal client. • Develop conversational interfaces that allow users to access complex, multi-domain scientific datasets using natural language. • Design AI agents that automate experimental data processing at scale. • Implement retrieval‑augmented generation (RAG) architectures to transform decades of scientific research into actionable intelligence. • Collaborate with stakeholders to solve one of the client’s 26 “lighthouse challenges” requiring faster, smarter decision-making. • Work across technologies such as AWS Bedrock, Databricks, vector databases, and advanced prompt‑engineering techniques. • Build agent frameworks supporting scientific discovery in areas like subsurface geology, supply chain intelligence, and materials science.

• Translate COBOL code logic and requirements into clear, standardized documentation using IBM WatsonX. • Produce detailed documents outlining logic, relationships, data flow, and data architecture. • Work closely with Business Owners and developers to understand documentation needs and ensure accuracy. • Serve as primary point of contact for client executives and technical leaders regarding project status and strategic decisions. • Monitor project timelines, milestones, and resource allocation to meet client expectations. • Manage client expectations about AI capabilities, limitations, and delivery timelines. • Lead client status meetings and internal project meetings. • Oversee validation processes for AI‑generated documentation to ensure quality before delivery. • Collaborate with development teams to understand legacy system architecture. • Assist stakeholders in designing and implementing secure connectivity between mainframe systems and the IBM WatsonX platform. • Partner with WatsonX Engineers to fine‑tune AI models with environment‑specific logic and integration details. • Support WatsonX Engineers with mapping and documentation of program interdependencies across integrated systems.

Who We Are Yieldmo is an advertising platform that helps brands invent creative experiences through tech and AI, using custom ad formats, proprietary attention signals, predictive format selection, and privacy-safe premium inventory curation. Yieldmo believes all ads should be human-centered, tailored, and provoke users' emotions and actions. Yieldmo helps brands deliver the best ad for every impression opportunity, merging creative and media for proven results. What We Need We’re building a general-purpose, AI-powered search engine that will redefine how users discover and engage with content across major publishers. We’re looking for engineers to join the team building it — people who want hands-on ownership of real problems in retrieval, ranking, data, and ML infrastructure at scale. This is a generalist role, and we’re open to strong candidates from multiple backgrounds. We are hiring across a range of seniority levels (mid-senior through staff) and are specifically interested in engineers who fit one of the following profiles: - ML-leaning engineer: Strong machine learning foundations with solid applied / backend engineering skills — you’ve shipped ML systems into production, not just notebooks. - Data / ingestion-leaning engineer: Strong data engineering and large-scale ingestion background, with ML as a working secondary skill — you’re comfortable picking up models, embeddings, and evaluation pipelines. - Search-leaning engineer: Strong search engineering with working, hands-on understanding of data, ML, and ingestion — you’ve built or meaningfully contributed to real search or retrieval systems end-to-end. Across all three paths, we care most about builders — engineers who write code, iterate quickly, make pragmatic tradeoffs, and raise the bar for the people around them. What You Can Expect In This Role - Design, build, and operate core components of Yieldmo’s AI-driven search engine — retrieval, ranking, indexing, ingestion, or ML infrastructure, depending on your strengths. - Be a hands-on builder: writing production code, iterating quickly, and owning systems from prototype through scale. - Partner closely with Product, ML, and Engineering teams to integrate modern retrieval, ranking, and recommendation technologies (LLMs, embeddings, vector search, RAG). - Contribute to the technical direction of the search platform and influence architectural decisions within your area. - Build and operate large-scale data and content ingestion pipelines that feed the search system. - Drive quality, performance, relevance, and reliability bars for the features and services you own. - Mentor peers and, for more senior candidates, grow into tech-lead responsibilities as the team scales. Requirements We expect every candidate to meet the core bar below, plus go deep in at least one of the three specialty tracks that follow. Core - Strong software engineering fundamentals and production experience building and operating backend systems at scale. - Proficiency in Python and SQL; comfort with Docker and microservices architectures. - Working familiarity with modern AI/search building blocks: LLMs, embeddings, vector databases, retrieval-augmented generation (RAG), function/tool calling. - Ability to work cross-functionally in a fast-moving environment, with excellent written and verbal communication. - A hands-on, ownership-oriented mindset — you ship. Track 1 — ML-leaning - Strong ML foundations: ranking/relevance, embeddings, representation learning, or LLM fine-tuning and evaluation. - Proven track record shipping ML systems to production, including training pipelines, model serving, and online/offline evaluation. - Solid applied engineering: you can own the backend and infra around your models, not just the modeling. Track 2 — Data / ingestion-leaning - Strong data engineering background: large-scale ingestion, streaming and batch pipelines, data modeling, and storage/query optimization. - Experience with distributed data systems (e.g., Kafka, Spark, Flink, Airflow, or equivalents) and modern data lake / warehouse architectures. - ML as a working secondary skill — comfortable integrating embeddings, feature pipelines, and model outputs into data workflows. Track 3 — Search-leaning - Hands-on experience designing or building search / retrieval systems — indexing, query processing, ranking, and relevance. - Working knowledge of both classical (inverted index, BM25, learning-to-rank) and modern (dense retrieval, hybrid search, rerankers) approaches. - Practical understanding of the data and ML layers that feed a search system, enough to debug and improve them end-to-end. Hiring Process Select candidates will be invited to schedule a 30 minute screening call with a member of our Talent Acquisition team. We will discuss the Hiring Process details at that time. The hiring process typically includes, but is not limited to: - A 30 minute video interview with the Hiring Manager. - Candidates will be invited to join a remote on-site interview round, consisting of video interviews with various team members and leadership. - Successful candidates will subsequently be made an offer. Nice to Haves - Exposure to or direct experience at leading AI/search organizations (OpenAI, Anthropic, Perplexity, xAI, Google DeepMind, etc.). - Experience with publisher-scale content, recommendation systems, or adtech. Our Values INNOVATION: We encourage curiosity, embrace new ideas, and believe no idea is too bold. AGILITY: We embrace change, act quickly, and adapt with a focus on getting things done. INTELLIGENCE: We make decisions guided by data, always aiming to deliver maximum value to our customers. AUTONOMY: We empower individuals to create their own paths with flexibility and independence. TOGETHERNESS: We foster an environment where teamwork thrives, support is mutual, and every voice matters. What We Offer We believe that diverse people and perspectives lead to breakthrough ideas, therefore we provide comprehensive benefits and an inclusive culture to support our valued team members. - Remote Work: Our team is fully distributed, though we love an opportunity to get together at our annual offsites, holiday parties, and more. - 100% Company Paid Health Coverage: Choose the medical, dental, and vision plan that’s best for you and your family – all with options for 100% company paid coverage. - 401(k) Plan: Invest in yourself by participating in our 401(k) plan with a company match. - Equity: Share in Yieldmo’s success through our employee stock option program. - Flexible Time Off, Company Slowdowns, and Summer Fridays: Take time off to relax and rejuvenate on your own terms with flexible time off, multiple company slowdowns, and Summer Fridays. - Home Office Setup and Stipend: Setup your home office for success with our premium technology packages and an additional stipend for any extra needs. - Professional Development: Grow your hard and soft skills with our annual professional development stipend. US Jobs: The base salary range for this role is: $200,000-$250,000 per year. The range listed is just one component of Yieldmo's total compensation package for employees. Individual compensation decisions are based on a number of factors, including experience, level, skillset, and balancing internal equity relative to peers at the company. We recognize that the person we hire may be less experienced (or more senior) than this job description as posted. In these situations, the updated salary range will be communicated with you as a candidate. For all other countries, we have competitive pay bands based on market standards.

About this Role: We are seeking a Senior Full Stack Engineer who is an absolute expert in AI-assisted development. You are a power user of tools like Claude, Cursor, or Copilot, and you understand that "Prompt Engineering" is actually "Context Engineering." Your primary goal is to build and scale our commerce platform using .NET Core and React at a pace that traditional development cannot match. You will be responsible for defining the patterns that allow AI to generate high-quality, testable, and secure code consistently. About KIBO Commerce: KIBO is a composable digital commerce platform for B2C, D2C, and B2B organizations who want to simplify the complexity in their businesses and deliver modern customer experiences. KIBO is the only modular, modern commerce platform that supports experiences spanning B2B and B2C Commerce, Order Management, and Subscriptions. Companies like Ace Hardware, Zwilling, Jelly Belly, Nivel, and Honey Birdette trust Kibo to bring simplicity and sophistication to commerce operations and deliver experiences that drive value. KIBO's cutting-edge solution is MACH Alliance Certified and has been recognized by Forrester, Gartner, IDC, Internet Retailer, and Trust Radius. KIBO has been named a leader in The Forrester Wave™: Order Management Systems, Q1 2025 and in the IDC Market Scape report “Worldwide Enterprise Headless Digital Commerce Applications 2024 Vendor Assessment”. By joining KIBO, you will be part of a team of Kibonauts all over the world in a remote-friendly environment. Whether your job is to build, sell, or support KIBO’s commerce solutions, we tackle challenges together with the approach of trust, growth mindset, and customer obsession. If you’re seeking a unique challenge with amazing growth potential, then come work with us! What You'll Do: - Rapid Feature Prototyping: Use AI agents to move from a concept or "vibe" to a working MVP in hours, not weeks. - Context Management: Expertly manage IDE context (using .cursorrules, MCP servers, or project indexing) to ensure AI outputs adhere to Kibo's specific architectural standards. - Full-Stack Orchestration: Lead the development of complex .NET microservices and React frontends, utilizing AI to handle 80% of the manual coding while you focus on the critical 20% of logic and integration. - AI-First Testing: Utilize AI to generate comprehensive test suites (nUnit, Jest, Playwright) that ensure high coverage and prevent regressions in an accelerated delivery environment. - Code Quality & Review: Act as the final gatekeeper, ensuring that AI-generated code is not just "functional" but follows SOLID principles, security best practices, and performance standards. - Database & Messaging: Architect data solutions in PostgreSQL and MongoDB, using AI to optimize queries and implement RabbitMQ event patterns.