WHO WE ARE Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com. The Role The Zeta AI R&D team sits at the heart of our technology and delivers on Zeta Global’s core brand promise: state-of-the-art marketing technology innovation. What You Will Do Zeta Global’s technology platform supports a real-time bidding system handling over 100 billion events per day, a reporting system aggregating and analyzing terabytes of data in real time, and a learning system applying machine learning and AI techniques to more than 40 petabytes of data. Together, these systems ensure Zeta serves the right advertisement to the right user at the right time. At the core of this platform is our Artificial Intelligence and Machine Learning team, which builds the models and decision-making systems that power Zeta’s products. This role is focused on post-training and alignment of large language models, with an emphasis on Supervised Fine-Tuning (SFT), preference optimization, and production operation of agentic LLM systems. As a Lead AI Engineer, you will own the end-to-end post-training lifecycle of LLMs—from SFT data curation and training strategy through evaluation, deployment, monitoring, and iteration—using platforms such as OpenAI and AWS Bedrock. Specifically, you will: - Lead Supervised Fine-Tuning (SFT) of large language models in production, shaping instruction-following, reasoning quality, tone, and domain-specific behavior - Extend SFT pipelines with instruction tuning and preference-based optimization (e.g., RLHF-style approaches or direct preference optimization) - Design, curate, and maintain high-quality SFT and preference datasets, combining human-labeled and synthetic data tailored to real-world marketing and decisioning use cases - Own model evaluation and benchmarking, including: - Offline behavioral evals (instruction adherence, reasoning depth, hallucination rates) - Online experiments and A/B tests - Continuous regression detection and performance monitoring - Develop and operate agentic LLM systems, enabling multi-step reasoning, tool use, workflow orchestration, and decision execution - Implement and optimize prompting, retrieval-augmented generation (RAG), memory, and tool-calling strategies, with a clear understanding of when to solve problems via SFT versus prompting - Partner closely with data engineering, platform, and product teams to integrate fine-tuned models into high-throughput, low-latency systems - Establish best practices for LLM versioning, experimentation, deployment, rollback, governance, and safety - Provide technical leadership and mentorship to engineers working on applied AI and LLM systems Who You Are You are deeply interested in how LLM behavior changes through SFT and post-training, especially in real production environments. You enjoy improving models through data, evaluation, and iteration—not just prompt engineering. You take ownership of outcomes, from model quality and alignment to reliability, latency, and cost. Required Experience and Skills - Significant hands-on experience with Supervised Fine-Tuning (SFT) of LLMs in production, beyond prompt-only approaches - Direct experience using OpenAI APIs and/or AWS Bedrock for SFT, post-training, and deployment - Strong understanding of LLM post-training workflows, including data preparation, instruction tuning, evaluation methodologies, and common failure modes - Experience building and operating agentic LLM systems (tool use, multi-step reasoning, workflow orchestration) - Proficiency in Python and modern ML frameworks (e.g., PyTorch) - Experience operating ML systems in distributed, production environments - Strong intuition for trade-offs between model quality, latency, cost, safety, and scalability BENEFITS & PERKS - Unlimited PTO - Excellent medical, dental, and vision coverage - Employee Equity - Employee Discounts, Virtual Wellness Classes, and Pet Insurance And more!! SALARY RANGE The salary range for this role is $200,000 - $215,000, depending on location and experience. PEOPLE & CULTURE AT ZETA Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual’s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression. We’re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here: https://zetaglobal.com/blog/a-look-into-zetas-ergs/ ZETA IN THE NEWS! https://zetaglobal.com/press/?cat=press-releases #LI-YW1

Southern New Hampshire University is a team of innovators. World changers. Individuals who believe in progress with purpose. Since 1932, our people-centered strategy has defined us — and helped us grow a team that now serves over 180,000 learners worldwide. Our mission to transform lives is made possible by talented people who bring diverse industry experience, backgrounds and skills to the university. And today, we're ready to expand our reach. All we need is you. Make an impact — from near or far At SNHU, you'll have the option to work remotely in the following states: Alabama, Arizona, Arkansas, Delaware, Florida, Georgia, Hawaii, Idaho, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Mississippi, Missouri, Nebraska, New Hampshire, New Mexico, North Carolina, North Dakota, Ohio, Oklahoma, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, West Virginia, Wisconsin and Wyoming. We ask that our remote employees have access to a reliable internet connection and a dedicated, properly equipped workspace that is free of distractions. Employees must reside in, and work from, one of the above approved states. The opportunity Southern New Hampshire University (SNHU) is looking for an experienced Senior Python Backend Engineer. You will report directly to the Vice President of AI Engineering and you will engineer the backend systems that power our agentic AI learning platform, empowering human-centered learning for the age of AI. As a backend and platform engineer with data engineering skills, you'll build the infrastructure that powers our AI learning platform. You'll also ensure our Data Science team has the systems they need to measure the effectiveness and guide personalization. You will work remotely from any of our approved States #LI-Remote What you'll do: - Agentic backend and data platform** - You will design and implement scalable APIs for agent orchestration and learner interaction - You will build data pipelines that feed agent evaluation and continuous improvement cycles - You will create event streaming infrastructure for real-time learner interaction analysis - You will ensure data quality and accessibility for training and evaluating learning agents - You will improve data infrastructure for cost and performance in close coordination with Data and ML teams - This is a remote position Measurement and evaluation - Design and implement rigorous evaluation frameworks to measure agent performance and improve cycles - Develop LLM evaluation processes and perform error analysis to identify systemic improvements for agentic learning systems - Support instrumentation that makes evaluation relevant (metrics, traces/logs, and analysis workflows) On-call and incident response - Participate in on-call rotation and incident response for learning platform reliability - Contribute to runbooks, postmortems, and reliability improvements as we evolve our operating model What we're looking for: - 5+ years production Python experience at scale - AWS expertise (Lambda, DynamoDB, S3, Glue, and Athena) - Experience with a relational database such as MySQL, PostgreSQL, or Oracle - Experience with agentic AI architectures and evaluation frameworks - Data pipeline development (streaming and batch) - Infrastructure as Code experience (CDK, Terraform) - Production SaaS experience, including participation in on-call / incident response - Experience in GitHub Actions or similar CI/CD platforms - Experience communicating updates and resolutions to customers and other partners) AI-Augmented Development: - Active user of AI development tools (GitHub Copilot, Cursor, Claude Code, Codex) with personal projects and evolution over the past 12 months - Demonstrated examples of AI-augmented productivity gains - Enthusiasm for pushing boundaries of AI-assisted engineering We believe real innovation comes from inclusion - where different experiences, perspectives and talents are celebrated. So if you're wondering whether SNHU is right for you, take the leap and apply. You might be just the person we're looking for. Compensation The annual pay range for this position is $113,908.00 - $182,287.00. Actual offer will be based on skills, qualifications, experience and internal equity, in addition to relevant business considerations. We expect this position to be hired in the following target hiring range $125,868.00 - $170,293.00. Exceptional benefits (because you’re exceptional) You’re the whole package. Your benefits should be, too. As a full-time employee at SNHU, you’ll get: - High-quality, low-deductible medical insurance - Low to no-cost dental and vision plans - 5 weeks of paid time off (plus almost a dozen paid holidays) - Employer-funded retirement - Free tuition program - Parental leave - Mental health and wellbeing resources

Head of Asset Management & AI Transformation Remote | Global Role | Multi-Industry This is not a traditional asset management role This role is designed for someone who understands that the future of maintenance and asset management is driven by data, reliability engineering, and intelligent systems. If your experience is rooted in Reliability-Centred Maintenance (RCM), preventive and predictive strategies, labour optimisation, and cost control, and you are interested in applying this knowledge to AI-driven solutions, this is a unique opportunity to operate at the forefront of the industry. About MaxAccelerate Technology Group MaxAccelerate Technology Group is a global technology and transformation business focused on helping asset-intensive organisations improve performance, reduce cost, and scale operations through data, systems, and AI. Through our ecosystem, including DataMax AI, we work with organisations across Facilities Management, Transportation, Pharma, Manufacturing, Energy, and Infrastructure to transform how assets are managed. Our approach is grounded in real operational understanding: - Maintenance strategies must be structured, measurable, and optimised - Systems such as IBM Maximo and other EAM platforms must deliver actionable insight - AI must be applied in a way that drives tangible outcomes across cost, labour, and asset performance The Role We are seeking a Head of Asset Management & AI Transformation to lead the definition and delivery of AI-driven asset management solutions. The focus of the role is to take deep operational knowledge — including: - RCM frameworks - Preventive and condition-based maintenance strategies - Workforce planning and labour models - Spare parts, inventory, and asset lifecycle considerations and translate these into structured AI use cases and scalable solutions. Key Responsibilities - Work closely with Heads of Maintenance, Engineering Leaders, and Operations teams to understand: - Maintenance strategies and maturity - Preventive vs reactive ratios - Labour utilisation and workforce efficiency - Spare parts management and inventory control - - Define and structure AI use cases that address: - Reduction in reactive maintenance - Optimisation of preventive maintenance schedules - Labour efficiency and workforce planning - Spare parts forecasting and cost reduction - Asset lifecycle optimisation - - Develop and lead Proof of Concepts (PoCs) that validate: - Operational improvements - Cost savings - Performance uplift - - Translate business requirements into clear scope of works for DataMax AI R&D teams - Ensure AI solutions integrate effectively with EAM/CMMS platforms (Maximo preferred) and align with real operational workflows - Build clear ROI models, ensuring every solution delivers measurable impact across: - Maintenance costs - Asset uptime - Labour efficiency - Inventory and working capital - - Support clients in adopting solutions that improve their infrastructure, operations, and long-term asset strategy Why This Role Matters Most organisations have: - Maintenance strategies defined on paper - Systems implemented but underutilised - Data available but not fully leveraged This role exists to bridge that gap. You will ensure that: - Maintenance frameworks such as RCM are not just theoretical, but digitised and optimised - Preventive strategies are continuously refined based on real performance data - Labour and cost structures are aligned with actual operational demand - AI is applied in a way that improves real infrastructure outcomes, not just reporting What We Are Looking For - Strong background in Asset Management, Maintenance, or Engineering leadership - Deep understanding of: - RCM and reliability frameworks - Preventive and predictive maintenance strategies - Workforce and labour optimisation - Spare parts and inventory management - - Experience working with EAM/CMMS platforms, ideally IBM Maximo - Ability to translate operational challenges into structured, solution-driven approaches - Commercial awareness and the ability to link technical improvements to cost and performance outcomes The Opportunity This role offers the opportunity to work across industries and influence how organisations manage and optimise critical assets. You will play a key role in: - Defining how AI is applied to asset management in real environments - Delivering measurable improvements in cost, performance, and efficiency - Shaping solutions that can be scaled across multiple industries Apply If you are interested in moving beyond traditional asset management roles and want to be involved in shaping the next generation of maintenance and asset optimisation, we would welcome a conversation.

• Design, build, and deploy scalable GenAI applications leveraging LLMs, RAG architectures, and foundation models • Develop and optimize advanced retrieval and context engineering strategies using internal knowledge bases and structured/unstructured data • Design and orchestrate multi-step AI agents capable of reasoning, retrieval, and action execution across business workflows • Contribute to enterprise GenAI architecture, ensuring modularity, reusability, scalability, and observability • Build reusable GenAI components and services (agent frameworks, chat interfaces, orchestration layers, evaluation pipelines) • Integrate AI systems with enterprise platforms (CRM, document management, APIs) using secure authentication and governance standards • Embed Responsible AI principles including privacy, compliance, auditability, explainability, and risk management • Continuously evaluate emerging models and tools, benchmark performance, and optimize cost, latency, and system efficiency