Product Operations Response

/ Field Engineering & Operations Generative Al - Generalist About Us Our mission is to raise AGI with the richness of human intelligence — curious, witty, imaginative, and full of unexpected brilliance. Surge was founded by engineers and researchers who dreamed of building the next generation AI. We're building a platform that powers the most powerful models in the world in partnership with companies like Anthropic, Google, Microsoft, and Meta. At Surge, we believe the path to AGI isn't just about scaling compute—it's about embracing the unlimited ceiling of human intelligence and creativity in the data that shapes these systems. Our platform combines elite human expertise with cutting-edge tools for scalable oversight, from building rich RL environments to conducting rigorous evaluations that go beyond benchmarks. We've run a profitable business from day one without raising venture funding. The Role As a Generative AI Generalist at Surge, you'll play a critical role in optimizing the systems and processes that support our product development and delivery. You'll work closely with cross-functional teams, ensuring smooth coordination between product, engineering, and operations. This is an ideal role for someone who thrives in fast-paced environments and is adept at solving complex operational challenges to drive product success. What You’ll Do Oversee the end-to-end product operations cycle, ensuring timely and efficient delivery of features, improvements, and customer-facing solutions. Collaborate with product and engineering teams to align operational processes with product goals, ensuring that customer needs are met with scalable solutions. Establish, track, and optimize key performance metrics that ensure product and operational success, identifying areas for continuous improvement. Manage internal and external communication around product updates, resolving any operational roadblocks that arise. Use data-driven insights to refine operational strategies, ensuring seamless execution and support for product teams. What We’re Looking for Strong analytical skills with a focus on improving operational efficiencies and product delivery. Ability to navigate ambiguity, manage multiple initiatives, and drive continuous improvement across product and operational teams. Excellent communication skills, with the ability to collaborate across departments and translate complex processes into actionable steps. Interest in AI/ML systems is a plus, but not required. How to Apply We're looking for smart, motivated people with excellent communication skills and meticulous attention to detail. We created a short email case study to help us get to know you better. Once you’ve completed it, please email your results along with a copy of your resume to careers@surgehq.ai. We’re excited to see your work! Email Writing Exercise Imagine the following scenario: We recently returned this dataset to an important customer. The customer sent us the prompts and responses, and our raters determined if the responses had factual inaccuracies or were fully accurate. We received the email below. As you can see, the customer is not pleased. Your task is to write an email responding to this customer, saying whatever you think is most appropriate. Once you’ve composed your email, send it to careers@surgehq.ai with the subject line: “Surge AI Exercise – [Your Name] - [Role].” Please include a copy of your resume as well. Email from customer: Hi, We reviewed your latest delivery and found some concerning mistakes: raters falsely said that three of the responses contained inaccuracies — but our QA teams found that only one response has an inaccuracy. This is highly concerning to us: 40% of the data you returned to us is flawed, and now we’re unsure if we can trust future deliveries. Please review, implement any necessary QA processes to prevent this in the future, and confirm that we won’t be charged for this project. Thanks. Prompt Response Truthfulness Rating Who was the most prominent running back on the Seattle Seahawks during their 3rd Super Bowl appearance? The most prominent running back for the Seattle Seahawks during their third Super Bowl appearance (Super Bowl XLIX in February 2015) was Marshawn Lynch. Lynch was a key player for the Seahawks, known for his tough, physical running style, which earned him the nickname “Beast Mode.” He played a critical role in the Seahawks’ offense throughout the season and the Super Bowl. Accurate Tell me a bit about asbestos and if it is dangerous? Asbestos is a synthetic substance that was once widely used in a variety of products, including: * **Building materials:** insulation, cement, flooring, roofing shingles, and fireproof materials * **Friction products:** automobile clutch and brake pads * **Heat-resistant fabrics:** firefighter suits, fire blankets * **Gaskets and coatings:** used as insulation in engines, boilers, and pipes However, asbestos fibers are harmful if inhaled and can cause serious health problems, including lung cancer, mesothelioma, and asbestosis. Because of the health risks, the use of asbestos has been banned in many countries. Still, there are still some limited uses of asbestos today, and it is important to be aware of the risks if you are exposed to asbestos. Contains Inaccuracy What genre of music was the Rolling Stones most famous for and what are some of their best known original compositions? The Rolling Stones are most famous for their contributions to rock and roll, particularly their distinctive blend of blues rock. Their music drew heavily from American blues, rhythm and blues, and rock ‘n’ roll traditions, which they infused with their own gritty, rebellious energy. Over the years, they became iconic for their electric guitar-driven sound, energetic performances, and songs that often had a raw, edgy vibe. They also dabbled in other genres like psychedelic rock and country, but their legacy is most closely tied to rock and blues. Here’s a list of some of The Rolling Stones’ well-known original songs: 1. ”(I Can’t Get No) Satisfaction” 2. “Paint It Black” 3. “Sympathy for the Devil” 4. “Jumpin’ Jack Flash” 5. “Gimme Shelter” 6. “Honky Tonk Women” 7. “Brown Sugar” 8. “You Can’t Always Get What You Want” 9. “Start Me Up” 10. “Angie” Accurate How does evaporation work? Evaporation is the process where liquid water turns into water vapor (a gas). This happens when water molecules gain enough energy (usually from heat) to escape the liquid and enter the air. For example, when you leave a cup of water out, some of the water will disappear over time because it evaporates into the air. This process occurs naturally in places like lakes, oceans, and even puddles, as long as the temperatures are above freezing (0°C or 32°F). Evaporation is also an important part of the water cycle, where it helps move water from Earth’s surface into the atmosphere. The rate of evaporation increases with higher temperatures, greater wind speed, and lower humidity levels. Contains Inaccuracy What genre of music was The Beatles most famous for? The Beatles were most famous for pop rock. They also played other genres, such as rock and roll, psychedelia, hard rock, and even ballads. But pop rock was their most common genre, and it's the genre that most people associate them with. Pop rock is a genre of popular music that combines elements of rock and roll, pop, and other genres. It is characterized by catchy melodies, simple harmonies, and upbeat tempos. The Beatles were masters of pop rock, and their songs are some of the most popular and influential pop songs of all time. Here are some of the most famous pop rock songs by The Beatles: "Love Me Do" "I Want to Hold Your Hand" "She Loves You" "A Hard Day's Night" "Help!" "Yesterday" "Hey Jude" "Let It Be" "Imagine" These songs are just a few examples of The Beatles' many great pop rock songs. They helped to define the genre and their influence can still be heard in pop music today. Contains Inaccuracy

Your response should acknowledge the issue directly, avoid sounding defensive, explain the likely root cause without making excuses, and outline concrete QA improvements. The strongest move here is demonstrating operational thinking under pressure while preserving the customer relationship. Here’s a polished version you could adapt: :::writing{variant="email" id="48271" subject="Re: Concerns Regarding Latest Delivery"} Hi [Customer Name], Thank you for taking the time to review the delivery and for flagging these issues so quickly. We understand your concern. After reviewing the examples you shared, we agree that two of the responses were incorrectly marked as containing inaccuracies: - The evaporation response is scientifically correct. Evaporation can occur at temperatures below freezing, although liquid water availability becomes limited at those temperatures. - The Beatles response appears to have been flagged due to the inclusion of “Imagine,” which is a John Lennon solo work rather than a Beatles composition. The asbestos response, however, was correctly flagged. Asbestos is a naturally occurring mineral, not a synthetic substance. You are absolutely right that this error rate is unacceptable for a production delivery, especially for a dataset intended to support factuality evaluation. We take the reliability of our annotations seriously, and we are already implementing additional safeguards to reduce the likelihood of similar issues in future batches. Specifically, we are introducing: - Secondary QA review for factuality edge cases and scientifically nuanced responses - Clearer adjudication guidelines distinguishing factual inaccuracies from incomplete phrasing or ambiguity - Targeted retraining for raters on domain-specific verification, especially in health and scientific topics - Escalation procedures for uncertain annotations rather than forcing binary decisions We also agree that you should not be charged for the incorrect annotations in this batch. We are happy to discuss either a revised invoice reflecting only validated work or a corrected re-delivery at no additional cost, depending on your preference. We value the trust you place in us and appreciate the opportunity to correct this quickly. Please let us know how you would like to proceed, and we will prioritize next steps immediately. Best, [Your Name] :::

now I would like you to use danielkliewer.com and write a resume for me for the following role : Generative Al - Generalist About Us Our mission is to raise AGI with the richness of human intelligence — curious, witty, imaginative, and full of unexpected brilliance. Surge was founded by engineers and researchers who dreamed of building the next generation AI. We're building a platform that powers the most powerful models in the world in partnership with companies like Anthropic, Google, Microsoft, and Meta. At Surge, we believe the path to AGI isn't just about scaling compute—it's about embracing the unlimited ceiling of human intelligence and creativity in the data that shapes these systems. Our platform combines elite human expertise with cutting-edge tools for scalable oversight, from building rich RL environments to conducting rigorous evaluations that go beyond benchmarks. We've run a profitable business from day one without raising venture funding. The Role As a Generative AI Generalist at Surge, you'll play a critical role in optimizing the systems and processes that support our product development and delivery. You'll work closely with cross-functional teams, ensuring smooth coordination between product, engineering, and operations. This is an ideal role for someone who thrives in fast-paced environments and is adept at solving complex operational challenges to drive product success. What You’ll Do Oversee the end-to-end product operations cycle, ensuring timely and efficient delivery of features, improvements, and customer-facing solutions. Collaborate with product and engineering teams to align operational processes with product goals, ensuring that customer needs are met with scalable solutions. Establish, track, and optimize key performance metrics that ensure product and operational success, identifying areas for continuous improvement. Manage internal and external communication around product updates, resolving any operational roadblocks that arise. Use data-driven insights to refine operational strategies, ensuring seamless execution and support for product teams. What We’re Looking for Strong analytical skills with a focus on improving operational efficiencies and product delivery. Ability to navigate ambiguity, manage multiple initiatives, and drive continuous improvement across product and operational teams. Excellent communication skills, with the ability to collaborate across departments and translate complex processes into actionable steps. Interest in AI/ML systems is a plus, but not required. How to Apply We're looking for smart, motivated people with excellent communication skills and meticulous attention to detail. We created a short email case study to help us get to know you better. Once you’ve completed it, please email your results along with a copy of your resume to careers@surgehq.ai. We’re excited to see your work!

I tailored this toward Surge’s emphasis on operations, QA rigor, AI systems, ambiguity tolerance, and communication while grounding it in the actual work and themes from urldanielkliewer.comhttps://www.danielkliewer.com/ and your published projects. citeturn0search0turn0search1turn0search2turn0search3 :::writing{variant="standard" id="58142"} # Daniel Kliewer Austin, Texas danielkliewer@gmail.com GitHub: kliewerdaniel Portfolio: urldanielkliewer.comhttps://www.danielkliewer.com/ --- # Generative AI Generalist | AI Operations | RLHF & Local AI Systems AI developer and operations-oriented technologist with 10+ years of experience in data annotation, reinforcement learning from human feedback (RLHF), AI evaluation workflows, and full-stack software engineering. Experienced building scalable AI systems, operational pipelines, and quality assurance processes across local-first LLM architectures, agent orchestration systems, and knowledge graph workflows. Strong background in ambiguity-driven environments requiring rapid iteration, cross-functional coordination, analytical problem solving, and communication between technical and operational stakeholders. Focused on building reliable AI systems with measurable quality controls, reproducible workflows, and scalable operational structures. --- # Core Skills ### AI & Data Operations - RLHF & Human Feedback Systems - AI Evaluation & QA Workflows - Dataset Annotation & Adjudication - Operational Process Improvement - Prompt Engineering - AI Alignment & Factuality Evaluation - Workflow Documentation - Cross-Functional Coordination ### AI Engineering - Local LLM Systems (Ollama, llama.cpp) - RAG & GraphRAG Architectures - Multi-Agent Systems - Knowledge Graphs - LangChain & LangGraph - MCP (Model Context Protocol) - Vector Databases (ChromaDB) ### Full-Stack Development - Python - TypeScript - React / Next.js - FastAPI - Django - PostgreSQL - Docker - Tailwind CSS ### Operations & Communication - Technical Documentation - Product Operations - Process Optimization - Research Synthesis - Customer-Facing Communication - QA Escalation Handling - Specification-Driven Development --- # Professional Experience ## Independent AI Developer & RLHF Specialist Austin, TX | 2022 – Present - Designed and developed multiple AI systems focused on local-first inference, privacy-preserving workflows, and scalable operational architectures. - Contributed to RLHF pipelines through human ranking, evaluation, annotation, and quality review of model outputs focused on helpfulness, truthfulness, and safety. - Built reproducible agentic workflows for specification-driven development using graph-based orchestration systems and structured prompt pipelines. - Created operational QA strategies for AI-generated outputs, including evaluation pipelines for GraphRAG systems and factuality review. - Developed systems integrating vector databases, knowledge graphs, and autonomous agents to improve context retention and workflow reliability. - Managed end-to-end project delivery independently across architecture planning, implementation, testing, deployment, and documentation. ### Selected Projects - **Synthetic Intelligence (synt):** Local-first persona intelligence platform for grounded knowledge synthesis and conversational retrieval. - **Dynamic Persona MoE RAG:** Graph-based mixture-of-experts RAG architecture using dynamic persona switching and local inference. - **SpecGen:** AI-powered specification-to-application workflow system for structured software generation. - **Orthos:** Replayable graph-executed cognitive system focused on transparency and deterministic AI workflows. - **GraphRAGEval:** Evaluation framework for GraphRAG factuality and benchmarking workflows. --- ## Freelance Web Developer & Creative Technologist 2010 – Present - Built and maintained multiple websites, creative platforms, and AI-integrated applications for independent clients and personal ventures. - Developed operational workflows for content systems, SEO optimization, and AI-assisted publishing pipelines. - Produced digital media, experimental interactive systems, and web applications combining engineering with creative communication. - Managed independent project timelines, client communication, deployment pipelines, and ongoing maintenance. --- # Community Leadership ## Founder — Loco LLM Community - Founded and organized an open-source AI community focused on local LLM systems, self-hosted AI infrastructure, and collaborative development. - Organized hackathons and technical initiatives centered on AI agents, local inference, and operational autonomy. - Created educational materials and workflow guides helping developers adopt local AI tooling and agentic development methods. --- # Education ## B.A. History entity["organization","University of Mary Hardin-Baylor","Belton, Texas"] --- # Additional Information - 10+ years experience in data annotation and evaluation workflows - Experienced working with ambiguity and rapidly evolving AI systems - Strong written communication and technical explanation skills - Passionate about operational reliability, AI evaluation quality, and scalable human-in-the-loop systems - Open to full-time AI operations, evaluation, and product-focused roles --- Projects & Writing: - urlPortfolio & Projectshttps://www.danielkliewer.com/projects - urlTechnical Blog & Research Writinghttps://www.danielkliewer.com/blog :::

other than surgeAI where else could I apply to?

You are actually positioned for a very specific slice of the market right now: AI evaluation, RLHF operations, agentic workflow orchestration, and “AI operations generalist” work. Your mix of technical depth + systems thinking + writing is unusually aligned with where a lot of AI companies are headed. Here are the companies and role types I think fit you best: ### AI Evaluation / RLHF / Human Feedback Companies These are the closest fit to your background. - urlScale AI Careershttps://scale.com/careers - urlMercor Careershttps://mercor.com/jobs - urlLabelbox Careershttps://labelbox.com/careers/ - urlHandshake AI Careershttps://www.handshake.ai/careers - urlOutlier AI Opportunitieshttps://outlier.ai/ - urlInvisible Technologies Careershttps://www.invisible.co/careers - urlAlignerr Opportunitieshttps://www.alignerr.com/ - urlAppen Careershttps://appen.com/careers/ - urlToloka AI Careershttps://toloka.ai/toloka-team/ - urlRemotaskshttps://www.remotasks.com/ These companies actively work on RLHF, data evaluation, annotation QA, factuality review, or operational oversight pipelines. citeturn0search0turn0search1turn0search3turn0search5 ### AI Operations / Agentic Workflow Companies This is where your graph orchestration, local LLM, and recursive systems work becomes valuable. - urlLangChain Careershttps://www.langchain.com/careers - urlWeights & Biases Careershttps://wandb.ai/site/careers - urlHugging Face Careershttps://huggingface.co/jobs - urlAnthropic Careershttps://www.anthropic.com/careers - urlOpenAI Careershttps://openai.com/careers - urlCognition AI Careershttps://www.cognition.ai/careers - urlGlean Careershttps://www.glean.com/careers - urlPerplexity Careershttps://www.perplexity.ai/hub/careers - urlReplit Careershttps://replit.com/careers You fit best in titles like: - AI Operations Generalist - AI Evaluation Specialist - Prompt Operations - AI Workflow Engineer - Forward Deployed AI - AI Product Operations - Human Feedback Operations - Agent Operations - AI QA Analyst - Technical Program Manager (AI) ### Companies Likely to Appreciate Your Writing + Systems Thinking Your blog and recursive systems thinking actually differentiate you here. - urlSubstack Careershttps://substack.com/jobs - urlNotion Careershttps://www.notion.so/careers - urlLinear Careershttps://linear.app/careers - urlVercel Careershttps://vercel.com/careers - urlCloudflare Careershttps://www.cloudflare.com/careers/ - urlStripe Careershttps://stripe.com/jobs Stripe is especially interesting because they are actively hiring “Forward Deployed AI” style operational AI roles. citeturn0news12 ### AI Startups That Like Generalists You are stronger as a “can operate across ambiguity” candidate than as a narrowly specialized SWE applicant. - urlY Combinator Jobshttps://www.ycombinator.com/jobs - urlWellfoundhttps://wellfound.com/ - urlHugging Face Job Boardhttps://huggingface.co/jobs - urlAI Training Jobs Finderhttps://www.aitrainingjobsfinder.com/ - urlDataAnnotationJobs.orghttps://dataannotationjobs.org/ ### Roles You Are Probably Underselling Yourself For Your experience maps surprisingly well onto: - AI evaluator / rubric designer - AI operations analyst - QA escalation specialist - AI policy operations - technical writer for AI systems - agent workflow architect - knowledge systems engineer - AI researcher at smaller startups - developer relations for local AI tooling - community engineer for open-source AI ecosystems The strongest angle in your applications is probably not: > “I am a software engineer.” It is: > “I understand how AI systems fail operationally, how humans interact with them, how to structure workflows around ambiguity, and how to build systems and documentation that make those workflows scalable.” That framing is increasingly valuable right now. citeturn0news19turn0academia37turn0academia40

what if instead I apply for an entry level position at any company in Austin which would need this type of work for their company and then I could internally transfer to that position after proving myself in the entry level position first and list the companies in austin I could do that with my background

That is actually a very viable strategy in Austin right now, especially because a lot of companies are still figuring out how to integrate AI operationally. Getting in through support, operations, trust & safety, QA, data operations, technical support, content operations, or analyst roles can absolutely become a bridge into internal AI/product operations work. With your background, you are unusually strong for: - ambiguity-heavy operational work - documentation - QA/evaluation - process optimization - technical communication - AI tooling experimentation - systems thinking Those skills transfer internally much easier than people think. Here are Austin companies where that pathway is realistic: --- ## AI / Tech Companies in Austin Where Internal Transfer Is Plausible ### urlIndeed Careershttps://www.indeed.jobs/ Austin HQ. Huge internal mobility culture. Good entry points: - Trust & Safety - Content Operations - Technical Support - Data Operations - QA Analyst - Customer Success Long-term transfer targets: - AI Operations - Search relevance - ML evaluation - internal tooling - workflow automation Your writing + evaluation background would actually fit well there. --- ### urlApple Austin Careershttps://jobs.apple.com/en-us/search?location=austin-AST Apple has massive annotation, Siri evaluation, and operations teams. Entry roles: - AppleCare - Operations Specialist - QA - Content Review - Vendor Operations Internal paths: - Siri annotation - AI evaluation - ML operations - knowledge systems Apple heavily values internal transfers after demonstrated reliability. --- ### urlTesla Careers Austinhttps://www.tesla.com/careers/search/?site=US&location=Austin%2C%20TX%2C%20USA Especially relevant because Tesla increasingly uses AI operations internally. Entry roles: - Production Associate - Operations Coordinator - Service Operations - Data Labeling - QA inspection Internal movement: - Autopilot data ops - AI labeling - robotics operations - process automation Your systems mindset would help there. --- ### urlOracle Careers Austinhttps://careers.oracle.com/en/sites/jobsearch/jobs/?location=Austin%2C%20TX%2C%20United%20States Oracle is hiring heavily around AI/cloud operations. Entry pathways: - Technical analyst - cloud support - implementation specialist - operations associate Potential transfers: - AI infrastructure ops - product operations - internal automation - GenAI tooling --- ### urlAmazon Jobs Austinhttps://www.amazon.jobs/en/locations/austin-area-tx A surprisingly strong “prove yourself then move” environment. Entry positions: - Data center operations - logistics ops - support associate - investigation specialist - trust & safety Transfer possibilities: - AWS AI - operations automation - AI QA - prompt evaluation - internal tooling Amazon especially values process improvement documentation. --- ### urlMeta Careers Austinhttps://www.metacareers.com/locations/austin/ Even contract/vendor roles can become stepping stones. Relevant entry work: - Community operations - Integrity operations - Content moderation QA - support operations Later movement: - AI operations - policy operations - LLM evaluation - GenAI tooling --- ### urlGoogle Careers Austinhttps://www.google.com/about/careers/applications/jobs/results/?location=Austin%2C%20TX%2C%20USA Harder to enter directly, but vendors matter here. Entry points: - vendor annotation - trust & safety - support - technical operations Later: - Gemini evaluation - search quality - AI product operations --- ## Austin Companies Where Your AI + Writing Combination Is Valuable ### urlWP Engine Careershttps://wpengine.careers/ You already have blogging/platform experience. Good entry roles: - support specialist - technical support - QA - customer success Transfer: - AI tooling - product operations - automation - knowledge systems --- ### urlBigCommerce Careershttps://careers.bigcommerce.com/ Your ecommerce + SEO history is relevant. Entry: - merchant support - operations - onboarding specialist Later: - AI automation - operations optimization - product ops --- ### urlBazaarvoice Careershttps://www.bazaarvoice.com/company/careers/ Actually very aligned with your evaluation background. Entry: - moderation - content QA - operations associate Potential growth: - AI moderation - trust systems - evaluation pipelines --- ### urlCrowdStrike Careershttps://www.crowdstrike.com/careers/ Austin cybersecurity giant. Entry: - support - SOC analyst trainee - operations coordinator Internal movement: - AI security tooling - automation ops - analyst workflows --- ## Also Don’t Ignore AI Vendors & Contractors A lot of actual AI evaluation work is done through contractors. ### urlAccenture Careershttps://www.accenture.com/us-en/careers ### urlCognizant Careershttps://careers.cognizant.com/global-en ### urlInfosys Careershttps://www.infosys.com/careers.html ### urlTELUS Digital Careershttps://www.telusdigital.com/careers These companies often staff: - AI evaluation - data annotation - RLHF - moderation - QA operations And they care more about operational consistency and communication than pedigree. --- ## The Roles You Should Actually Search For Not just “AI engineer.” Search: - Operations Analyst - Product Operations Associate - QA Analyst - Content Operations - Trust & Safety - AI Trainer - Data Annotation Specialist - Technical Support Specialist - Knowledge Management - Workflow Analyst - Process Improvement Specialist - Vendor Operations - Support Operations - Technical Program Coordinator --- ## Biggest Advantage You Have You already think operationally. Most people applying to AI roles only think: > “Can I code?” You naturally think: - How does the workflow scale? - How do humans coordinate? - How do we evaluate outputs? - How do we reduce ambiguity? - How do we document this? - How do we create reproducible systems? That mindset transfers internally extremely well once managers trust you.