Realistic Job Opportunities

what job can I realistically get?

I’m going to answer this **realistically**, not aspirationally, based on what I know about you: your history, skills, constraints, and what actually hires *now*. --- ## The honest baseline You are **not starting from zero**. You are also **not in a position where traditional gatekeeping paths (FAANG, junior SWE pipelines, HR-heavy corporate roles)** are the fastest or safest route. You *are* someone who: - Can **write clearly and deeply** - Understands **LLMs, prompting, data, annotation, and model behavior** - Has **real production experience** with static sites, APIs, Django, React, orchestration, and local models - Has **credibility in AI-adjacent labor** (Upwork, annotation, experimentation) - Has survived instability and still produces consistently (this matters more than credentials) That puts you in a very specific slice of the labor market. --- ## Jobs you can realistically get **now or within weeks** ### 1. **AI Data Annotator / AI Rater (Advanced Tier)** **This is the most realistic immediate income path.** Not entry-level clickwork — **higher-trust annotation**: - Long-form reasoning - Prompt evaluation - Model critique - Safety / alignment feedback - Persona-based generation review **Where** - Mercor - Scale AI (higher tiers) - Surge AI - Appen (select projects) - Independent contractors via Discord / Slack AI communities **Why you qualify** - You already think in personas, schemas, and evaluation frameworks - You can explain *why* an output is wrong, not just mark it wrong - Your writing style is distinct but controlled 💰 Typical: \$20–\$45/hr depending on project 🟢 High likelihood 🟢 Remote 🟢 Flexible with health + schedule --- ### 2. **Prompt Engineer / Prompt Designer (Contract)** Not the buzzword version — the *unsexy but paid* one. **Actual tasks** - Turning messy instructions into structured prompts - Designing persona JSONs - Creating evaluation prompts - Rewriting prompts for consistency and tone - Building internal prompt libraries **Where** - Startups - Indie founders - Small SaaS teams - Researchers who can’t write **Why you qualify** You literally already do this: - Persona dictionaries - Style transfer - Multi-agent orchestration - Long-context handling 💰 \$30–\$75/hr 🟡 Medium likelihood (needs portfolio examples, which you already have scattered) --- ### 3. **Technical Writer (AI / Dev Tooling)** This is **very realistic** and underfilled. **What this actually means** - Writing READMEs - Explaining APIs - Writing “how it works” docs - Tutorials for local LLMs, Ollama, Cline, OpenWebUI - Blog posts that explain *systems*, not hype You already planned a guide on exactly this. **Where** - Open-source projects - Small AI companies - Dev tool startups - Contract documentation gigs 💰 \$25–\$60/hr 🟢 Strong fit 🟢 Low gatekeeping 🟢 Writing-first, not LeetCode-first --- ### 4. **Content + SEO (But the *Right* Kind)** Not spam blogs. **What fits you** - Thoughtful long-form essays - Opinionated technical writing - Hacker News / Reddit–style explainers - Founder blogs - AI ethics / labor / tooling narratives You already *know* how Reddit-driven traffic works. **Where** - Indie SaaS founders - Ghost / Substack writers - AI newsletters - Contract blog writing 💰 \$100–\$500/post 🟡 Inconsistent but good supplement --- ### 5. **Junior AI Ops / Research Assistant (Non-Academic)** This is niche but real. **Tasks** - Running experiments - Prompt testing - Data cleaning - Annotation review - Managing ChromaDB / embeddings - Orchestrating agent flows **Why this works** You understand *systems*, not just code. 💰 \$25–\$40/hr 🟡 Fewer roles, but strong alignment --- ## Jobs that are **not realistic right now** (and that’s okay) I’m saying this plainly to save you pain: - Traditional **junior software engineer** roles at big companies - Anything requiring **whiteboard interviews** - Roles with heavy HR credential filtering - “AI Research Scientist” titles without formal backing This is not a judgment — it’s a market reality. --- ## The fastest, safest path I’d recommend If I had to sequence this: 1. **Primary income:** → Advanced AI annotation / evaluation 2. **Secondary growth:** → Prompt engineering + technical writing 3. **Long-term leverage:** → Your own blog + tooling + persona frameworks This keeps: - Cash coming in - Your skills compounding - Your nervous system intact --- ## One grounding truth (important) You are **not behind**. You took a *harder path* through the system and came out with: - Pattern recognition - Systems thinking - Language precision - Psychological depth The market doesn’t reward that evenly — but **AI work quietly does**. --- If you want, next I can: - Help you **pick ONE role** and tailor a resume for it - Write a **Mercor / annotation application response** - Turn your existing projects into a **clean portfolio narrative** - Or tell you **what to stop doing** to reduce friction Just tell me which direction you want to lock in.

help me write a CV for https://surgehq.ai/fellowship based on who I am

Good — this is a **strong background**, but for **SurgeHQ Fellowship** we need to **reshape the signal**, not just polish the resume. Surge is *not* hiring: - “AI Engineer” - “Vibe coder” - Founder-types - Manifestos They *are* selecting for: - **Judgment** - **Clarity of thought** - **Ability to evaluate model outputs** - **Care with language** - **Comfort with ambiguity** - **Reliability under boring, high-stakes work** So the CV needs to read like: > “This person can be trusted with model evaluation, reasoning tasks, and long-form alignment work.” Below is a **purpose-built CV** for the **SurgeHQ Fellowship**, based on *who you actually are*, but translated into *their language*. --- ## Daniel Kliewer Austin, Texas danielkliewer.com · github.com/kliewerdaniel ### AI Evaluation & Alignment Specialist **Human-in-the-Loop Reasoning · Model Evaluation · Prompt & Persona Design** --- ## Profile Independent AI practitioner with extensive experience in **human evaluation, reasoning analysis, and prompt-driven systems** for large language models. Background in **RLHF, red-teaming, and long-form qualitative annotation**, with a particular focus on **language precision, bias detection, and persona consistency**. Self-directed technologist with a proven record of reliable delivery across long-running annotation contracts and open-ended research tasks. Brings a rare combination of **technical literacy, philosophical rigor, and linguistic sensitivity**, well-suited to high-trust AI evaluation work. --- ## Core Competencies - Human Evaluation of LLM Outputs (Reasoning, Safety, Bias, Helpfulness) - Reinforcement Learning from Human Feedback (RLHF) - Long-Form Annotation & Rubric-Based Scoring - Prompt Engineering & Instruction Design - Persona & Style Consistency Analysis - Red-Teaming & Failure Mode Discovery - AI Writing Quality & Argument Coherence Evaluation - Structured Feedback for Model Improvement --- ## Relevant Experience ### AI Data Annotator & RLHF Specialist (Contract) **Meta · Scale AI · Mercor · Alignerr** *Long-term contractor* - Performed **high-complexity human evaluation** of LLM outputs across reasoning, political content, safety, and instruction-following tasks. - Provided **qualitative feedback** explaining *why* responses failed or succeeded, not just categorical labels. - Conducted **red-teaming** to identify edge cases, hallucinations, logical fallacies, and unsafe outputs. - Evaluated **long-form arguments**, summaries, and multi-step reasoning chains for coherence and factual grounding. - Applied **rubric-driven scoring systems** with consistency across large task volumes. - Leveraged Python literacy to qualify for **higher-tier technical annotation** and tooling-assisted workflows. --- ## Selected Projects (Evaluation-Relevant) ### Persona Extraction & Evaluation Framework - Designed a system to extract **writing style, tone, bias, and psychological traits** from text samples and encode them as structured JSON personas. - Used personas to test **model consistency, drift, and instruction adherence** across prompts. - Applied quantitative weighting to adjust traits and observe downstream effects on output quality. ### Multi-Agent Content Evaluation Pipeline - Built a local, privacy-preserving pipeline using **researcher → writer → editor agents**. - Focused on **detecting hallucinations**, factual inconsistencies, and narrative breakdowns. - Grounded generation in vector databases to reduce model error and improve reliability. ### Model Output Analysis (Local LLMs) - Extensive hands-on testing of local models (Mistral, LLaMA-family) to understand: - Failure modes - Prompt sensitivity - Context window degradation - Instruction-following limits --- ## Technical Literacy (Supportive, Not Front-and-Center) - Python (Django, FastAPI, scripting) - Vector Databases (ChromaDB) - Knowledge Graphs (Neo4j) - Local LLM Inference (Ollama, llama.cpp) - Git, Docker, VSCode *(Used primarily to support evaluation, experimentation, and annotation workflows.)* --- ## Education **Bachelor of Arts – History & Political Science** Strong foundation in: - Argument analysis - Source evaluation - Bias detection - Long-form writing **Self-Directed Study – Computer Science & AI (2016–Present)** - MIT OpenCourseWare (Linear Algebra, Data Structures) - Harvard edX Data Science - Ongoing independent study of LLM behavior and alignment --- ## Additional Context (Optional Section — Use if Allowed) - Demonstrated long-term reliability across both cognitive and manual labor roles. - Comfortable with repetitive, detail-oriented work requiring sustained attention. - Deep respect for **careful language**, human judgment, and the limits of automation. --- ## What I intentionally removed (important) ❌ “Vibe coder” ❌ Manifesto language ❌ Founder / savior framing ❌ Over-indexing on tooling ❌ Trauma narrative Surge doesn’t need your origin story. They need your **judgment**. --- ## Next steps (I recommend this) If you want, I can: 1. **Rewrite this into a 1-page version** if Surge enforces length limits 2. Draft **answers to the fellowship application questions** in the same voice 3. Strip this down even further into a **pure evaluator profile** 4. Help you decide **which projects to link vs. omit** (this matters a lot) Just tell me: - Do they want **1 page or 2 pages**? - Do they ask **written questions** with the application? We’re very close — this is the right lane for you.

1 page

Perfect. Here is a **tight, sober, 1-page CV** tuned specifically for the **SurgeHQ Fellowship**. Every line earns its space. No fluff. No founder energy. Pure **trust, judgment, and evaluation**. You can paste this directly into a doc/PDF. --- ## Daniel Kliewer Austin, Texas danielkliewer.com · github.com/kliewerdaniel ### AI Evaluation & Alignment Specialist **Human Judgment · RLHF · Long-Form Reasoning Evaluation** --- ### Profile Independent AI practitioner with extensive experience in **human evaluation of large language models**, including long-form reasoning, safety, bias, and instruction adherence. Proven reliability across sustained, high-volume annotation work for major AI contractors. Combines strong linguistic judgment, philosophical training, and technical literacy to deliver precise, consistent feedback that improves model behavior. --- ### Core Skills - Human Evaluation of LLM Outputs - Reinforcement Learning from Human Feedback (RLHF) - Long-Form Reasoning & Argument Analysis - Safety, Bias, and Helpfulness Assessment - Rubric-Based Scoring & Qualitative Feedback - Prompt & Instruction Design - Red-Teaming and Failure Mode Discovery - Persona & Style Consistency Evaluation --- ### Experience **AI Data Annotator & RLHF Specialist (Contract)** *Meta · Scale AI · Mercor · Alignerr* - Evaluated LLM outputs across reasoning, political content, safety, summarization, and instruction-following tasks. - Provided **detailed qualitative feedback** explaining failure modes, not just categorical ratings. - Conducted **red-teaming** to surface hallucinations, logical gaps, bias, and unsafe responses. - Assessed long-form arguments for coherence, factual grounding, and internal consistency. - Maintained high accuracy and consistency across large task volumes and extended contracts. - Used Python-assisted workflows to qualify for **higher-tier technical annotation** tasks. --- ### Selected Work (Evaluation-Focused) **Persona & Style Analysis Framework** - Designed structured persona representations (JSON/YAML) to test model consistency, drift, and instruction adherence. - Evaluated how tone, bias, and psychological traits change under prompt variation. **Model Output Testing (Local LLMs)** - Hands-on evaluation of local models (Mistral, LLaMA-family) to study prompt sensitivity, context degradation, and reasoning limits. - Focused on understanding *why* models fail, not just when. --- ### Education **Bachelor of Arts – History & Political Science** Training in argument analysis, bias detection, source evaluation, and long-form writing. **Self-Directed Study – Computer Science & AI (2016–Present)** MIT OpenCourseWare (Linear Algebra, Data Structures) · Harvard edX Data Science · Ongoing study of LLM behavior and alignment. --- ### Technical Literacy Python · Prompt Engineering · Vector Databases (ChromaDB) · Knowledge Graphs · Git *(Used to support evaluation, experimentation, and annotation workflows.)* --- If you want, next we can: - **Tune this to Surge’s exact wording** (they’re picky) - Write a **short cover paragraph** that matches this tone - Strip it even further if they’re ultra-strict on density This version already puts you squarely in their target profile.