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About This Role
Remote (International) Full-time
Careerflow Human Data Labs partners with AI companies to bring real-world professional expertise into their products.
Role Overview We are looking for experienced AI Evaluation Engineers (Engineering Simulation & Design) to author and validate "model-breaking," simulation-based engineering design problems to train and evaluate state-of-the-art AI agents. Operating across major engineering disciplines—including Electrical, Mechanical, Control Systems, Aerospace, Systems, and Robotics—you will create complex, multi-constraint tasks where AI agents must interpret requirements, navigate trade-offs, configure open-source simulation tools, diagnose failures, and iterate toward valid solutions. You will analyze agent execution logs, expose systemic reasoning gaps, and build automated, objective graders to elevate frontier model performance.
Job Requirements:
- Education & Expertise: Master’s degree or PhD in Electrical, Mechanical, Aerospace, with 10+ years of hands-on engineering design experience.
- Simulation Tooling: Proficiency with at least one domain-relevant open-source simulation package (e.g., ngspice, PySpice, OpenFOAM, FEniCSx, CalculiX, python-control, CadQuery, build123d, OpenModelica, Cantera, Gmsh) combined with strong Python scripting skills.
- AI Evaluation & Failure Diagnostics: Hands-on experience with modern LLMs/coding agents and evaluation concepts (pass@k, failure-mode analysis, nondeterministic behavior), with the ability to audit trajectory logs and isolate core reasoning/tool-use failures.
- Domain Rigor & Precision: Uncompromising attention to physical plausibility, unit consistency, boundary conditions, convergence criteria, and technical documentation.
- Availability & Commitment: Talent must have weekend on-call availability (part-time engagement is acceptable).
- Technical Infrastructure: Personal desktop/laptop equipped with a stable, high-speed internet connection in a remote setup.
Job Responsibilities:
- Model-Breaking Problem Design: Author original, self-contained engineering design tasks with competing constraints, explicit optimization targets, validated reference solutions, and objective autograders.
- Environment & Simulation Integration: Build, run, and validate problem environments using open-source simulation tools and custom Python test benches.
- Trajectory Analysis & Failure Mode Taxonomy: Evaluate coding agent outputs and execution logs across repeated trials to identify systemic failure modes (e.g., misinterpreting simulator feedback, premature design convergence, physically impossible geometries).
- Difficulty Calibration & Benchmark Refinement: Iteratively refine problem difficulty based on empirical model performance data without introducing ambiguity or missing information.
- Cross-Functional Collaboration: Partner with AI researchers, pod leads, and domain experts to integrate high-rigor benchmarks into the model evaluation pipeline.
- Location: Bangladesh, India, Indonesia, Egypt, Ghana, Kenya, Nigeria, Turkey, Brazil, Colombia.
Domains:
- Electrical Engineering
- Mechanical Engineering
- Aerospace Engineering
Education & Experience
- Bachelor's degree or equivalent practical experience in any field.
- Experience in AI evaluation, data annotation, content review, quality assurance, or a related analytical role is preferred but not required.
Offer Details:
- Commitments Required: 30-40 hours per week with 4 hours of overlap with PST.
- Engagement type: Contractor
- Engagement Length: upto 24 weeks
Evaluation Process -
- Shortlisted candidates will be sent a Job Interest Form.
- Finalized talents will go through delivery review & proceed further accordingly.
Engagement Length: Up to 24 weeks, Full-time (8 hours/day) 40 hours per week. Overlap 4 hours with PST (Weekend on-call availability required) Rate Range: $500 per task (considering 30-40 hours of AHT per task)
This is a Pay-Per-Task model which means you'll get paid per task for an estimated hourly rate.
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Role Details
About This Role
LLM Engineers specialize in building applications powered by large language models. They design RAG systems, fine-tune models, build agent frameworks, and optimize inference pipelines for cost and latency. This is the role that didn't exist three years ago and now has thousands of open positions.
The scope is broad. You might be building a customer support chatbot that needs to pull from a knowledge base of 50,000 documents, or designing an agent that can navigate a company's internal tools to complete multi-step tasks. The common thread is taking a foundation model and making it do something useful, reliably, at scale, without bankrupting the company on API costs.
Across the 9,653 AI roles we're tracking, LLM Engineer positions make up 0% of the market. At CareerFlow, this role fits into their broader AI and engineering organization.
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
What the Work Looks Like
A typical week includes: building and testing RAG pipelines (chunking strategies, embedding models, retrieval evaluation), debugging why the agent took a wrong action path, optimizing inference costs (caching, batching, model selection), and working with the product team on new LLM-powered features. You'll context-switch between deep technical work and cross-functional collaboration.
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
Skills Required
RAG and vector databases are the most common requirements. Expect to work with LangChain or LlamaIndex, embedding models, and at least one vector store (Pinecone, Weaviate, Chroma). Python is non-negotiable. Understanding the cost/latency/quality tradeoffs between different model providers and architectures is what separates senior from junior engineers.
Fine-tuning experience is valuable for specific use cases but most production LLM work is RAG-based. Agent frameworks (LangGraph, CrewAI, custom orchestration) are increasingly important as companies move beyond simple chat interfaces. Evaluation and observability tools (LangSmith, Arize, custom dashboards) are essential for production deployments.
Look for roles that specify the production stack, mention specific use cases, and talk about cost optimization. Companies that understand LLM engineering will mention evaluation methodology, latency requirements, and scale targets. Vague 'build AI features' postings often mean they haven't figured out their architecture yet.
Compensation Benchmarks
LLM Engineer roles pay a median of $160,000 based on 33 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
CareerFlow AI Hiring
CareerFlow has 1 open AI role right now. They're hiring across LLM Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into LLM Engineer roles include Software Engineer, ML Engineer, Data Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, AI Engineering Manager.
The fastest path is through software engineering. If you can build production systems and you understand LLM capabilities and limitations, you're already qualified for most roles. Build a portfolio project that demonstrates RAG implementation, evaluation, and cost optimization. Open-source contributions to LLM frameworks are strong signals to hiring managers.
What to Expect in Interviews
Technical screens cover RAG architecture design, embedding model selection, chunking strategies, and retrieval evaluation. Expect questions about cost optimization: how you'd reduce inference costs by 50% without degrading quality. System design rounds often present scenarios like 'design a customer support chatbot that can access 100K documents' and evaluate your understanding of the full stack from embedding to serving.
When evaluating opportunities: Look for roles that specify the production stack, mention specific use cases, and talk about cost optimization. Companies that understand LLM engineering will mention evaluation methodology, latency requirements, and scale targets. Vague 'build AI features' postings often mean they haven't figured out their architecture yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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