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Why this grade This listing scored 63/100, which is a C. It lost the most ground on pay transparency. See the breakdown
- Description depth 20 / 20 How much the posting actually says about the work, measured in characters of real text.
- Freshness 12 / 15 How recently it was posted. Older postings are likelier to be filled or abandoned.
- Pay transparency 12 / 25 A published salary range, worth more than any other single factor because it is what a candidate cannot find out without applying.
- Remote clarity 8 / 15 Whether "remote" means anywhere, or is quietly restricted to one country.
- Role specificity 6 / 10 Whether the listing is tagged well enough to tell what the role actually is.
- Corroboration 5 / 10 Whether more than one source carries this listing.
Every figure above is arithmetic over the posting itself — its salary field, its text, its age, its tags and how many sources carry it. How the grades work →
This listing does not state a salary
$163k – $276k
That is the middle half of what comparable roles paid on this board over the last 90 days — 31 listings that did publish a figure, median $179k. It is not this employer's offer, and we have no idea what they pay. It is only what the rest of the market advertised.
Developer Operations Mid level Full Time
Responsibilities
- - Agentic RAG & Engineering: Design and operate next-generation retrieval pipelines — moving beyond static retrieve-once patterns to adaptive, self-correcting, and multi-hop retrieval workflows; architect Agentic RAG systems with dynamic retrieval control, query decomposition, iterative retrieve-reflect-refine loops, and multi-agent retrieval collaboration
- Frontier Harness: Collaborate deeply with researchers and engineers to define and implement model-capability-driven innovations — including context management, long-term memory, subagent and multi-agent architectures, self-evolving agents, and real-word task execution
- Benchmarking & Evaluation: Propose harness-domain and RAG-domain benchmarks and evaluation methodologies; construct benchmark datasets, define annotation strategies, and systematically measure and improve agent intelligence across domains — including retrieval efficiency, latency, groundedness, and task success rate
- Real-world Feedback Loops: Leverage multi-channel user feedback and real-world task data as primary research signals; design experiments and datasets to continuously improve agent and retrieval performance in production scenarios
Requirements
- - 2-8+ Year hands-on experience with LLM, RAG and AI agent systems in production
- RAG & Agentic RAG Engineering: Hands-on experience building production retrieval pipelines end-to-end — embedding models (BGE, OpenAI, etc.), vector stores (Qdrant, Milvus, Pinecone, Weaviate), hybrid search (keyword + vector), reranking models; deep understanding of chunking strategy, text cleaning, and multimodal data parsing; experience implementing Agentic - RAG patterns — Self-RAG, Corrective RAG, adaptive retrieval, multi-hop decomposition, retrieve-reflect-refine loops
- Agent Harness Engineering — hands-on experience with Agent Harness runtimes (Pi Agent, AgentScope 2.0 or equivalent orchestration frameworks): session recovery, sandbox isolation, middleware/hook systems, multi-tenant runtime, plan/execute loops, and retrieval-grounded tool calling
- LLM & Agent Fundamentals: Deep familiarity with LLM and agent mechanisms — LLM APIs, KV Cache, Agent Loop, Tool Use, Reasoning, Planning, Skills, MCP, Memory, Subagent, Multi-Agent; strong grasp of Prompt Engineering, Context Engineering
- Independent Research Capability: Can analyze ambiguous problems from first principles, generate original ideas, and drive research from 0 to 1; able to rapidly translate ideas into runnable prototypes with tight experiment iteration loops
- Heavy Agent User: Power user of agent products (coding agents, general-purpose agents); agent tools are already integrated into your daily work and life; you have taste and judgment about model behavior
- AI-native Engineering: Proficient in vibe coding — ships fast using AI-assisted workflows across unfamiliar languages, frameworks, and domains; strong learning velocity in software development
Why Binance
• Shape the future with the world’s leading blockchain ecosystem• Collaborate with world-class talent in a user-centric global organization with a flat structure• Tackle unique, fast-paced projects with autonomy in an innovative environment• Thrive in a results-driven workplace with opportunities for career growth and continuous learning• Competitive salary and company benefits• Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team) Binance is committed to being an equal opportunity employer. We believe that having a diverse workforce is fundamental to our success. By submitting a job application, you confirm that you have read and agree to our Candidate Privacy Notice.Originally posted on Himalayas
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Where this listing came from
- 19 Sep 2026 Himalayas first sighting
Seen on 1 board over 0 days.