Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Cloud AI Engineer (Legal & Compliance)
- Remote from
- USA
- Salary
- USD 186,300–347,300 / yr
- Department
- Software Engineering
- Employment
- Full Time
- Experience
- Open level
- Published
- Apply before
- 8 Dec 2026
- Listing views
- 33
- Application actions
- 3
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About this role.
Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by 300+ million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products. Binance offerings range from trading and finance to education, research, payments, institutional services, and more. Our mission is to increase the freedom of money and financial access for people around the world.
About The Role
This role works on Americas business hours, with a schedule that overlaps with our Legal, Compliance, and MLRO teams. Legal and Compliance is building AI tools to support our regulatory work, helping the team review policies, track launch requirements, and manage complex legal documents more efficiently. In this role, you’ll design and evaluate the LLM and retrieval systems that support these tasks, with all outputs reviewed by our Legal and Compliance professionals. You’ll have room to experiment, prototype quickly, and help shape how the team uses these tools.
What You’ll Do
- Work closely with Legal, Compliance, and the MLRO to support internal compliance policy reviews, files delivery, remediation tracking, and technical support.
- Track compliance-related requirements for new product and service launches, and coordinate action items with relevant stakeholders.
- Partner with Legal and Compliance teams to identify, design, and implement AI-driven solutions that improve the efficiency and scalability of compliance and legal operations.
- Use the enhance intelligent systems leveraging LLMs, RAG, agentic workflows, long-term memory, tool use, subagents, and multi-agent architectures for handling law files.
- Develop benchmark frameworks and evaluation methodologies for legal and compliance AI systems, including benchmark dataset construction, annotation strategy design, and performance measurement.
- Continuously evaluate and improve systems across retrieval quality, latency, groundedness, factuality, and task success metrics.
- Conduct independent research and rapidly prototype new ideas to solve ambiguous legal and compliance workflow challenges.
- Support related administrative and operational activities in accordance with company compliance requirements.
What We’re Looking For
- Solid experience in compliance technology, legal technology, regulatory technology, or software/application development supporting compliance or legal functions.
- Strong understanding of relevant compliance rules, regulations, and day-to-day compliance operations.
- Bachelor’s degree in relevant field is require, i.e AI, Computer Science.
- Hands-on experience building production RAG systems, including embeddings, vector databases, hybrid search, reranking, chunking strategies, text preprocessing, and multimodal parsing.
- Strong understanding of LLM and agent fundamentals, including prompt engineering, context engineering, memory, planning, tool use, MCP, and multi-agent systems.
- Demonstrated ability to conduct independent research, solve ambiguous problems, and rapidly prototype practical solutions.
- Strong written and verbal communication skills.
Nice to Haves
- You have experience working in a legal/compliance related firm or domain
- Experience implementing advanced Agentic RAG approaches such as Self-RAG, Corrective RAG, adaptive retrieval, and multi-hop reasoning workflows.
- Bilingual English/Mandarin is a nice to have, so you will be able to coordinate with overseas partners and stakeholders.
Annual Base Salary
$186,300—$347,300 USD
Why Binance
- Shape the future of finance
- Collaborate with world-class talent in a user-centric global organization with a flat structure
- Tackle challenging projects in a fast-paced and innovative enviornment with autonomy
- Thrive in a results-driven workplace with opportunities for career growth and continuous learning
- Competitive salary and company benefits
- Remote-first, work-from-home arrangement (may vary depending on the nature of the role and team)
Binance is committed to being an equal opportunity employer. We believe that having a diverse workforce is fundamental to our success.
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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Sample interview questions
I would begin by defining user tasks, risk boundaries, and measurable success criteria with legal stakeholders. I would build an ingestion pipeline with document versioning, metadata, chunking tuned to legal structure, hybrid retrieval, reranking, citation generation, and access controls. I would then evaluate retrieval recall, answer groundedness, factuality, latency, and reviewer acceptance using a curated benchmark set.
I would require retrieval-backed responses with clear citations, constrain the model to supplied evidence, and define abstention behavior when sources are insufficient or conflicting. I would also use structured outputs, automated grounding checks, adversarial evaluation cases, and human review for high-risk decisions. Monitoring production feedback and failure patterns would drive iterative improvements.
I would measure retrieval recall and precision, citation correctness, groundedness, factual accuracy, task completion rate, tool-call success rate, latency, and cost per successful task. For agentic workflows, I would additionally inspect planning quality, unnecessary tool usage, recovery from failures, and whether the final output follows compliance-approved policies.
I would translate technical trade-offs into workflow outcomes, risks, and measurable acceptance criteria. Early prototypes and annotated examples would help validate assumptions quickly, while regular review sessions would let subject-matter experts identify unsafe outputs. I would document system limitations, escalation paths, and ownership for model changes clearly.
I would first map the current process, users, documents, decisions, controls, and failure consequences. I would identify a narrow, low-risk prototype with explicit success metrics and a human-review loop, then test it against representative and adversarial cases. Based on results, I would decide whether to scale the solution, revise the design, or keep the task human-led.
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