研究工程师，训练后
Research Engineer, Post-Training
WHY HARVEY
在Harvey，我们正在改变法律和专业服务的运作方式。通过结合前沿的代理AI、企业级平台和深厚的领域专业知识，我们正在重塑未来数十年关键知识工作的完成方式。
这是一个难得的机会，帮助打造一家具有时代意义的公司，在真正的转折点上共同成长。我们拥有强大的产品市场匹配度和世界级的投资支持。我们正在快速扩展，并实时定义一个新类别。这项工作充满雄心，标准很高，个人、职业和财务上的成长机会无与伦比。
我们的团队行动迅速，有主人翁意识，并对使命充满热情——以高强度运作，贴近客户，互相推动追求卓越。我们秉持三个价值观：决断力、简洁性以及“工作尚未完成”。我们在清晰判断的基础上快速行动，而非等待完美信息；我们认为简洁是可扩展的关键；我们从不满足于现状。如果你希望与志同道合、充满激情的人一起完成职业生涯中最出色的工作，我们期待与你携手共建。
在Harvey，专业服务的未来正在今天被书写——而我们才刚刚开始。
职位概述
后训练阶段是Harvey将专家反馈和代理轨迹转化为在法律工作上更有意义的改进模型的方式。我们正在寻找一名研究工程师，帮助扩大这一循环：定义并运行模型训练实验，解释结果，并与内部和外部研究合作伙伴一起构建更好的数据、环境、评估者和训练方案。
此职位适合能够自主管理模型训练和应用研究项目的人选。你将与内部和外部研究合作者紧密合作，推进对产品路线图有重要意义的后训练工作。理想的候选人具备在研究或生产环境中训练开源权重模型的丰富实践经验，并具备足够的工程深度，能够高效地运行和调试实验。
职责内容
- 主导后训练实验，在成本、延迟、安全性和治理之间找到最佳平衡，提升代理性能。
- 优化代理框架，包括领域特定技能、工具、子代理、检索策略和验证循环，提高长期法律工作的质量。
- 设计和开发可靠的评估系统和奖励机制，既足够可靠用于评估，又足够高效用于迭代
查看英文原文
WHY HARVEY
At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come.
This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched.
Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you.
At Harvey, the future of professional services is being written today — and we’re just getting started.
ROLE OVERVIEW
Post-training is how Harvey turns expert feedback and agent traces into models that are meaningfully better at legal work. We are looking for a research engineer who can help scale that loop: defining and running model training experiments, interpreting results, and working with internal and external research partners to build better data, environments, graders, and training recipes.
This role is for someone who can self-manage model training and applied research projects. You will work closely with internal and external research collaborators on post-training efforts that matter to our product roadmap. The ideal candidate has extensive hands-on experience training open weight models, either in a research or production setting, and enough engineering depth to run and debug experiments efficiently.
WHAT YOU'LL DO
- Drive post-training experiments, pushing agent performance while navigating the Pareto frontier of cost, latency, security, and governance.
- Optimize agent harnesses, including domain-specific skills, tools, subagents, retrieval strategies, and validation loops that improve quality on long-horizon legal work.
- Design and develop grading and reward systems that are reliable enough for evaluation, efficient enough for iteration, and strict enough for high-stakes legal work.
- Study agent behavior, identifying patterns that correlate with successful work product, and converting those findings into training data, evals, or harness changes.
- Work with Harvey researchers and external research partners to define experiments, evaluate methodology, review results, and keep projects moving toward concrete model improvements.
WHAT YOU HAVE
- Hands-on experience with post-training or model-training work, such as SFT, preference optimization, RLHF/RLAIF, reward modeling, distillation, or adapting open-weight models to specialized domains.
- Strong judgment about model behavior: you can read traces, inspect outputs, identify failure modes, and reason about whether a metric is measuring the thing that matters.
- Strong Python and research-engineering ability. You can write clean code, debug experiments, and build the simple but reliable systems needed to make research move faster.
- Ability to self-manage ambiguous applied research projects and communicate clearly with researchers, engineers, product teams, domain experts, and external partners.
Nice to Have
- Experience building data or evaluation infrastructure for ML workflows, such as dataset curation pipelines, model-output processing, experiment tracking, evaluation dashboards, or regression analysis tooling.
- Experience with distributed training, inference systems, GPU workloads, or large-scale ML experimentation.
- Research publications, open-source contributions, or shipped industry work in LLMs, agents, evaluation, or ML systems.
COMPENSATION
$231,000 - $340,000
DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES [HERE https://www.notion.so/harveyai/Harvey-Candidate-Privacy-Policies-319ac3fcdd7a803bb807d5094f249922].
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Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law.
We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai