Staff Applied AI Engineer, Harvey
$238-312k
+ Offers Equity + Offers Bonus
Domain-specific AI for legal and professional services
Open for applications
Domain-specific AI for legal and professional services
501-1000 employees
Open for applications
$238-312k
+ Offers Equity + Offers Bonus
501-1000 employees
Company mission
To help professionals develop expertise, faster.
Role
Who you are
- 7+ years of experience (post-BS/MS) in an engineering role
- Experience with shipping a scaled and impactful product powered by machine learning: how to use offline datasets, online experiments, and recent research to build simple and high performance systems. Prior experience with LLMs and retrieval pipelines is not required
- Track record of shipping reliable products and a strong attention to detail
- Experience building backend platforms that can support multiple product lines
- Grit - experience working at early-stage startups is a plus
What the job involves
- As an Applied AI Engineer on the Engineering team at Harvey, you will own and lead engineering projects across our various product lines. We are looking for individuals who have worked across the stack on incredible products and have experience building products where machine learning models are a core component
- This role is based in New York City, NY. We use an in-person work model and offer relocation assistance to new employees
- Conduct data collection, experimentation, and analysis to drive algorithmic development for RAG and multi-step AI pipelines
- Zero-to-one product development: rapidly prototype, evaluate, integrate, and test new product features in close partnership with our legal team
- Develop new AI native workflows: implement streaming, long-running tasks, procedural UX, etc. for new AI tasks, finding the balance between state-of-the-art and pragmatism
- Representative Projects
- Partner with our legal team to design and implement a method for evaluating the correctness of document citations. Use this to build a large dataset of ground-truth citations and then improve our core citation algorithm. You can read about some of our work on evaluation and citations here (1, 2, 3)
- Make targeted improvements to our RAG pipelines to improve answer quality for user questions over corpuses of complex data, like massive banks of spreadsheets or Japan’s tax code
- Design and build systems that leverage state-of-the-art LLMs from multiple model providers, including custom models
- Work across the stack and with our legal team to create seamless multi-step AI workflows for complex legal tasks, like corporate merger due diligence
Our take
The explosive popularity of generative AI tools has seen specialized solutions popping up for all kinds of industries. Hoping to help supercharge efficiency and productivity for legal teams, Harvey supplies generative AI that can assist with documentation, as well as aid research and optimize workflows.
Via natural language instructions, Harvey helps draft and analyze legal documents. It can also answer complex questions across large document sets, or even handle research queries that cover multiple domains in legal, regulatory and tax.
Its ability to support legal practitioners as if it's another member of the team is what makes Harvey particularly interesting. This approach has seen the company raise substantial funding, reaching Unicorn status in 2024. Funds are now being used to expand to new geographies, grow headcount and further collect and curate data to train more “domain-specific” AI models.
Kirsty
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 9 rounds)
Oct 2025
$160m
LATE VC
Oct 2025
$59m
GROWTH EQUITY VC
Total funding: $1.0bn
Company HQ
Financial District, San Francisco, CA
Leadership
Winston Weinberg
(Co-Founder & CEO)Graduated with a Juris Doctor from the USC Gould School of Law, then spent 1 year as an Associate at O'Melveny & Myers before founding Harvey.
Gabe Pereyra
(Co-Founder & President)Previously worked as a Research Scientist at Meta and DeepMind. Also spent a year as a Brain Resident at Google.
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