D
Founding AI Engineer
Discernis
5 hours ago
Full-time
On-site
New York City, New York, United States
About Discernis
Discernis builds AI for legal discovery at a scale where general legal AI tools fall apart. When a litigation team needs to know what a group of people knew, and when, across millions of emails, chats, and documents, our platform finds it with the recall attorneys can stake a case on. We deploy in private cloud and fully air-gapped on-prem for AmLaw firms and corporate legal teams. We’re seed-stage and venture-funded, have paying Big Law customers, and have a pipeline of AmLaw firms and Fortune 500 companies lined up for paid pilots and future deals. Industry incumbents are coming to us to integrate. Discovery is our wedge: the bigger goal is helping enterprises understand and act on their largest, messiest datasets. We’re a small team based in NYC, and you’d be joining before our next stage of growth. How we work
We value ownership, curiosity, and shipping. We hire people who teach themselves new things, make good calls without much direction, and care about getting it right for the attorneys who depend on us. We work hard at a sustainable pace, without crunch culture or arbitrary deadlines. The role
Today our founder runs the AI stack personally. He holds a Columbia PhD in AI and built Amazon’s first customer-facing RLHF pipeline. You’ll take the stack over, all of it: agentic workflows, retrieval and evaluation, inference optimization, fine-tuning, and post-training. For your first ~90 days you’ll work side by side with him. After that, you set our AI direction and grow the team around it. At a larger company, this would be three separate roles. Here, it’s one. This is likely the single highest-impact role at the company, and we need someone who can do the engineering and the science. Rough split
~40% agentic systems:
orchestration, tool use, and reliability
~20% inference:
serving and optimization, including small models on constrained on-prem hardware
~40% longer-horizon research:
model adaptation, post-training, and RL
Problems you’ll own
High-recall retrieval and synthesis across billion-token document collections
Agents that are reliable, testable, and deployable, not just impressive in a demo
Getting strong performance from small open-source models in air-gapped environments
Evals that capture what “correct” means to a litigator
RL that trades off tool use against compute
Our stack
Models:
open-weight models we fine-tune, adapt, and post-train for legal reasoning, then serve as our own versions
Serving:
vLLM on our own H100 and H200 GPUs
Deployment:
the same models run in private cloud and fully air-gapped customer environments
Language:
Python
Around it:
PostgreSQL, Elasticsearch, Kubernetes (K3s), Vue
What we’re looking for
You’ve trained or post-trained real models (fine-tuning, RLHF, or RL). Experience calling APIs is not enough.
You’ve also shipped LLM or agentic systems to production that real users depend on.
You have strong evaluation instincts and know how to tell real gains from noise.
You have hands-on inference and serving experience.
We’re hiring for this role across levels, from strong AI engineer to AI lead. Compensation reflects the level we hire at. Nice to have
Experience on a big tech AI team or at an AI startup
A PhD or MS in ML
Research publications or open-source work in LLMs, agents, RL, or evaluation
Distributed training or large-scale GPU experimentation
Our process
A call with our founder
A screen with our Head of Engineering
A deep dive on a real problem
Why Discernis
Research problems with a direct path to production and paying customers
A founder who speaks your language
Meaningful equity with a 4-year vest and 1-year cliff
Health insurance
Unlimited PTO plus standard US holidays
If you need an accommodation during the hiring process, email us at admin@discernis.ai.
#J-18808-Ljbffr
Discernis builds AI for legal discovery at a scale where general legal AI tools fall apart. When a litigation team needs to know what a group of people knew, and when, across millions of emails, chats, and documents, our platform finds it with the recall attorneys can stake a case on. We deploy in private cloud and fully air-gapped on-prem for AmLaw firms and corporate legal teams. We’re seed-stage and venture-funded, have paying Big Law customers, and have a pipeline of AmLaw firms and Fortune 500 companies lined up for paid pilots and future deals. Industry incumbents are coming to us to integrate. Discovery is our wedge: the bigger goal is helping enterprises understand and act on their largest, messiest datasets. We’re a small team based in NYC, and you’d be joining before our next stage of growth. How we work
We value ownership, curiosity, and shipping. We hire people who teach themselves new things, make good calls without much direction, and care about getting it right for the attorneys who depend on us. We work hard at a sustainable pace, without crunch culture or arbitrary deadlines. The role
Today our founder runs the AI stack personally. He holds a Columbia PhD in AI and built Amazon’s first customer-facing RLHF pipeline. You’ll take the stack over, all of it: agentic workflows, retrieval and evaluation, inference optimization, fine-tuning, and post-training. For your first ~90 days you’ll work side by side with him. After that, you set our AI direction and grow the team around it. At a larger company, this would be three separate roles. Here, it’s one. This is likely the single highest-impact role at the company, and we need someone who can do the engineering and the science. Rough split
~40% agentic systems:
orchestration, tool use, and reliability
~20% inference:
serving and optimization, including small models on constrained on-prem hardware
~40% longer-horizon research:
model adaptation, post-training, and RL
Problems you’ll own
High-recall retrieval and synthesis across billion-token document collections
Agents that are reliable, testable, and deployable, not just impressive in a demo
Getting strong performance from small open-source models in air-gapped environments
Evals that capture what “correct” means to a litigator
RL that trades off tool use against compute
Our stack
Models:
open-weight models we fine-tune, adapt, and post-train for legal reasoning, then serve as our own versions
Serving:
vLLM on our own H100 and H200 GPUs
Deployment:
the same models run in private cloud and fully air-gapped customer environments
Language:
Python
Around it:
PostgreSQL, Elasticsearch, Kubernetes (K3s), Vue
What we’re looking for
You’ve trained or post-trained real models (fine-tuning, RLHF, or RL). Experience calling APIs is not enough.
You’ve also shipped LLM or agentic systems to production that real users depend on.
You have strong evaluation instincts and know how to tell real gains from noise.
You have hands-on inference and serving experience.
We’re hiring for this role across levels, from strong AI engineer to AI lead. Compensation reflects the level we hire at. Nice to have
Experience on a big tech AI team or at an AI startup
A PhD or MS in ML
Research publications or open-source work in LLMs, agents, RL, or evaluation
Distributed training or large-scale GPU experimentation
Our process
A call with our founder
A screen with our Head of Engineering
A deep dive on a real problem
Why Discernis
Research problems with a direct path to production and paying customers
A founder who speaks your language
Meaningful equity with a 4-year vest and 1-year cliff
Health insurance
Unlimited PTO plus standard US holidays
If you need an accommodation during the hiring process, email us at admin@discernis.ai.
#J-18808-Ljbffr