💼 Machine Learning Engineer
Full-time | Hybrid | NYC or San Francisco
Compensation: $250K – $400K + Competitive Equity
🚀 About the Role
:
We’re looking for an Applied AI Engineer to help turn cutting-edge machine-learning research into production-grade, revenue-driving product
s.
You’ll own projects end-to-end — from model selection and data pipelines to deployment, monitoring, and iteration in live environments. Expect full autonomy, high accountability, and constant cross-functional collaboration with product and operations tea
ms.
💼 About the Compa
ny:
This company is a fast-growing AI-driven healthcare startup on a mission to make life-changing therapies accessible faster and more affordably. They’re combining first-party healthcare data with cutting-edge AI to streamline one of the most complex and outdated systems in the world — from insurance to drug access to patient sup
port.
Backed by top-tier investors (including funds behind companies like Stripe, OpenAI, and Airbnb), they’re scaling rapidly and have already achieved strong product-market fit. The team is composed of exceptional engineers, operators, and scientists from top startups and research
labs.The culture is intense, collaborative, and ownership-driven — ideal for builders who thrive in zero-to-one environments and want to see their work make a measurable impact on real
lives.
What you
- ’ll do:
Build and productionize ML and LLM-based systems that power automation, prediction, and intelligent - search.Combine techniques like data extraction, document classification, workflow orchestration, and multimodal m
- odeling.Lead zero-to-one experiments and deliver models that ship to real cu
- stomers.Collaborate directly with business and engineering stakeholders to scope, design, and deploy AI-driven f
- eatures.Evaluate new methods, fine-tune models, and continuously improve reliability, latency, and a
- ccuracy.Build internal tools and pipelines that accelerate future AI deve
lopment.
This is a Hybrid, high-ownership position for builders who thrive in fast-moving, product-driven envi
ronments.
🧠What We’re Lo
oking For:
Experience1+ years as an AI / ML Engineer, Applied Scientist, or ML Resear- ch EngineerHands-on experience building and deploying ML systems in production (not res
- earch-only)Background at a top-tier tech or early-stage startup that has shipped AI-power
- ed productsEnd-to-end project ownership — data, training, infra, deployment
, iterationTechn
- ical SkillsProficiency with modern ML frameworks (PyTorch, TensorFlow, Transformers
- , LLM APIs)Experience fine-tuning, prompting, or orchestrating large-language-mo
- del systemsStrong foundation in full-stack development (Python + React / TypeScript / PostgreSQL /
- Kubernetes)Comfortable designing scalable data and inference pipelines on cloud (AWS
preferred)
- Soft SkillsLow-ego, high-owners
- hip mindsetStrong written + verbal communication and cross-team co
- llaborationBias toward speed, clarity, and tangi
ble results
- Nice to HaveFounder or early-startu
- p experiencePear Fellow / Neo Schola
- r backgroundDegree in CS or related field from a top program (or equivalent practical
excellence)
💡 Why Join:
Product-market fit + hypergrowth: the platform already serves thousands of users and is- scaling fast.AI-first mission: core business outcomes are directly driven by applied ML and
- generative AI.Top-tier funding + team: backed by leading investors; small, elite engineering org where impact comp
- ounds quickly.High autonomy + ownership: you’ll shape not just the product but the AI
infrastructure
🧩 Int
- erview Process:
Initial Screen (30 min): Background, motivation, and alignment with - company mission.Technical Interview (45 min): Coding-focused (Python), similar to a Leetcode
- -style exercise.Project Walkthrough (45 min): Deep dive into a previous ML or AI syst
- em you’ve built.Systems Design (45 min): Evaluate how you approach scaling, deployment, a
- nd architecture.Onsite / Final Round (Half Day): Collaborative project with the team to assess real-world problem solving an
d communication.
