Machine Learning Engineer

💼 Machine Learning Engineer

Full-time | Hybrid | NYC or San Francisco

Compensation: $200K – $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 products.

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 teams.

💼 About the Company:

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 They’re combining first-party healthcare data with cutting-edge AI to streamline one of the most complex and outdated systems in the world — from insurance to drug access to patient support.

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 modeling.
• Lead zero-to-one experiments and deliver models that ship to real customers.
• Collaborate directly with business and engineering stakeholders to scope, design, and deploy AI-driven features.
• Evaluate new methods, fine-tune models, and continuously improve reliability, latency, and accuracy.
• Build internal tools and pipelines that accelerate future AI development.

This is a Hybrid, high-ownership position for builders who thrive in fast-moving, product-driven environments.

🧠 What We’re Looking For:

Experience

• 1+ years as an AI / ML Engineer, Applied Scientist, or ML Research Engineer
• Hands-on experience building and deploying ML systems in production (not research-only)
• Background at a top-tier tech or early-stage startup that has shipped AI-powered products
• End-to-end project ownership — data, training, infra, deployment, iteration

Technical Skills

• Proficiency with modern ML frameworks (PyTorch, TensorFlow, Transformers, LLM APIs)
• Experience fine-tuning, prompting, or orchestrating large-language-model systems
• Strong foundation in full-stack development (Python + React / TypeScript / PostgreSQL / Kubernetes)
• Comfortable designing scalable data and inference pipelines on cloud (AWS preferred)

Soft Skills

• Low-ego, high-ownership mindset
• Strong written + verbal communication and cross-team collaboration
• Bias toward speed, clarity, and tangible results

Nice to Have

• Founder or early-startup experience
• Pear Fellow / Neo Scholar background
• Degree 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 compounds quickly.
• High autonomy + ownership: you’ll shape not just the product but the AI infrastructure

🧩 Interview 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 system you’ve built.
• Systems Design (45 min): Evaluate how you approach scaling, deployment, and architecture.
• Onsite / Final Round (Half Day): Collaborative project with the team to assess real-world problem solving and communication.