Machine Learning Engineer

💼 Machine Learning Engineer Full-time | Hybrid | NYC or San Francisco Compensation: $250K – $400K + Competitive Equity 🚀 About the Role : We’re looking for a n Applied AI Engine er 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 They’re combining first-party healthcare data with cutting-edge AI to streamline one of the most complex and outdated systems in the world — from insurance to drug access to patient sup port.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 cultu re 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)Backg round at a top-tier tech or early-st age 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 f unding + team: backed by leading investors; small, elite engineering org where impact comp ounds quickly.High autonom y + 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 Int erview (45 min): Coding-focused (Python), similar to a Leetcode -style exercise.Project Walkt hrough (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 R ound (Half Day): Collaborative project with the team to assess real-world problem solving an d communication.