Machine Learning Engineer

Protech Talent; New York (NY); Hybrid; USD 200,000 - 400,000; Full time

Job summary

Protech Talent seeks an Applied AI Engineer to transform machine-learning research into impactful products. This role entails full autonomy over projects, from data pipelines to model deployment. You'll collaborate with various teams in a hybrid work environment, making substantial contributions to a fast-growing AI-driven healthcare startup.

Ideal candidates possess hands-on experience deploying machine learning systems and a background at top tech firms. Join us to innovate in healthcare and drive meaningful change.

Qualifications

• 1+ years as an AI/ML Engineer, Applied Scientist, or ML Research Engineer.
• Hands-on experience building and deploying ML systems in production.
• Strong written and verbal communication skills.

Responsibilities

• Build and productionize ML and LLM-based systems.
• Collaborate with business and engineering to deploy AI features.
• Evaluate new methods and improve model accuracy.

Skills

ML system deployment

Machine learning frameworks (PyTorch, TensorFlow)

Data extraction and classification

Large-language model orchestration

Cross-team collaboration

Education

Degree in CS or related field

Tools

AWS

Job description

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 They’re combining first‑party healthcare data with cutting‑edge AI to streamline one of the most complex and outdated systems in the world — from insurance to drug access to patient support.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