Platform Engineer - Generative AI

Uncountable Inc.

Job Overview

Location

New York, San Francisco, Munich or London

Job Type

Full-time

Full Job Description

📋 Description

• Own the end-to-end delivery of LLM-powered product features that will be used daily by scientists at Fortune-500 materials, chemistry, and biotech companies. You’ll move from whiteboard sketch to production deployment in days, not months, shipping search, visualization, and conversational assistants that compress weeks of lab work into minutes.
• Architect and harden Uncountable’s in-house generative-AI stack: low-latency inference services, prompt-caching layers, fine-tuning pipelines, A/B testing harnesses, and real-time observability dashboards. Your decisions will directly determine how quickly our users can iterate on hypotheses and how confidently they can trust AI-generated insights.
• Pair cutting-edge prompting techniques with rock-solid full-stack engineering. On any given week you may optimize a diffusion model’s token budget, refactor a Flask microservice for 10× throughput, or craft a React component that renders 3-D molecular structures streamed from an LLM—then watch scientists use it the next morning.
• Champion a culture of rapid experimentation. We deploy multiple times per day; you’ll instrument every feature with analytics, interview users for qualitative feedback, and roll forward or roll back in hours. Your metrics will guide the roadmap and prove ROI to R&D directors who have never before relied on AI.
• Act as product-manager-by-default for the features you ship. You’ll translate vague scientific pain points (“I need to find every experiment where yield dropped after solvent swap”) into crisp specs, prioritize edge cases, and negotiate scope so that the 80 % solution ships this week and the remaining 20 % ships the next.
• Collaborate with a cross-functional team of PhD scientists, designers, and DevOps engineers in NYC, SF, Munich, or London. Whether white-boarding multimodal UX flows or pair-programming at 2 a.m. to squash a GPU memory leak, you’ll teach and learn in equal measure.
• Contribute to open-source and publish technical deep-dives on our blog. We believe the fastest way to raise the bar for scientific AI is to share what we learn about prompt engineering, evaluation frameworks, and scalable LLMOps.
• Lay the groundwork for the next generation of Uncountable’s platform. Today we serve thousands of scientists; tomorrow we’ll serve millions. Your early choices on data pipelines, model versioning, and security will echo for years.
• Enjoy unmatched career acceleration. Past engineers have spun out their own AI startups with our blessing, armed with the network, skills, and war stories gained here. Whether you dream of founding a company or becoming a Staff/Principal engineer, this role is your launchpad.

🎯 Requirements

• 2+ years of professional software development experience
• Demonstrated interest in generative AI—side projects, open-source contributions, or prior LLM product experience count
• Solid CS fundamentals: algorithms, data structures, and system design
• B.S. in Computer Science or equivalent (preferred)
• Track record of shipping user-facing software that incorporates generative AI (preferred)
• Familiarity with full-stack web development (React, Flask/Postgres) (preferred)

🏖️ Benefits

• Competitive base salary of $120k–$160k plus meaningful equity
• Comprehensive health and dental insurance
• 401(k) with employer contribution
• Hybrid work with offices in New York, San Francisco, Munich, and London
• Rapid iteration culture—ship daily, learn weekly, grow monthly

Skills & Technologies

Python

TypeScript

React

Flask

Hybrid

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Uncountable Inc.

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About Uncountable Inc.

Uncountable provides a cloud-based R&D data platform that helps materials, chemicals, and life-science manufacturers replace scattered spreadsheets and siloed lab notebooks with a unified, searchable system. The software structures experimental results, links formulations to performance data, and applies machine-learning models to predict outcomes, accelerating formulation cycles and reducing trial-and-error lab work. Teams use the platform to design experiments, capture measurements, share knowledge, and generate regulatory reports while maintaining full data traceability and version control across global research sites.

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