Software Engineer, Enterprise AI
Build and scale enterprise Generative AI platform, owning large product areas across backend, frontend, LLMs, and ML models. Requires 4+ years experience, proficiency in Python/JavaScript/SQL, Kubernetes, and cloud providers.
About the job
Scale GP is an enterprise-grade Generative AI platform providing APIs for knowledge retrieval, inference, evaluation, and more. We're seeking a strong engineer to build and scale our product, owning large areas across backend, frontend, LLMs, and ML models while solving scalability and reliability challenges.
You will:
- Own large new areas within our product
- Work across backend, frontend, and interacting with LLMs and ML models
- Deliver experiments at a high velocity and level of quality to engage our customers
- Work across the entire product lifecycle from conceptualization through production
- Be able, and willing, to multi-task and learn new technologies quickly
Ideally you'd have:
- 4+ years of full-time engineering experience, post-graduation
- Experience scaling products at hyper growth startups
- Experience tinkering with or productizing LLMs, vector databases, and the other latest AI technologies
- Proficient in Python or Javascript/Typescript, and SQL
- Experience with Kubernetes
- Experience with major cloud providers (AWS, Azure, GCP)
Skills
Python, JavaScript, TypeScript, SQL, Kubernetes, AWS, Azure, GCP, LLMs, Vector Databases
Similar jobs
ML Engineering jobs
Applied Intuition
Sunnyvale, CA
Optimizes distributed machine learning training and high-throughput offline inference across large accelerator clusters. The role focuses on profiling, scaling efficiency, cluster goodput, GPU performance, and cost-effective processing of autonomy data.
Cinder
New York, NY
Build and operate production machine-learning systems for content safety, from messy customer data through classification, evaluation, and inference. The role requires 5+ years of ML engineering experience, strong Python and MLOps skills, and sound judgment across classical models and LLMs.
Perplexity
San Francisco, CA
Build AI agent harnesses, models, and product capabilities that enable agents to perform complex work across digital environments. The role combines applied AI research and software engineering, requiring Python proficiency, strong product judgment, and experience with agent tooling, reinforcement learning, or browser technologies.
Mercor
San Francisco, CA
Builds the platform, verifiers, environments, and grading infrastructure used to evaluate enterprise AI agents at scale. The role combines strong software engineering with expertise in agent runtimes, evaluation design, benchmarks, and production failure analysis.
Firecrawl
San Francisco, CA
Build and operate production machine-learning systems for search ranking, relevance, extraction quality, and LLM-driven features. The role requires production ML ownership, ranking or relevance expertise, large-scale data experience, Python, and rigorous experimentation skills.