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# Applied Machine Learning Engineer

- **Company:** Elorian, LLC
- **Location:** Palo Alto, CA, United States
- **Experience:** Expert
- **Salary:** $250,000
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Application Programming Interfaces (APIs), Artificial Intelligence, Data Architecture, Software Debugging, Python (Programming Language), Machine Learning, Language Modeling, Open Source Technology, Systems Integration, TypeScript, Data Ingestion, Large Language Models, Model Validation, Agentic-AI, Integration Frameworks, Live Streaming, Data Management, Machine Learning Operations, Api Design, Data Pipelines, Web Api
- **Published:** November 9, 2026
- **Apply:** https://www.careerbuilder.com/job-details/applied-machine-learning-engineer-palo-alto-ca--5e4eef08-a55f-499f-a7d6-af4473fd1925

## About the Role

Machine Learning, Glimpse, Artificial Intelligence (AI), Research Laboratory, Civil Engineering, Bridge Building, Product Engineering, Data Management, High Throughput, Product Demonstration, * Strong Core Engineering: 5+ years of experience building and shipping high-performance, production software in Python (and ideally TypeScript or Go/Rust). You write clean, maintainable code and can speak deeply to your architectural decisions.

 * Hands-on LLM/Multimodal Application Engineering: Experience building complex systems beyond basic API calls-such as agentic workflows, structured tool-use frameworks, model evaluation pipelines, or orchestration layers for vision-language models.
 * Model Understanding & Evaluation: Hands-on experience evaluating, fine-tuning, or benchmarking ML models. You can systematically differentiate between model failure modes, pipeline errors, and data-quality issues.
 * Full-Lifecycle Ownership: A builder mindset with a bias for shipping quickly. Experience as a founding engineer, startup core contributor, or forward-deployed engineer is highly valued.
 * Systems & Data Architecture: Familiarity with modern cloud architecture, API design, vector/multimodal indexing, streaming protocols, and handling high-volume image/video data pipelines under tight latency and cost constraints.
 * Clear Technical Communication: Ability to communicate complex architectural tradeoffs clearly to both internal research teams and external engineering leaders., * Prior experience at a foundation model provider, AI developer platform, or ML infrastructure company.
 * Direct experience building multimodal applications in vision-heavy verticals (e.g., robotics, industrial automation, manufacturing, geospatial, medical imaging, or media production).
 * Experience developing public developer APIs, open-source AI tooling, or developer ecosystems.

## Description

As our first Applied, User-Facing Member of Technical Staff, you will build the engineering bridge between our core multimodal research and real-world production deployments. This is an engineering-first, high-impact role where you will design and implement reference architectures, production-grade integration frameworks, developer SDKs, evaluation harnesses, and bespoke end-to-end applications powered by Glimpse.

You will sit at the intersection of product engineering, model evaluation, and customer architecture. Working directly with founders, early enterprise partners, and our internal research team, you will turn complex multimodal requirements into robust, high-performance systems and feed real-world constraints directly into our model roadmaps.

What You Will Do

 * Architect & Build Production Integrations: Design, build, and deploy low-latency, scalable application architectures integrating Glimpse into complex enterprise data pipelines and high-throughput workflows.
 * Develop Reference Systems & Tooling: Create open-source reference implementations, production-ready demo applications, developer tooling, and SDK components that establish the standard for building on Glimpse.
 * Build Evaluation & Benchmarking Infrastructure: Design and implement rigorous evaluation pipelines and benchmarking harnesses to systematically test Glimpse against other frontier multimodal models on diverse, real-world visual datasets.
 * Debug Across the Stack: Systematically isolate and resolve bottlenecks across application pipelines, data ingestion layers, model inference APIs, and prompt/orchestration logic.
 * Partner Technically with Early Adopters: Serve as the core technical architect for engineering teams at design-partner companies, guiding technical discovery, proof-of-concept development, and deployment to production.
 * Influence Model & Platform Roadmaps: Translate real-world application failure modes, edge cases, and performance requirements into prioritized specifications for our core research and model infrastructure teams.