Founding ML Engineer – Flower Frontier Model Team

Job not on LinkedIn

🕒 December 13, 2025

🌐 United States, Germany, +1 more countries – Remote

🇺🇸 United States – Remote 🇩🇪 Germany – Remote 🇬🇧 United Kingdom – Remote

🏄 California, New York – Remote

🏄 California – Remote 🗽 New York – Remote

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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Flower Labs

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2023

🏥 Healthcare

🚘 Automotive

💼 Consulting

Healthcare • Automotive • Consulting

Flower Labs is a company that develops a friendly federated AI framework called Flower, which enables users to implement federated learning across various machine learning frameworks and programming languages. Flower aims to simplify the process of federated learning, allowing efficient distribution and execution of workloads across a multitude of clients. The platform is designed for scalability and usability, supporting diverse devices and cloud systems for research and production purposes.

📋 Description

• Play a critical role in building SOTA LLMs and foundation models within a small, high-impact team • Help build a reliable, maintainable and scalable software stack • Produce world-leading models that are open-sourced and integrated into new Flower Lab products • Design, implement and optimize core components across the full spectrum of stages relevant to frontier model building: data curation, evals, pre-training, post-training • Diagnose and resolve GPU/kernel issues, memory/storage bottlenecks, and multi-node failures at scale • Collaborate on the debugging of training instabilities and related issues • Devise surrounding infrastructure, tooling, monitoring, and observability, essential for large-scale LLM development • Contribute ideas, be heard and influence the direction of the company across the board.

🎯 Requirements

• Exceptional software engineering skills (Python, deep learning frameworks, testing, profiling, refactoring, reproducibility) • Expertise with modern ML training stacks: PyTorch, JAX or equivalent; experience implementing model architectures from scratch and working within libraries like DeepSpeed, Megatron or equivalent • Ability to tune, debug, and profile large-scale training runs • Hands-on experience working with large GPU clusters, including job orchestration, scheduling, multi-node runs, NCCL/RDMA issues, and GPU performance optimization • Ability to collaborate effectively with both research-oriented and engineering-oriented colleagues; comfortable turning research ideas into robust, maintainable implementations • Good engineering hygiene: modular design, code reviews, documentation, reproducibility, versioning of data/models/configurations • Familiarity with common tools (Linux command line, git, Docker, …) • Openness to adopting new tooling • Solid understanding of distributed systems and networking • Strong written English • Open, honest and transparent communication skills.

🏖️ Benefits

• Flexible working hours • Professional development opportunities