Pre / Post-Sales Machine Learning Engineer

About Oumi

Why we exist :

Oumi is on a mission to make frontier AI truly open for all. We are founded on the belief that AI will have a transformative impact on humanity, and that developing it collectively, in the open, is the best path forward to ensure that it is done efficiently and safely.

What we do :

Oumi provides an all-in-one platform to build state-of-the-art AI models, end to end, from data preparation to production deployment, empowering innovators to build cutting-edge models at any scale. Oumi also develops open foundation models in collaboration with academic collaborators and the open community.

Our Approach :

Oumi is fundamentally an open-source first company, with open-collaboration across the community as a core principle. Our work is :

Research-driven : We conduct and publish original research in AI, collaborating with our community of academic research labs and collaborators.

Community-powered : We believe in the power of open-collaboration and welcome contributions from researchers and developers worldwide.

Role Overview

We’re looking for our first Pre / Post-Sales Machine Learning Engineer who bridges the gap between technical expertise and customer success. You’ll work closely with our customers to design, train, and deploy AI models using Oumi’s platform. This role blends solution architecture, applied machine learning, and customer interaction - ideal for someone who loves both building systems and helping others use them effectively.

You’ll play an important role in shaping not only the direction of the team but also the product. You will collaborate closely with research, product, and engineering teams to ensure customers have a world-class experience using Oumi, while channeling feedback to improve our platform.

You will :

Drive success with customers : Partner with customers through the pre-sales and post-sales lifecycle - from technical demos and proof-of-concept design to hands-on training and deploying models.

Create custom models : Train, fine-tune, and evaluate modern LLMs and other AI models using Oumi’s platform.

Design AI solution architectures : Work with customers to design scalable ML workflows using best practices in data preparation, training, and deployment.

Be a technical advocate : Help customers understand Oumi’s capabilities, providing technical insight and translating research innovations into practical solutions.

Drive product improvements : Work closely with the product and research teams to surface customer needs and help shape the future of Oumi’s platform.

Educate & Enable : Develop technical guides, demos, and best practices to help customers and the open community succeed with Oumi.

What You’ll Bring :

Experience : You have 4+ years of experience in applied ML, ML infrastructure, or solution / sales engineering roles.

ML Expertise : You bring a solid understanding of machine learning workflows, with experience training or fine-tuning modern foundation models (e.g., LLMs, diffusion models, multimodal systems).

Development Skills : You are proficient in Python with a strong understanding of AI / LLM best practices including practical experience with model training frameworks and using LLMs in production environments for modern use cases (e.g. RAG, agents, or agentic systems).

Customer-Facing Strength : You have strong experience communicating complex technical concepts clearly and empathetically to both technical and non-technical audiences.

Learners Mindset : You can proactively address gaps in your understanding of concepts and are willing to pick up whatever knowledge you're missing to get the job done.

Operational Excellence : You can operate in and manage ambiguity while owning problems end-to-end.

Values : You embody Oumi's core values : Beneficial for All, Customer-Ossessed, Radical Ownership, Exceptional Teammates, Science-Grounded.

Benefits :

Equity in a high-growth startup

Comprehensive health, dental, and vision insurance

21 days PTO

Regular team offsites and events

Location :

Remote or Hybrid work environment with offices in :

Seattle, WA

Palo Alto, CA

New York, NY