Applied Deep Learning Engineer

Technical Staff - Deep Learning

Location:

Seattle, WA, USA (Remote ok)
Type:
Full-time

Ndea is building frontier AI systems that combine intuitive pattern recognition with program synthesis.

We’re looking for an experienced deep learning engineer to help build the "System 1" (à la Kahneman) sideof that architecture: fast, intuitive systems that complement symbolic reasoning.

You’ll take problems from training data to working production systems - choosing the right approach,training and evaluating models, building the surrounding system, and deploying it to real users. We’relooking for someone who has done this end-to-end before and has strong practical intuition about whatworks.

The larger challenge is open-ended: help us determine what intuition guidance should look like as our architecture evolves.

This is a high-ownership role for someone who combines deep ML expertise with a builder's mindset.

We offer:

• Meaningful equity, competitive salary, and benefits
• Aggressive compute budget
• Small, high-talent-density, globally remote team

Ndea is an equal opportunity employer and does not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or any other legally protected status.

• Deep experience training machine learning models end-to-end
• Experience building and deploying production ML systems for tasks such as prediction,classification, ranking, recommendation, routing, or decision-making
• Experience across the full ML lifecycle: data, training, evaluation, system design,productionization, deployment, and operation
• Experience taking ML systems beyond research prototypes and putting them into the hands of realusers
• Current, technically-grounded understanding of advances in deep learning architectures and techniques
• Ability to evaluate new approaches from first principles: how they work, what they can and can’t do,and whether they actually improve on existing methods
• High agency - you can take an under specified problem, determine the right path forward, and drive itto a measurable result

• Experience operating in an early-stage startup or similarly fast-moving environment
• Experience with non-LLM architectures and earlier generations of deep learning
• Familiarity with symbolic AI (program synthesis, logic, solvers, formal methods, etc.)