Applied Deep Learning Engineer

Ndea; Seattle (WA); Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Equity

Competitive compensation

Remote-friendly team

Job summary

Ndea is seeking an experienced deep learning engineer in Seattle to build the System 1 side of our frontier AI architecture. You will take problems from training data through to production, choose approaches, train and evaluate models, and deploy them to real users.

Remote work is ok, with a strong emphasis on delivering measurable results.

Qualifications

• 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 real users.
• 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 underspecified problem, determine the right path forward, and drive it to a measurable result.

Responsibilities

• Take problems from training data to working production systems.
• Choose the right approach, train and evaluate models, build the surrounding system, and deploy it to real users.
• Help determine what intuition guidance should look like as our architecture evolves.
• This is a high-ownership role combining deep ML expertise with a builder's mindset.

Skills

End-to-end ML training

Production ML systems

ML lifecycle

Deep learning advances

First-principles evaluation

High initiative

Startup experience

Job description

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 ourarchitecture 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, nationalorigin, gender, sexual orientation, age, veteran status, disability or any other legally protectedstatus.

• 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 andtechniques
• 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 underspecified problem, determine the right path forward, and drive itto a measurable result

Nice-to-have:

• 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.)