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Salaried Deep Learning Jobs in Washington (NOW HIRING)

Staff Deep Learning Engineer

Columbia, MD · On-site

$185K - $235K/yr

Overview We are seeking a Staff Deep Learning Research Engineer to design, build, and train novel ... Salary Range: $185,000 - $235,000. * Annual bonus and equity as appropriate. Benefits: * Health ...

Staff Deep Learning Engineer

Columbia, MD · On-site

$185K - $235K/yr

Overview We are seeking a Staff Deep Learning Research Engineer to design, build, and train novel ... Salary Range: $185,000 - $235,000. * Annual bonus and equity as appropriate. Benefits: * Health ...

Engineer, Machine Learning

Arlington, VA · On-site

$157K - $185K/yr

This includes building pipelines for training and deploying deep learning and other machine ... Salary Range $157,000-$185,000 Venture Global LNG is an Equal Opportunity Employer. We do not ...

SIMILAR CAREER TITLES Data Scientist, AI Engineer, Deep Learning Engineer, Artificial Intelligence ... Excellent Salaries * Flexible Work Schedule * Cafeteria Style Benefits * 10% - 401k Matching ...

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Salaried Deep Learning information

What is the difference between Salaried Deep Learning vs Salaried Machine Learning Engineer?

AspectSalaried Deep LearningSalaried Machine Learning Engineer
Required CredentialsMaster's or PhD in Computer Science, AI, or related fields; experience with neural networksMaster's in CS, Data Science, or related; strong programming and statistical skills
Work EnvironmentResearch labs, AI startups, tech companies focusing on neural network modelsTech companies, data-driven firms, AI product development teams
Employer & Industry UsagePrimarily in AI research, academia, and companies developing deep learning modelsAcross industries like finance, healthcare, and e-commerce implementing ML solutions

While both roles involve machine learning, Salaried Deep Learning specialists focus on neural network architectures and AI research, whereas Salaried Machine Learning Engineers work on broader ML applications and deployment across various industries.

What are the most commonly searched types of Deep Learning jobs in Washington?

The most popular types of Deep Learning jobs in Washington are:

What are popular job titles related to Salaried Deep Learning jobs in Washington?

For Salaried Deep Learning jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Salaried Deep Learning jobs in Washington look for?

The top searched job categories for Salaried Deep Learning jobs in Washington are:

What cities in Washington are hiring for Salaried Deep Learning jobs?

Cities in Washington with the most Salaried Deep Learning job openings:

Infographic showing various Salaried Deep Learning job openings in Washington as of August 2026, with employment types broken down into 50% Full Time, and 50% Part Time. Highlights an 100% In-person job distribution.

Staff Deep Learning Engineer

Columbia, MD • On-site

Quidient
Software Development • 11 - 50 employees

$185K - $235K/yr

Full-time

Medical, Life, Retirement, PTO

Re-posted 8 days ago


Job description

Overview

We are seeking a Staff Deep Learning Research Engineer to design, build, and train novel neural network architectures that solve hard problems across Quidient's GSR platform. This is not an applied-ML role - you will work from foundational principles to create new networks from scratch, implement cutting-edge papers, and run end-to-end experiments across domains including geometric anomaly detection, neural rendering, and 3D reconstruction quality.

This is a hybrid position, meaning that you will need to live within easy driving distance to our Technology Center in Columbia, Maryland.

What You'll DoResearch & Network Design
  • Design and train novel deep neural network architectures from scratch for a variety of reconstruction tasks - including surface anomaly detection (e.g., dent detection), geometry-based defect identification, and neural rendering improvements.
  • Implement state-of-the-art papers and adapt published architectures to Quidient's specific reconstruction challenges, exercising deep judgment about what will translate from benchmark to production.
  • Identify technical gaps in the current reconstruction pipeline, propose neural network-based solutions, and build the roadmap for how deep learning capabilities evolve across the platform.
  • Design and maintain rigorous evaluation pipelines grounded in real-world captures to measure model performance, regression, and generalization.
Model Development
  • Run end-to-end experiments independently - from hypothesis through data preparation, training, evaluation, and iteration - with minimal supervision.
  • Stay current with the latest advances in deep neural network architectures, training techniques, and optimization methods, continuously bringing relevant ideas into the pipeline.
  • Contribute production-quality C++ and Python to integrate trained models into the reconstruction engine.
  • Bridge deep learning methods with the geometric and physical foundations of the reconstruction platform, applying domain expertise in one or more of: light transport, 3D reconstruction, or SLAM
  • Drive inference optimization and GPU/CUDA performance work toward real-time and on-device targets.
What You BringMust-Have Qualifications:
  • Master's or PhD in Computer Science, Electrical Engineering, Machine Learning, or a related field. A graduate-level foundation in deep learning theory is required, not just applied experience.
  • 6+ years of experience in deep learning research and engineering, with demonstrated ability to design, train, and evaluate novel neural network architectures from scratch.
  • Deep domain expertise in at least one of: light transport, deep learning for 3D vision, or SLAM.
  • Ability to read, critically evaluate, and implement current deep learning papers (CVPR, NeurIPS, ICLR, ICML) and translate them into working systems.
  • Strong software engineering in C++ and Python, with deep proficiency in PyTorch or equivalent frameworks for model development and training.
  • Willingness to work on-site in Columbia, MD, in a hybrid capacity.
  • Meet Quidient, customer, and government security requirements, which may include, but are not limited to a background check, citizenship verification, and Criminal Justice Information Services verification
Nice-to-Have Qualifications:
  • Experience in fast-paced or startup environments.
  • Publications or open-source contributions in deep learning, neural rendering, 3D reconstruction, or computer vision (CVPR, NeurIPS, ICLR, ICML, SIGGRAPH, or similar).
  • Experience designing evaluation pipelines and experiment infrastructure for deep learning research.
  • Hands on with geometric or physics-informed neural networks, or anomaly detection in 3D data.
  • Track record of taking a research idea from paper to production-deployed model.
What We OfferCompensation:
  • Salary Range: $185,000 - $235,000.
  • Annual bonus and equity as appropriate.
Benefits:
  • Health insurance
  • HSA
  • 401(k) with company match
  • Life & disability insurance
  • Paid holidays & generous PTO
  • Opportunities for bonuses, equity, and career growth
Equal Opportunity Employer Statement

Quidient is an Equal Opportunity Employer. Quidient will consider all qualified applicants without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability, or any other classification protected by applicable state, federal, or local laws.