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Full Time Neural Networks Jobs (NOW HIRING)

Designworks Talent

Staff Applied Scientist

Seattle, WA · Remote

$220K - $275K/yr

Seattle, WA (On-site downtown) - Hybrid Type: Full-Time Industry: Software (AI) Specialty ... Large language models (LLMs), agentic systems, reinforcement learning, and/or graph neural networks.

Sarian, Inc.

Sr. Data Scientist

Irving, TX · On-site

Irving, TX - Fulltime Position Data Scientist with Generative AI Expertise * Programming languages ... Strong understanding of supervised and unsupervised learning, neural networks, transformers

Scripps College

Post-Doctoral Researcher

Claremont, CA · On-site

$70.30K/yr

... networks. * Develop, test, and refine original theories, methodologies, neural network topologies ... This is a temporary status, full-time, benefits eligible, exempt position. The position is expected ...

Scripps College

Post-Doctoral Researcher

Claremont, CA · Hybrid

$70.30K/yr

... networks. * Develop, test, and refine original theories, methodologies, neural network topologies ... This is a temporary status, full-time, benefits eligible, exempt position. The position is expected ...

Sonsoft Inc

C# / Machine Learning

Redmond, WA

Machine Learning, Deep Learning/neural networks. * Data mining. * Azure ML, Cortana Intelligence ... This is a Full-Time & Permanent job opportunity for you. 2.Only US Citizen, Green Card Holder, GC ...

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Full Time Neural Networks information

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$38.5K

$103.9K

$179K

How much do full time neural networks jobs pay per year?

As of May 31, 2026, the average yearly pay for full time neural networks in the United States is $103,920.00, according to ZipRecruiter salary data. Most workers in this role earn between $75,000.00 and $132,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Neural Networks Engineer, and why are they important?

To thrive as a Neural Networks Engineer, you need a strong background in mathematics, deep learning theory, programming (especially Python), and a relevant degree such as computer science or engineering. Proficiency with frameworks like TensorFlow or PyTorch, as well as experience with cloud platforms and version control systems, is typically expected. Analytical thinking, creativity in problem-solving, and effective communication help distinguish top performers in this field. These skills are vital for designing, implementing, and optimizing neural network models that solve complex real-world problems efficiently.

What are the typical challenges faced by professionals working full-time with neural networks, and how can they be managed?

Professionals working full-time with neural networks often encounter challenges such as handling large and complex datasets, ensuring model generalization, and optimizing computational resources. Staying updated with rapidly evolving frameworks and techniques can also be demanding. Collaborating closely with data engineers, software developers, and domain experts helps in addressing these challenges effectively and ensures the successful deployment and scalability of neural network models.

What are full-time neural network jobs?

Full-time neural network jobs are positions where professionals work primarily with artificial neural networks and deep learning models. These roles typically involve developing, training, and deploying neural network architectures for tasks such as image recognition, natural language processing, and predictive analytics. Professionals in these jobs often have backgrounds in computer science, mathematics, or engineering, and work in industries like tech, healthcare, finance, and automotive. The work is usually project-based and may require collaboration with data scientists, engineers, and product managers.

What is the difference between Full Time Neural Networks vs Data Scientist?

AspectFull Time Neural NetworksData Scientist
Required CredentialsDegree in Computer Science, AI, or related fields; knowledge of neural network frameworksDegree in Statistics, Computer Science, or related fields; programming skills
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness environments, analytics teams, consulting firms
Industry UsageAI development, machine learning projects, deep learning researchData analysis, predictive modeling, business insights

Full Time Neural Networks specialists focus on designing and implementing neural network models for AI applications, often within research or development teams. Data Scientists analyze data to extract insights and build predictive models, sometimes utilizing neural networks. While both roles require programming skills and a background in data or AI, their primary focus and work environments differ. Understanding these distinctions helps in choosing the right career path or job search focus.

What cities are hiring for Full Time Neural Networks jobs? Cities with the most Full Time Neural Networks job openings:
What are the most commonly searched types of Neural Networks jobs? The most popular types of Neural Networks jobs are:
What states have the most Full Time Neural Networks jobs? States with the most job openings for Full Time Neural Networks jobs include:
Full Time Neural Networks jobs near you
Staff Applied Scientist

Staff Applied Scientist

Designworks Talent

Seattle, WA • Remote

$220K - $275K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 16 days ago

Job description

Location: Seattle, WA (On-site downtown) - Hybrid
Type: Full-Time
Industry: Software (AI)
Specialty: Identity verification, fraud prevention, background screening, and risk intelligence.

Overview

Our publicly traded client develops proprietary technology and analytics to deliver identity intelligence, powering critical solutions that help organizations operate confidently. Their solutions enable real-time identification and location of people, businesses, assets, and their relationships for risk mitigation, due diligence, fraud prevention, regulatory compliance, and customer acquisition. These solutions support frictionless commerce, enhance safety, reduce fraud, and lower related societal costs.

Key Responsibilities

Our client is looking for a collaborative and forward-thinking Staff Applied Scientist to join their Seattle-based AI team. In this role, your work will focus on developing and deploying generative AI, agentic AI, and deep learning-based solutions.
You’ll partner with a diverse team of scientists, engineers, and product leaders to build scalable solutions that solve meaningful real-world challenges. This is an opportunity for someone who enjoys balancing research, experimentation, and hands-on product impact in a fast-moving environment where curiosity, creativity, and continuous learning are valued.

Qualifications

Education

Ph.D. in Computer Science, Artificial Intelligence, or a related field with a focus on: generative AI, agentic AI, deep learning, reinforcement learning, and/or graph neural networks.

-or-

Equivalent industry experience

Experience:

  • 5-7 years with Applied research in AI.

  • Large language models (LLMs), agentic systems, reinforcement learning, and/or graph neural networks.

  • Successfully shipping AI-powered products (not just publishing or prototyping).


Strengths:

  • Hands-on experience designing and deploying agentic AI systems, including tool usage, function calling, planning, multi-agent orchestration, retrieval-augmented generation, and evaluating agent behavior in production environments.

  • Pre-training and/or fine-tuning foundational large language models.

  • Proficiency in programming languages such as Python or C++, along with expertise in AI frameworks (e.g., PyTorch, TensorFlow, JAX).

  • Deep understanding of transformer architectures, deep learning, and generative AI, with experience in natural language processing (NLP) for generative and agentic AI applications.

  • Experience building and deploying scalable AI/ML solutions in real-world applications, including those at national or global scales.

  • Demonstrated ability to drive projects forward independently with minimal guidance. Ownership of ambiguous problem framing through research, prototyping, productionization, and post-launch iteration.

  • Bonus

    • Familiarity with supervised fine-tuning, RLHF/DPO, LoRA/PEFT, distillation, and large-scale distributed training.

    • AWS certifications related to generative AI.

    • Published research findings at top-tier conferences and journals.

Why You’ll Love This Role

  • Big Data | Research | Innovation | Problem Solving | Experimentation | Productization

  • You thrive in communicative, collaborative, cross-functional environments.

  • Comfortable working at the intersection of research and engineering, able to read papers on Friday and ship a prototype by Monday. Enjoys transforming ambiguous research ideas into reliable production systems.

  • Benefits: stock (RSU) grants, a 401K and generous company match, flexible PTO policy, medical, dental and vision coverage, commuter benefits, in-office healthy snacks, team events

  • Our client is a proud to be an Equal Opportunity Employer.

Compensation Range: $220K - $275K