ZipRecruiter

Log In

1

Generative Ai Training Jobs in Washington (NOW HIRING)

next page

Showing results 1-20

All JobsGenerative Ai Training JobsGenerative Ai Training Jobs in Washington

Generative Ai Training information

What is generative AI training?

Generative AI training refers to the process of teaching artificial intelligence models, such as neural networks, to create new content like text, images, audio, or code. This is done by exposing the AI to large datasets so it can learn underlying patterns and generate outputs that mimic human-like creativity. The training process often involves techniques like supervised learning, unsupervised learning, or reinforcement learning, depending on the desired outcome. Generative AI is widely used in applications like chatbots, image generation, and content creation.

What are the key skills and qualifications needed to thrive in generative AI training?

To thrive in Generative AI Training, you need a strong background in machine learning, data science, and programming (especially Python), often supported by a degree in computer science or a related field. Experience with frameworks like TensorFlow, PyTorch, and familiarity with large language models and cloud platforms is typically required. Strong analytical thinking, creativity, and effective communication are essential soft skills for designing training data and refining model outputs. These skills and qualities are crucial for developing high-quality, ethical, and scalable AI systems that meet organizational goals.

What are some common challenges faced by professionals working in generative AI training roles?

Professionals in Generative AI training often encounter challenges such as ensuring data quality and diversity, combating model bias, and staying updated with fast-evolving algorithms. Collaborating closely with data scientists, engineers, and subject matter experts is essential to create robust training datasets and refine model outputs. Additionally, balancing computational resource demands with project deadlines can be demanding, making strong project management and adaptability key assets in this role.

What is the difference between Generative Ai Training vs Data Scientist?

AspectGenerative Ai TrainingData Scientist
Required CredentialsKnowledge of AI models, programming, machine learningStatistics, programming, data analysis
Work EnvironmentAI development teams, tech companies, research labsBusiness, finance, tech firms, research institutions
Industry UsageDeveloping generative models like GPT, DALL·EData analysis, predictive modeling, insights generation

Generative Ai Training focuses on developing and fine-tuning AI models that generate content, requiring expertise in AI frameworks and machine learning. Data Scientists analyze data to extract insights and build predictive models. While both roles involve programming and data skills, Generative Ai Training is specialized in AI model creation, whereas Data Scientists work broadly with data analysis across industries.

What are popular job titles related to Generative Ai Training jobs in Washington?

For Generative Ai Training jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Generative Ai Training jobs in Washington look for?

The top searched job categories for Generative Ai Training jobs in Washington are:

Generative Ai Training jobs near you
Infographic showing various Generative Ai Training job openings in Washington as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 15% Part Time, and 2% Contract. Highlights an 86% Physical, 1% Hybrid, and 13% Remote job distribution.

Generative AI Engineer Role

OpenDataJobs

Washington, DC • On-site

Full-time

Posted 18 days ago

Job description

The work
Generative AI Engineers build production applications around foundation models and large language models. They turn model capabilities into tools for search, drafting, summarization, information extraction, multimodal work, and guided action, with the controls and evidence needed to understand how those tools behave.
The role concentrates on application and context engineering. Generative AI Engineers connect models to authoritative knowledge, tools, and workflows; design prompts and structured outputs; evaluate quality, grounding, safety, security, latency, and cost; and monitor the application after release. They treat fluent output as something to test, not proof that the system is correct.
What you'll build
• Grounded assistants and knowledge applications with retrieval-augmented generation (RAG), hybrid or vector search, citations, metadata filtering, and source-level access controls.
• Drafting, summarization, classification, extraction, and transformation services exposed through user interfaces or APIs.
• Agents and multistep workflows with defined tool schemas, constrained permissions, approval gates, memory boundaries, replay, and exception handling.
• Evaluation systems with curated test cases, task-specific rubrics, retrieval measures, grounding checks, safety and security tests, and regression thresholds.
• Operational pipelines for versioning prompts and configurations, comparing models, tracing execution, monitoring quality and cost, collecting feedback, and responding to incidents.
Who you are
You are an application engineer who can work with fast-moving model capabilities without chasing every new release. You choose architectures by evidence, make uncertainty visible, and separate a convincing demonstration from a dependable service.
You think in complete workflows: sources, context, models, tools, permissions, people, and failure paths. You collaborate with domain experts, data and software engineers, security and privacy specialists, and accountable owners to decide where generation helps and where deterministic methods or human judgment should remain in control.
What you bring
• A strong application-engineering foundation, including programming, APIs, testing, version control, service integration, and production debugging.
• Practical experience with foundation-model integration, prompt and context design, structured outputs, model selection, and failure analysis.
• Working knowledge of retrieval systems, embeddings, search, knowledge stores, document ingestion, and data-access controls.
• Evaluation discipline across answer quality, retrieval relevance, grounding, safety, security, latency, cost, and user outcomes.
• The judgment to constrain tools and agents, design human-review paths, document limitations, and respond when production behavior changes.
About OPEN Data Jobs
OPEN Data Jobs connects AI, data, and software professionals with critical roles, primarily in the federal sector. Registering with ODJ can put your profile in view for multiple positions across several clients.
Register for Generative AI Engineer Role
Click Apply below to register for Generative AI Engineer Role.
Requirements
What openings may require
An opening may emphasize enterprise search, document intelligence, multimodal applications, code generation, contact-center support, agent workflows, model adaptation, synthetic data, or evaluation and red-team engineering. Building or training a foundation model from scratch is not a universal requirement.
Specific openings may name a model provider, cloud platform, vector or search service, agent framework, observability stack, programming language, model-evaluation approach, content-safety service, or security and governance framework. OPEN Data Jobs will state which capabilities are required and which are preferred.
Benefits
Compensation, benefits, work location, and employment terms are set for each specific opening and will be stated with that opening

Apply