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Software Engineer Ai Model Training Jobs in Washington

540

AI/ML Engineer

Arlington, VA · On-site

Working with software engineers, data engineers, data scientists, cybersecurity teams, and mission ... Develop automated pipelines for model training, validation, testing, deployment, and monitoring

540

AI/ML Engineer

Arlington, VA · On-site

Working with software engineers, data engineers, data scientists, cybersecurity teams, and mission ... Develop automated pipelines for model training, validation, testing, deployment, and monitoring

540

AI/ML Engineer

Arlington, VA

Working with software engineers, data engineers, data scientists, cybersecurity teams, and mission ... Develop automated pipelines for model training, validation, testing, deployment, and monitoring

Dark Wolf Solutions

Senior AI Software Engineer

Herndon, VA · On-site

$130K - $170K/yr

Dark Wolf is seeking a Senior AI Software Engineer to design, build, and integrate AI/ML solutions ... End-to-end knowledge of model training, dataset curation, and data pipeline design. * Programming ...

540

Senior AI/ML Engineer

Arlington, VA · On-site

$120K - $165K/yr

Advanced software engineering experience using Python and commonly used AI/ML frameworks * Experience architecting automated model training, validation, deployment, and monitoring pipelines

540

Senior AI/ML Engineer

Arlington, VA · On-site

$120K - $165K/yr

Advanced software engineering experience using Python and commonly used AI/ML frameworks * Experience architecting automated model training, validation, deployment, and monitoring pipelines

540

Senior AI/ML Engineer

Arlington, VA

$120K - $165K/yr

Advanced software engineering experience using Python and commonly used AI/ML frameworks * Experience architecting automated model training, validation, deployment, and monitoring pipelines

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Software Engineer Ai Model Training information

What does a software engineer AI model training do?

A Software Engineer specializing in AI Model Training is responsible for designing, developing, and optimizing machine learning models. Their work involves preparing and processing large datasets, selecting appropriate algorithms, implementing training pipelines, and evaluating model performance. They collaborate closely with data scientists and other engineers to ensure that AI models are accurate, efficient, and suitable for deployment in real-world applications. Additionally, they may help maintain infrastructure for model training and contribute to research and development of new AI techniques.

What are the key skills and qualifications needed to thrive as a software engineer AI model training?

To excel as a Software Engineer in AI Model Training, you need strong programming skills (especially in Python), a solid grasp of machine learning fundamentals, and typically a degree in computer science or a related field. Experience with frameworks like TensorFlow or PyTorch, familiarity with data processing tools, and sometimes certifications in AI or ML are highly valuable. Analytical thinking, problem-solving, and effective collaboration enhance your ability to develop and refine complex AI models. These skills ensure that AI solutions are robust, scalable, and aligned with organizational goals in a rapidly evolving technological landscape.

What are some common challenges faced by software engineers AI model training, and how can they be addressed?

Software Engineers focusing on AI model training often encounter challenges such as managing large datasets, ensuring data quality, and optimizing model performance. Addressing these issues typically involves close collaboration with data scientists, domain experts, and DevOps engineers to streamline the data pipeline and refine training processes. Staying up to date with the latest advancements in machine learning frameworks and tools can also help overcome technical hurdles. Regular code reviews and cross-functional meetings further support problem-solving and foster a productive work environment.

What is the difference between Software Engineer Ai Model Training vs Data Scientist?

AspectSoftware Engineer Ai Model TrainingData Scientist
Required CredentialsBachelor's in CS, related field; experience with ML frameworksBachelor's or higher in CS, statistics, or related field; strong analytical skills
Work EnvironmentDevelopment teams, AI labs, cloud platformsData analysis, research environments, business units
Employer & Industry UsageTech companies, AI startups, research institutionsTech firms, finance, healthcare, consulting

While both roles involve working with data and machine learning, Software Engineer Ai Model Training focuses on developing and optimizing AI models through coding and engineering practices. Data Scientists analyze data, build models, and generate insights. The roles often collaborate but differ in their core responsibilities and skill sets.

Can I get paid to train AI models?

Yes, software engineers and AI specialists can be paid to train AI models, especially in roles that involve developing, fine-tuning, and optimizing machine learning algorithms. These positions often require knowledge of programming languages like Python, experience with machine learning frameworks, and access to computational resources. Compensation varies based on experience, location, and the complexity of the models being trained.

How to become a software engineer AI model trainer?

To become a software engineer AI model trainer, you should have a strong background in computer science, programming skills in languages like Python, and experience with machine learning frameworks such as TensorFlow or PyTorch. Gaining knowledge in data preprocessing, model evaluation, and working with large datasets is essential, along with relevant certifications or advanced degrees in AI or related fields.

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Cities in Washington with the most Software Engineer Ai Model Training job openings:

Software Engineer Ai Model Training jobs near you

Full Stack Software Engineer (AI Infrastructure)

Laurel, MD • On-site

Bytoa
See more jobs at Bytoaexternal

$200K - $220K/yr

Other

Posted 22 days ago

Job description

Full Stack Software Engineer (AI Infrastructure)
  • Laurel, MD
Description:

Bytoa is seeking a Full-Stack Software Engineer to support our AI infrastructure team. In this role, you’ll help build and maintain the platform that provides the foundation for the customer’s AI capabilities, focusing on inference services while supporting the broader ecosystem of AI-enabled applications. This role is intended for experienced engineers who can independently design, implement, and operate scalable AI infrastructure components.

Salary range:$200,000-$220,000 Disclaimer: Salary for this position, along with additional compensation options, will be determined on an individual basis following the interview process, considering various factors such as years of experience, skills, education/certifications, contract specifications, market conditions, etc.

Responsibilities:
  • Design, implement, and optimize infrastructure for AI model inference at scale.
  • Support the development and maintenance of production AI services and applications, including retrieval augmented generation (RAG), autonomous agents, and emerging technologies.
  • Navigate ambiguity and define solutions for underspecified systems and requirements.
  • Drive adoption of new technologies and practices across engineering teams.
  • Implement monitoring, logging, and observability solutions for AI services.
  • Automate infrastructure provisioning and configuration using IaC principles.
  • Ensure high availability, reliability, and performance of AI platform components.
  • Contribute to security best practices for AI systems and data.
  • Provide technical guidance and informal mentorship to junior engineers.
Skills Requirements:
  • Proven experience building and maintaining production systems at scale.
  • Experience with high-volume web application architecture and performance optimization.
  • Strong background in systems integration across diverse technologies and platforms.
  • Hands-on experience with cloud engineering in AWS.
  • Proficiency with Kubernetes administration and deployment patterns.
  • Experience implementing observability solutions (APM, OpenTelemetry, Grafana, Prometheus).
  • Familiarity with CI/CD pipelines and DevOps practices.
  • Strong change management and organizational influence skills.
  • Ability to thrive in ambiguous environments and create structure where needed.
  • Excellent communication and collaboration skills.
Nice to Haves:
  • Experience with AI inference serving technologies (vLLM, LiteLLM, etc.).
  • Previous experience with agentic frameworks (LangChain).
  • Knowledge of vector databases and embedding systems.
  • Experience with high-performance computing or distributed systems.
Experience Requirement:

8 yrs., B.S. in a technical discipline or 4 additional yrs. in place of B.S.

Clearance Requirement:

Active TS/SCI with a polygraph

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