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Ai Model Trainer Jobs in Reston, VA (NOW HIRING)

Goldbelt, Inc.

AI/ML SME

Arlington, VA

$190K - $235K/yr

Document model assumptions, data inputs, and training parameters for reproducibility. * Conduct ... Prepare AI business case summaries that explain model purposes, value proposition, expected ...

DCCA

SME - Data Engineer

Bethesda, MD · On-site

$122K - $146K/yr

They are seeking a Senior Data Engineer to design and implement large-scale data systems and prepare datasets for AI model training. Responsibilities : • Experience in designing and implementing ...

Goldbelt, Inc.

AI/ML SME

Arlington, VA · On-site

$190K - $235K/yr

Document model assumptions, data inputs, and training parameters for reproducibility. * Conduct ... Prepare AI business case summaries that explain model purposes, value proposition, expected ...

Cp Marine Llc

AI/ML SME

Arlington, VA · On-site

$190K - $235K/yr

Document model assumptions, data inputs, and training parameters for reproducibility. * Conduct ... Prepare AI business case summaries that explain model purposes, value proposition, expected ...

Cp Marine Llc

AI/ML SME

Arlington, VA

$190K - $235K/yr

Document model assumptions, data inputs, and training parameters for reproducibility. * Conduct ... Prepare AI business case summaries that explain model purposes, value proposition, expected ...

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Ai Model Trainer information

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How much do ai model trainer jobs pay per hour?

As of Jul 30, 2026, the average hourly pay for ai model trainer in Reston, VA is $26.79, according to ZipRecruiter salary data. Most workers in this role earn between $20.05 and $31.06 per hour, depending on experience, location, and employer.

What does an AI Model Trainer do?

An AI Model Trainer is responsible for developing, training, and refining artificial intelligence models using large datasets. They preprocess data, select appropriate algorithms, monitor model performance, and tune parameters to improve accuracy and efficiency. Model trainers often collaborate with data scientists, engineers, and domain experts to ensure the AI meets specific requirements and delivers reliable results. Their work is essential for creating applications like image recognition, natural language processing, and predictive analytics.

What are some typical challenges faced by AI Model Trainers when working with large datasets?

AI Model Trainers frequently encounter challenges such as ensuring data quality, handling imbalanced datasets, and maintaining data privacy. Working with large datasets often requires careful preprocessing and cleaning to avoid biases and inaccuracies in the model. Additionally, trainers must collaborate closely with data engineers, domain experts, and software developers to validate data sources and to fine-tune models for optimal performance. Addressing these challenges is critical for producing reliable, high-performing AI systems.

What are the key skills and qualifications needed to thrive as an AI Model Trainer, and why are they important?

To thrive as an AI Model Trainer, you need strong expertise in machine learning, data analysis, and a background in computer science or a related field, often supported by an advanced degree. Proficiency with programming languages like Python, frameworks such as TensorFlow or PyTorch, and familiarity with data labeling and annotation tools are essential. Attention to detail, critical thinking, and effective communication help in accurately training and refining AI models while collaborating with diverse teams. These skills ensure that AI systems are trained accurately and efficiently, leading to reliable and ethical AI solutions.

What is the difference between Ai Model Trainer vs Data Scientist?

AspectAi Model TrainerData Scientist
Required CredentialsBachelor's in CS, ML, or related; experience with ML frameworksBachelor's or higher in CS, Statistics, or related; advanced analytics skills
Work EnvironmentHands-on model training, data preprocessing, tuningData analysis, modeling, visualization, reporting
Employer & Industry UsageTech companies, AI startups, research labsTech, finance, healthcare, consulting firms
Search & Comparison IntentUnderstanding roles in AI developmentAnalyzing data to inform decisions

While both roles involve working with data and machine learning, an Ai Model Trainer primarily focuses on training and fine-tuning AI models, whereas a Data Scientist emphasizes analyzing data, building models, and deriving insights. The roles often overlap but serve different stages of AI and data projects.

What are popular job titles related to Ai Model Trainer jobs in Reston, VA? For Ai Model Trainer jobs in Reston, VA, the most frequently searched job titles are:
What job categories do people searching Ai Model Trainer jobs in Reston, VA look for? The top searched job categories for Ai Model Trainer jobs in Reston, VA are:
What cities near Reston, VA are hiring for Ai Model Trainer jobs? Cities near Reston, VA with the most Ai Model Trainer job openings:
Ai Model Trainer jobs near you

Product Manager, AI/ML & Foundation Models (R4991)

Shield AI

Washington, DC • On-site

$190K - $290K/yr

Full-time

Re-posted 4 days ago

Job description

Founded in 2015, Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software and V-BAT and X-BAT aircraft. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube. 

Job Description:
 
The Product Manager will drive the strategy and execution of Shield AI’s next-generation autonomy intelligence stack—enabling customers and internal teams to train, evaluate, and deploy foundation and domain models that power resilient autonomy at the edge. This PM owns the product vision and roadmap for the Hivemind AI Platform (Forge, training pipelines, data infrastructure, evaluation, and deployment toolchains), ensuring we can manufacture, govern, and field advanced world models, robotics foundation models, and vision-language-action systems safely and at scale. 
 
This role sits at the intersection of AI/ML, autonomy, model lifecycle, infrastructure, and product strategy. The PM partners closely with engineering, AI research, Hivemind Solutions, and field teams to deliver the tooling that enables sovereign autonomy, AI Factories at the edge, and continuous learning—capabilities that are central to Shield AI’s strategic direction. 
 
This is a high-impact role for an experienced product leader excited to define how foundation models are trained, validated, governed, and deployed across thousands of autonomous systems in highly contested environments.
What you'll do:
  • AI Model Development & Training Platform
  • Own the roadmap for foundation model training workflows, including dataset ingestion, curation, labeling, synthetic data generation, domain model training, and distillation pipelines.
  • Define requirements for world models, robotics models, and VLA-based training, evaluation, and specialization.
  • Lead the evolution of MLOps capabilities in Forge, including data lineage, experiment tracking, model versioning, and scalable evaluation suites.
  • Data, Simulation & Synthetic Data Factory
  • Define product requirements for synthetic data generation, simulation-integrated data flywheels, and automated scenario generation.
  • Partner with Digital Twin, Simulation, and autonomy teams to convert natural-language mission inputs into data needs, training procedures, and model variants.
  • Safe Deployment & Model Governance
  • Lead the development of model governance and auditability tooling, including model cards, dataset rights, lineage tracking, safety gates, and compliance evidence.
  • Build guardrails and workflows to safely deploy models onto edge hardware in disconnected, GPS- or comms-denied environments.
  • Partner with Safety, Certification, Cyber, and Engineering teams to ensure traceability and evaluation pipelines meet operational and accreditation requirements.
  • Edge Deployment & AI Factory Integration
  • Partner with Pilot, EdgeOS, and hardware teams to integrate foundation-model-based perception and reasoning into autonomy behaviors.
  • Define requirements for distillation, quantization, and inference tooling as part of the “three-computer” development and deployment model.
  • Ensure closed-loop workflows between cloud model training and edge-native execution.
  • Cross-Functional Leadership
  • Collaborate with Engineering, Research, Product, Customer Engagement, and Solutions teams to ensure model outputs meet mission and platform constraints.
  • Translate advanced AI capabilities into intuitive workflows that platform OEMs and partner nations can use to build sovereign AI factories.
  • Sequence foundational capabilities that unblock autonomy, simulation, and customer-facing product teams.
  • User & Customer Impact
  • Develop deep empathy for ML engineers, autonomy developers, and Solutions engineers who rely on the platform.
  • Capture operational data gaps, mission-driven model needs, and domain-specific specialization requirements.
  • Lead demos and onboarding for model-development capabilities across internal and external teams.
Required qualifications:
  • 7+ years of experience in product management or highly technical ML/AI product roles.
  • 2+ years of experience in a hands-on software development role.
  • Strong engineering background (Computer Science, Electrical Engineering, Robotics, or related field).
  • Deep understanding of foundation models, robotics models, multimodal models, MLOps, and training infrastructure.
  • Experience managing complex products spanning data pipelines, cloud training clusters, model governance, and edge deployments.
  • Proven success partnering with research teams to transition ML innovations into stable, production-grade workflows.
  • Familiarity with simulation-based data generation and large-scale data management.
  • Excellent communicator with strong cross-functional leadership skills.
Preferred qualifications:
  • Experience working on autonomy, robotics, embedded AI, or mission-critical systems.
  • Hands-on familiarity with GPU infrastructure, distributed training, or data lakehouse architectures.
  • Experience supporting defense, dual-use, or safety-critical AI systems.
  • Background designing or operating AI Factory–style pipelines (data → training → evaluation → distillation → edge deployment).
  • Advanced degree in engineering, ML/AI, robotics, or a related field.
#LI-DM2
#LE

Full-time regular employee offer package:
Pay within range listed + Bonus + Benefits + Equity
 
Temporary employee offer package:
Pay within range listed above + temporary benefits package (applicable after 60 days of employment)
 
Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.
 
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Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know. 

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.