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Machine Learning Trainer Jobs in Washington (NOW HIRING)

Ametek

Machine Learning Engineer

Herndon, VA · Hybrid

We are seeking an earlycareer Machine Learning Engineer who is excited to grow rapidly by building ... Develop and maintain feature engineering pipelines, data preprocessing flows, and training ...

Kitware

Machine Learning Engineer

Arlington, VA · On-site

$110K - $160K/yr

Strong background in both classical and modern (deep learning) machine learning, including model selection, architecting, training, validation, testing, and deployment * Machine learning experience ...

Heven AeroTech

Machine Learning Engineer

Sterling, VA · On-site

Machine Learning Engineer Company: Heven AeroTech Location: Sterling, Virginia FLSA: Exempt About ... That's why we invest in your professional growth, offering robust training programs, leadership ...

Ametek

Machine Learning Engineer

Herndon, VA · On-site

We are seeking an early-career Machine Learning Engineer who is excited to grow rapidly by building ... Develop and maintain feature engineering pipelines, data preprocessing flows, and training ...

NT Concepts

Machine Learning Engineer

Herndon, VA · On-site +1

Practical experience training and deploying Machine Learning models. Ideal candidate would have experience with PyTorch, NumPy, TensorFlow, VS Code) * Understanding of machine learning techniques and ...

NT Concepts

Machine Learning Engineer

Chantilly, VA · On-site +1

Practical experience training and deploying Machine Learning models. Ideal candidate would have experience with PyTorch, PyTorch3D, NumPy, TensorFlow) * Understanding of machine learning techniques ...

NT Concepts

Machine Learning Engineer

Chantilly, VA · On-site

Practical experience training and deploying Machine Learning models. Ideal candidate would have experience with PyTorch, PyTorch3D, NumPy, TensorFlow) * Understanding of machine learning techniques ...

NT Concepts

Machine Learning Engineer

Herndon, VA · On-site

Practical experience training and deploying Machine Learning models. Ideal candidate would have experience with PyTorch, NumPy, TensorFlow, VS Code) * Understanding of machine learning techniques and ...

Varsity Tutors

Machine Learning Tutor

Laurel, MD · Remote

$40/hr

Skilled at breaking down model training pipelines, hyperparameter tuning, and evaluation metric ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Rockville, MD · Remote

$40/hr

Skilled at breaking down model training pipelines, hyperparameter tuning, and evaluation metric ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Fairfax, VA · Remote

$40/hr

Skilled at breaking down model training pipelines, hyperparameter tuning, and evaluation metric ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Leesburg, VA · Remote

$40/hr

Skilled at breaking down model training pipelines, hyperparameter tuning, and evaluation metric ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Bowie, MD · Remote

$40/hr

Skilled at breaking down model training pipelines, hyperparameter tuning, and evaluation metric ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Alexandria, VA · Remote

$40/hr

Skilled at breaking down model training pipelines, hyperparameter tuning, and evaluation metric ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Washington, DC · Remote

$40/hr

Skilled at breaking down model training pipelines, hyperparameter tuning, and evaluation metric ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

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How much do machine learning trainer jobs pay per year?

As of May 30, 2026, the average yearly pay for machine learning trainer in Washington is $98,903.00, according to ZipRecruiter salary data. Most workers in this role earn between $68,000.00 and $125,700.00 per year, depending on experience, location, and employer.

What is a Machine Learning Trainer job?

A Machine Learning Trainer is responsible for preparing and curating datasets, fine-tuning machine learning models, and optimizing algorithms for accuracy and efficiency. They work closely with data scientists and engineers to improve model performance and ensure high-quality training data. This role involves tasks like labeling data, selecting features, and implementing preprocessing techniques. Additionally, they may develop training methodologies and evaluate models using various metrics to enhance their effectiveness.

What are the key skills and qualifications needed to thrive in the Machine Learning Trainer position, and why are they important?

To thrive as a Machine Learning Trainer, you need a solid background in computer science, statistics, and machine learning concepts, often supported by relevant academic degrees and industry experience. Familiarity with programming languages like Python, frameworks such as TensorFlow or PyTorch, and certifications like TensorFlow Developer or AWS Machine Learning are valuable assets. Excellent communication, patience, and adaptability allow trainers to effectively convey complex concepts to diverse learners. These skills ensure effective teaching, learner engagement, and successful knowledge transfer in a rapidly evolving technological landscape.

What are the typical challenges faced by a Machine Learning Trainer in the workplace?

Machine Learning Trainers often encounter the challenge of explaining complex algorithms and abstract mathematical concepts to learners with varying levels of expertise. Adapting course materials to suit different learning styles and staying current with the latest advancements in machine learning require continuous self-development. Trainers may also need to collaborate closely with data scientists, engineers, and curriculum developers to ensure their training aligns with real-world applications. Overcoming these challenges not only enhances teaching effectiveness but also contributes to the overall growth of both trainers and their learners.
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Machine Learning Trainer jobs near you
Machine Learning Engineer

Machine Learning Engineer

Ametek

Herndon, VA • Hybrid

Other

Posted 23 days ago

AMETEK rating

7.9

Company rating: 7.9 out of 10

Based on 43 frontline employees who took The Breakroom Quiz

51st of 137 rated electronics manufacturers

Job description

We are seeking an earlycareer Machine Learning Engineer who is excited to grow rapidly by building and deploying productiongrade ML systems. The ideal candidate has a strong engineering mindset, has contributed to shipping ML features or products endtoend, and is eager to take ownership across the full lifecycle-from data pipelines to model design to deployment, monitoring, and iteration in realworld environments.

This role offers handson exposure to applied ML, working with IoT datasets, user needs, and product requirements to build scalable solutions that deliver measurable customer ROI.

Responsibilities:

  • Design, build, and deploy ML models into production environments, ensuring reliability, scalability, and performance.
  • Ability to select and apply the appropriate ML approach for a given problem - including supervised learning (e.g., logistic regression, random forest, gradient boosting), unsupervised learning (e.g., clustering, dimensionality reduction), and deep learning techniques when appropriate.
  • Develop and maintain feature engineering pipelines, data preprocessing flows, and training workflows.
  • Collaborate with crossfunctional partners including product, data engineering, DevOps & QA to deliver endtoend ML solutions.
  • Work with DevOps team to implement robust MLOps practices, including versioning, CI/CD for ML, monitoring/alerting, automated retraining, and model governance.
  • Continuously evaluate and improve models by monitoring performance, identifying and addressing bias, detecting data or concept drift, and iterating on features, algorithms, or training processes to maintain reliability over time.
  • Ensure solutions meet security, compliance, and data privacy standards.
  • Document system architectures, modeling decisions, and operational procedures.
  • Work in a high performing scrum team to deliver quality code for stakeholders.

Qualifications - Must Have Skills:

  • 3+ years of professional experience as an ML Engineer, Applied Scientist, or Data Scientist with an emphasis on handson software engineering responsibilities, particularly around productionizing models.
  • Demonstrated contributions to shipping ML models into production-not just prototypes-and supporting their maintenance over time.
  • Proficiency in Python and ML frameworks such as PyTorch and Scikitlearn.
  • Prior hands-on experience with cloud platforms (AWS, Azure, GCP) and ML services (e.g., SageMaker, Vertex AI, Azure ML).
  • Familiarity with GenAI system components and architecture, including vector databases, LLM finetuning, embeddings pipelines, and retrievalaugmented systems (RAG).
  • Experience with MLOps tooling: Docker, Kubernetes, MLflow, Feature Stores, CI/CD pipelines is preferred.
  • Strong understanding of data structures, algorithms, software engineering fundamentals, and distributed systems concepts.
  • Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Engineering, Mathematics, or a closely related quantitative field.
  • This is a hybrid role in Herndon, VA and no relocation assistance is able to be provided.

Other Beneficial Skills:

  • Familiarity with emerging Agentic AI concepts.
  • Familiarity with Edge ML patterns.
  • Experience working with large-scale data pipelines using Spark, Flink, Beam, or similar frameworks.
  • Experience or demonstrated interest in Vision ML, with familiarity in common vision models and techniques for image classification, object detection, and segmentation.
  • Knowledge of observability and monitoring tools for ML systems (Prometheus, Grafana, etc.)
  • Experience with cloud infrastructure and managing resources in the cloud.
  • Master's degree in a relevant field may be considered equivalent to up to 2 years of professional ML engineering experience, particularly when supported by handson coursework, research, internships, or realworld projects involving applied machine learning.

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