AI/ML Engineer (Remote)

Posted 3 Hours Ago
Hiring Remotely in United States
Remote
85K-130K Annually
Entry level
Artificial Intelligence • Hardware • Software • Semiconductor
The Role
Develop, train, evaluate, optimize, and deploy machine learning models and AI applications using Python and PyTorch. Prepare datasets, build REST APIs for model serving, troubleshoot data and API issues, and maintain model versions and documentation. Collaborate with engineering, data science, and product teams while following software development practices such as Git, code reviews, automated testing, and deployment workflows.
Summary Generated by Built In
AI/ML Engineer

Job Title: AI/ML Engineer
Job Type: Full-Time
Work Arrangement: Fully Remote
Experience Level: Entry-Level to Early Career (0–3 Years)
Salary Range: $85,000–$130,000 per year
Target Locations: San Francisco, CA | Seattle, WA | New York City, NY | Boston, MA | Austin, TX
Candidate Location Requirement: Must currently reside in the United States.

About the Role

We are seeking a motivated and technically skilled AI/ML Engineer to join our engineering team. The ideal candidate will have a strong foundation in Python programming, machine learning concepts, PyTorch, and API development.

In this role, you will help design, develop, evaluate, and deploy machine learning models and AI-powered applications. You will collaborate with software engineers, data scientists, and product teams to turn data and machine learning research into reliable, scalable solutions.

This opportunity is suitable for recent graduates and early-career professionals with 0–3 years of experience who are passionate about artificial intelligence, machine learning, and building real-world AI applications.

Location requirement: Applicants must currently reside in the United States. This position is fully remote, subject to the employer's approved U.S. work locations and employment requirements.

Key Responsibilities
  • Develop, train, test, and evaluate machine learning models using Python and PyTorch.

  • Implement machine learning algorithms for classification, regression, prediction, and other relevant use cases.

  • Prepare, clean, preprocess, and analyze datasets for model training and evaluation.

  • Build reusable Python modules and maintain clean, well-documented code.

  • Develop and integrate REST APIs to serve machine learning models and AI-powered features.

  • Evaluate model performance using appropriate metrics and validation techniques.

  • Collaborate with software engineers, data scientists, and product teams to deliver AI solutions.

  • Optimize model performance, inference speed, and resource utilization.

  • Assist with deploying machine learning models into development, testing, and production environments.

  • Troubleshoot model errors, API issues, and data quality problems.

  • Maintain experiment documentation, model versions, and technical reports.

  • Follow software engineering best practices, including Git version control, code reviews, and automated testing.

  • Stay current with emerging AI/ML techniques, frameworks, and development tools.

Required Qualifications
  • Bachelor's or master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field, or equivalent practical experience.

  • 0–3 years of experience in AI/ML engineering, software development, data science, or related projects.

  • Proficiency in Python programming.

  • Working knowledge of PyTorch and its core components, including tensors, neural networks, and model training.

  • Understanding of machine learning fundamentals, including supervised and unsupervised learning.

  • Familiarity with model evaluation, training and validation datasets, and overfitting prevention.

  • Understanding of REST APIs, HTTP, JSON, and API integration.

  • Basic knowledge of data preprocessing, feature engineering, and exploratory data analysis.

  • Familiarity with Git and collaborative software development practices.

  • Strong analytical, mathematical, debugging, and problem-solving skills.

  • Good written and verbal communication skills.

  • Must currently reside in the United States.

Preferred Qualifications
  • Experience with scikit-learn, NumPy, pandas, or similar Python libraries.

  • Familiarity with deep learning architectures, including convolutional neural networks or transformers.

  • Exposure to natural language processing (NLP), computer vision, or generative AI.

  • Familiarity with FastAPI, Flask, or similar frameworks for deploying model-serving APIs.

  • Basic understanding of large language models (LLMs), embeddings, and vector databases.

  • Exposure to cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.

  • Familiarity with Docker, CI/CD pipelines, and automated testing.

  • Understanding of model deployment, monitoring, and machine learning lifecycle management.

  • Academic projects, internships, research experience, or GitHub repositories demonstrating practical AI/ML skills.

Technical Skills

Skill CategoryRequired / Preferred SkillsProgrammingPythonMachine LearningML algorithms, model training, evaluationDeep LearningPyTorch, neural networksAPI DevelopmentREST APIs, HTTP, JSONData Processingpandas, NumPy, data preprocessingML Librariesscikit-learn — preferredDeploymentFastAPI, Flask — preferredCloudAWS, Azure, or Google Cloud — preferredVersion ControlGit, GitHub or GitLabCore CompetenciesAnalytical thinking, debugging, problem-solving

Salary and Benefits
  • Annual base salary: $85,000–$130,000 USD.

  • Full-time employment.

  • Fully remote work arrangement within eligible U.S. locations.

  • Potential benefits may include medical, dental, and vision insurance, retirement plans, paid time off, and professional development opportunities, depending on the employer.

  • Opportunities to work on AI-powered applications, machine learning models, and production-oriented engineering projects.

  • Career development through technical mentorship, collaboration, and continued learning.

The salary range is an indicative target range, not a verified offer. Actual compensation may vary by employer, experience, location, and job responsibilities.

Work Location and Remote Eligibility

This is a fully remote position targeting candidates in the following U.S. markets:

  • San Francisco, California

  • Seattle, Washington

  • New York City, New York

  • Boston, Massachusetts

  • Austin, Texas

Remote work requirements:

  • Candidates must currently reside in the United States.

  • Candidates must meet the employer's applicable work authorization and hiring requirements.

  • Applicants must be able to work remotely from a state approved by the employer.

  • Candidates should be comfortable collaborating with distributed teams during agreed U.S. business hours.

Equal Employment Opportunity

We are committed to providing equal employment opportunities to qualified applicants. Employment decisions are made in accordance with applicable federal, state, and local laws.

How to Apply

Interested candidates should submit an updated résumé highlighting their Python, PyTorch, machine learning, and API development experience.

Applicants are encouraged to include:

  • AI/ML projects, internships, or research experience.

  • GitHub repositories or technical portfolios.

  • Examples of trained machine learning or deep learning models.

  • API development or model deployment projects.

  • Relevant coursework, certifications, or contributions to open-source projects.

Please include your current U.S. city and state in your application to confirm remote work eligibility.

Job Summary

CategoryDetailsJob TitleAI/ML EngineerExperience0–3 yearsEmployment TypeFull-timeWork ArrangementFully remoteSalary Range$85K–$130K annuallyCore SkillsPython, PyTorch, ML, APIsTarget LocationsSF, Seattle, NYC, Boston, AustinCandidate LocationMust currently reside in the U.S.

Skills Required

  • Bachelor’s or master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field, or equivalent practical experience
  • 0-3 years of experience in AI/ML engineering, software development, data science, or related projects
  • Proficiency in Python programming
  • Working knowledge of PyTorch, including tensors, neural networks, and model training
  • Understanding of supervised and unsupervised machine learning fundamentals
  • Familiarity with model evaluation, training and validation datasets, and overfitting prevention
  • Understanding of REST APIs, HTTP, JSON, and API integration
  • Basic knowledge of data preprocessing, feature engineering, and exploratory data analysis
  • Familiarity with Git and collaborative software development practices
  • Strong analytical, mathematical, debugging, and problem-solving skills
  • Good written and verbal communication skills
  • Must currently reside in the United States
  • Experience with scikit-learn, NumPy, pandas, or similar Python libraries
  • Familiarity with deep learning architectures, including convolutional neural networks or transformers
  • Exposure to natural language processing, computer vision, or generative AI
  • Familiarity with FastAPI, Flask, or similar model-serving frameworks
  • Basic understanding of large language models, embeddings, and vector databases
  • Exposure to AWS, Microsoft Azure, or Google Cloud Platform
  • Familiarity with Docker, CI/CD pipelines, and automated testing
  • Understanding of model deployment, monitoring, and machine learning lifecycle management
  • Academic projects, internships, research experience, or GitHub repositories demonstrating practical AI/ML skills
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The Company
5 Employees
Year Founded: 2018

What We Do

EdgeQ develops 5G systems-on-chip for wireless infrastructure, combining connectivity and AI in a software-customizable, programmable platform. Its technology supports configurable 5G services and Open RAN deployments for enterprises, telecom operators and cloud providers, including base-station and edge-network use cases. The company’s mission is to make 5G more accessible through software-driven customization and bring cloud computing and data processing closer to where connectivity and data are used.

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