Machine Learning Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Machine Learning Engineer with a contract length of "unknown" and a pay rate of "$/hour." Work location is "remote." Key skills include Python, ML frameworks, and cloud platforms. A Bachelor's or Master's in a related field is required, along with experience in deploying models at scale.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
-
🗓️ - Date discovered
August 23, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
Unknown
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📄 - Contract type
Unknown
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🔒 - Security clearance
Unknown
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📍 - Location detailed
San Francisco Bay Area
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🧠 - Skills detailed
#Data Pipeline #Data Science #Programming #NLP (Natural Language Processing) #Reinforcement Learning #Kubernetes #Cloud #Azure #PyTorch #TensorFlow #Model Evaluation #Continuous Deployment #"ETL (Extract #Transform #Load)" #AWS (Amazon Web Services) #GCP (Google Cloud Platform) #Python #Monitoring #SageMaker #Deployment #Hugging Face #C++ #ML (Machine Learning) #Docker #Java #Scala #Computer Science #Airflow #R #MLflow
Role description
About the Role
We are seeking a skilled Machine Learning Engineer to design, build, and deploy production-grade ML models that drive innovation across our products and services. You will work closely with data scientists, software engineers, and business stakeholders to translate cutting-edge research into scalable, real-world applications. This role is ideal for someone who thrives at the intersection of data science, software engineering, and problem-solving.
Key Responsibilities
• Design, develop, and optimize machine learning models for [industry use case — e.g., predictive analytics, natural language processing, computer vision].
• Collaborate with data scientists to prototype algorithms and transform them into scalable production systems.
• Build and maintain data pipelines and model training workflows.
• Apply best practices in MLOps, including versioning, monitoring, and continuous deployment of models.
• Work with software engineers to integrate ML models into customer-facing applications.
• Conduct performance testing, validation, and error analysis to ensure robustness and fairness.
• Stay up to date with the latest ML frameworks, research, and tools (e.g., PyTorch, TensorFlow, Hugging Face, Scikit-learn).
Qualifications
Required:
• Bachelor’s or Master’s in Computer Science, Data Science, Engineering, or related field.
• Strong programming skills in Python (plus familiarity with Java, C++, or R a bonus).
• Hands-on experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Solid understanding of data structures, algorithms, and distributed systems.
• Experience with data pre-processing, feature engineering, and model evaluation.
• Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker/Kubernetes).
Preferred:
• Experience with MLOps tools (MLflow, Kubeflow, Airflow, SageMaker).
• Background in NLP, computer vision, or reinforcement learning.
• Prior experience deploying models at scale in a production environment.
• Strong collaboration and communication skills.
About the Role
We are seeking a skilled Machine Learning Engineer to design, build, and deploy production-grade ML models that drive innovation across our products and services. You will work closely with data scientists, software engineers, and business stakeholders to translate cutting-edge research into scalable, real-world applications. This role is ideal for someone who thrives at the intersection of data science, software engineering, and problem-solving.
Key Responsibilities
• Design, develop, and optimize machine learning models for [industry use case — e.g., predictive analytics, natural language processing, computer vision].
• Collaborate with data scientists to prototype algorithms and transform them into scalable production systems.
• Build and maintain data pipelines and model training workflows.
• Apply best practices in MLOps, including versioning, monitoring, and continuous deployment of models.
• Work with software engineers to integrate ML models into customer-facing applications.
• Conduct performance testing, validation, and error analysis to ensure robustness and fairness.
• Stay up to date with the latest ML frameworks, research, and tools (e.g., PyTorch, TensorFlow, Hugging Face, Scikit-learn).
Qualifications
Required:
• Bachelor’s or Master’s in Computer Science, Data Science, Engineering, or related field.
• Strong programming skills in Python (plus familiarity with Java, C++, or R a bonus).
• Hands-on experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Solid understanding of data structures, algorithms, and distributed systems.
• Experience with data pre-processing, feature engineering, and model evaluation.
• Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker/Kubernetes).
Preferred:
• Experience with MLOps tools (MLflow, Kubeflow, Airflow, SageMaker).
• Background in NLP, computer vision, or reinforcement learning.
• Prior experience deploying models at scale in a production environment.
• Strong collaboration and communication skills.
