Edtech.com's Summary

HMH is hiring a Machine Learning Engineer. The Machine Learning Engineer will design, develop, and deploy machine learning systems to enhance AI solutions at scale, manage machine learning models, and support data and product teams. They will also optimize machine learning models and ensure seamless integration and functionality throughout the development lifecycle.

Highlights

Responsibilities include managing and deploying machine learning models, designing and evaluating tests, and optimizing model performance.
Proficiency in tools like SageMaker, MLflow, Kubeflow, and programming languages such as Python and C++ is required.
Salary range is $100k – $120k.
Requires a Master’s or Ph.D. in computer science, engineering, or a related field.
2-3 years of relevant experience in machine learning and data science is essential.
Knowledge of ML algorithms, frameworks like TensorFlow, and cloud platforms is necessary.
The position values problem-solving skills and the ability to adapt to new technologies.

Machine Learning Engineer Full Description

Job Title: Machine Learning Engineer

Location: Remote

Who We Are

NWEA® is a division of Houghton Mifflin Harcourt that supports students and educators through research, assessment solutions, policy and advocacy services, professional learning and school improvement services that fight for equity, drive classroom impact and push for systemic change in our educational communities. For nearly 50 years, NWEA has developed innovative pre-K–12 assessments, including their flagship interim assessment, MAP® Growth™ and their reading fluency and comprehension assessment, MAP® Reading Fluency™. For more information, visit NWEA.org to learn more.

What you will do

The Machine Learning Engineer (ML engineer) contributes to the design, development, and deployment of real-world machine learning systems that facilitate and accelerate advanced AI solutions at scale. Machine learning engineers manage aspects of the machine learning pipeline, including sourcing and preparing data, building, and training models, and deploying to and monitoring models in production.

Responsibilities

Manage, maintain, scale, and improve machine learning models already running in production environments.
Design, test, and deploy models to production, initially as a prototype, and then as an API that can serve predictions for end users.
Design, run, and evaluate machine learning tests and experiments.
Optimize and tweak machine learning models according to how they behave in production.
Use CI/CD processes throughout the model development lifecycle to develop higher quality code and models without disruption to production.
Provide technical support to data and product teams, helping relevant parties use and understand machine learning systems and datasets.

Education and Experience

Masters or Ph.D. in computer science, engineering, or quantitative field.
2-3 years of hands-on experience in machine learning, data science, and analytics.
Proficient with tools for machine learning pipeline management and monitoring (e.g., SageMaker, MLflow, Kubeflow).
Proficient with machine learning algorithms, architectures, programming languages (e.g., Python, C++, R), and libraries (e.g., PyTorch, TensorFlow).
Proficient with SQL and analytical warehouses (e.g., Snowflake).
Familiarity with automation and deployment (e.g., Jenkins, GitHub actions, Concourse, etc.) and infrastructure as code (e.g., Terraform) technologies.
Employed distributed processing frameworks such as Spark and Dask and has interacted with cloud platforms and container technologies.
Practical knowledge of software engineering concepts and best practices, like testing frameworks, packaging, API design, DevOps, DataOps and MLOps.
Excellent problem-solving skills and easily adapt to new technologies, trends, and frameworks.
Demonstrated ability to elevate the machine learning discipline and foster a collaborative and high-performing environment.

Salary range: 100k – 120k.

Application Deadline:
The application window for this position is anticipated to close on March 31, 2025. We encourage you to apply as soon as possible. The posting may be available past this date but is not guaranteed.

HMH is fully committed to Equal Employment Opportunity and to attracting, retaining, developing and promoting the most qualified employees without regard to race, gender, color, religion, sexual orientation, family status, marital status, pregnancy, gender identity, ethnic/national origin, ancestry, age, disability, military status, genetic predisposition, citizenship status, status as a disabled veteran, recently separated veteran, Armed Forces service medal veteran, other covered veteran, or any other characteristic protected by federal, state or local law. We are dedicated to providing a work environment free from discrimination and harassment, and where employees are treated with respect and dignity. We actively participate in E-Verify.

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Original Job Description

Job Title: Machine Learning Engineer

Location: Remote

Who We Are

What you will do

Responsibilities

Manage, maintain, scale, and improve machine learning models already running in production environments.
Design, test, and deploy models to production, initially as a prototype, and then as an API that can serve predictions for end users.
Design, run, and evaluate machine learning tests and experiments.
Optimize and tweak machine learning models according to how they behave in production.
Use CI/CD processes throughout the model development lifecycle to develop higher quality code and models without disruption to production.
Provide technical support to data and product teams, helping relevant parties use and understand machine learning systems and datasets.

Education and Experience

Masters or Ph.D. in computer science, engineering, or quantitative field.
2-3 years of hands-on experience in machine learning, data science, and analytics.
Proficient with tools for machine learning pipeline management and monitoring (e.g., SageMaker, MLflow, Kubeflow).
Proficient with machine learning algorithms, architectures, programming languages (e.g., Python, C++, R), and libraries (e.g., PyTorch, TensorFlow).
Proficient with SQL and analytical warehouses (e.g., Snowflake).
Familiarity with automation and deployment (e.g., Jenkins, GitHub actions, Concourse, etc.) and infrastructure as code (e.g., Terraform) technologies.
Employed distributed processing frameworks such as Spark and Dask and has interacted with cloud platforms and container technologies.
Practical knowledge of software engineering concepts and best practices, like testing frameworks, packaging, API design, DevOps, DataOps and MLOps.
Excellent problem-solving skills and easily adapt to new technologies, trends, and frameworks.
Demonstrated ability to elevate the machine learning discipline and foster a collaborative and high-performing environment.

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Edtech.com's Summary

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