Machine Learning Engineer II

Help develop machine learning solutions that support real products and applications. As a Machine Learning Engineer II, you'll work across algorithm development, data pipelines, model validation, and production deployment while helping turn promising ideas into dependable technical solutions. This role combines hands-on programming with testing, documentation, and collaboration across engineering teams.

You'll be a key contributor on projects that move machine learning from concept to implementation. The ideal candidate has enough experience to work independently on assigned initiatives, but is still excited to learn, explore new approaches, and collaborate with others when challenges arise. From designing proof-of-concept solutions to monitoring deployed models, you'll gain broad exposure to the machine learning lifecycle.

Required Skills & Experience
3-5 years of related experience after completing a bachelor's degree, or a recent master's degree

Experience implementing, refining, and validating machine learning or deep learning algorithms

Strong programming and software development skills

Familiarity with Python, Java, Scala, or similar programming languages

Experience designing or supporting data pipelines for ingestion, validation, cleaning, and monitoring

Bachelor's degree in Computer Science, Computer Engineering, Mathematics, or a related technical discipline-or equivalent industry experience

Desired Skills & Experience
Experience with data mining or statistical analysis tools

Experience deploying and monitoring machine learning models

Knowledge of Kafka, Spark, Docker, or similar data and cloud technologies

Experience designing proof-of-concept solutions or contributing to technical studies

Familiarity with model testing, accuracy evaluation, case studies, or performance reporting

Experience collaborating with teams outside of an immediate work group

What You Will Be Doing
Implement, refine, and validate machine learning algorithms for products and applications

Design and develop data pipelines for data ingestion, validation, cleaning, and monitoring

Train models, evaluate accuracy, and support the deployment of validated models into production

Design proof-of-concept solutions and contribute to future product development efforts

Test and evaluate solutions through case studies, technical reviews, and reporting

Collaborate with cross-functional teams to address technical issues and support project delivery

Tech Breakdown
30% Machine Learning Algorithm and Model Development

25% Data Pipeline and Cloud Technologies

20% Model Training, Validation, and Deployment

15% Proofs of Concept, Testing, and Evaluation

10% Documentation, Collaboration, and Technical Support

Daily Responsibilities
Develop and refine machine learning algorithms and models

Build, test, and monitor data pipelines

Review model accuracy, performance, and production results

Support model deployment and ongoing monitoring activities

Contribute to proof-of-concept projects, case studies, and technical evaluations

Prepare requirements, test results, reports, presentations, and other technical documentation

Work with engineering and technical teams to resolve project issues and improve solutions