Marker Learning · 1 week ago

Machine Learning Engineer

United States

Full-time

Remote

Mid Level

4+ years exp

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ConsultingEdTech

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Responsibilities

Design, implement, and deploy machine learning models for assessment tools and methodologies.

Collaborate with data scientists and psychometricians to integrate machine learning techniques into psychometric test development.

Work with one of the largest learning differences datasets to develop models for in-depth analysis of assessment results and ensure high clinical quality standards.

Deploy and integrate large language models (LLMs) and other natural language processing (NLP) solutions into production environments, ensuring high performance, scalability, and reliability while collaborating with software engineers to streamline the deployment process and monitor model performance in real-world applications.

Stay up-to-date with the latest research and best practices in machine learning and AI, ensuring our assessment tools remain at the forefront of the field.

Communicate findings and insights to both technical and non-technical stakeholders, including product managers, designers, and clinicians.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Machine LearningDeep LearningNatural Language ProcessingPythonProduction Environment Deployment CommunicationComputer VisionOpenCVTensorFlowAudio ProcessingLibrosaPydubNLPSpaCyNLTKHugging Face TransformersAdvanced NLP TechniquesTransformer-Based ModelsLanguage Model Fine-TuningDynamic Few-Shot Learning

Required

B.S, Masters or PhD in Computer Science, Mathematics, Statistics, or a related field

4+ years of experience in developing and deploying machine learning models in a production environment

Strong understanding of machine learning algorithms, deep learning, and natural language processing

Proficiency in Python

Excellent communication skills with both technical and non-technical stakeholders

Preferred

Experience with computer vision frameworks (e.g., OpenCV, TensorFlow), audio processing libraries (e.g., librosa, pydub), and NLP libraries (e.g., spaCy, NLTK, Hugging Face Transformers)

Knowledge of advanced NLP techniques such as transformer-based models, language model fine-tuning, or dynamic few-shot learning