Machine Learning Researcher (Data Science & Applied AI)

Machine Learning Researcher (Data Science & Applied AI)
Location: Bay Area, CA (Onsite)
Experience: 1–3 Years
Employment Type: Full-Time
About the Role
We are seeking a highly motivated Machine Learning Researcher with a strong foundation in Machine Learning, Data Science, and AI model development to join our growing research team in the Bay Area.
This role is ideal for an early-career ML professional who is passionate about solving complex real-world problems through data-driven research. You will work alongside experienced ML engineers and researchers to design, develop, train, evaluate, and optimize machine learning models and data pipelines. The position emphasizes hands-on research, experimentation, and model development rather than production software engineering.
The ideal candidate enjoys working with large datasets, developing novel approaches, and translating research into practical machine learning solutions.
Responsibilities
Design, develop, and optimize machine learning models for research and applied AI projects.
Build scalable data processing and feature engineering pipelines.
Train, evaluate, validate, and benchmark machine learning models using modern ML frameworks.
Conduct experiments to improve model performance, robustness, and generalization.
Analyze structured and unstructured datasets to extract meaningful insights.
Research and evaluate new machine learning algorithms, architectures, and state-of-the-art techniques.
Collaborate closely with software engineers and ML developers to enhance existing models and research initiatives.
Perform statistical analysis, exploratory data analysis (EDA), and model validation.
Document research findings, experiments, and technical approaches.
Stay current with emerging trends in machine learning, deep learning, generative AI, and data science.
Required Qualifications
Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Statistics, Applied Mathematics, or a related quantitative field.
1–3 years of hands-on experience in Machine Learning, AI Research, Data Science, or related fields.
Strong programming skills in Python.
Experience with one or more major ML frameworks:
PyTorch
TensorFlow
Keras
Scikit-learn
Strong understanding of:
Supervised and unsupervised learning
Deep learning
Neural networks
Model evaluation and optimization
Feature engineering
Data preprocessing
Experience training and evaluating custom machine learning models.
Strong analytical and problem-solving skills.
Excellent communication and collaboration abilities.
Preferred Qualifications
Experience with one or more of the following is highly desirable:
AWS SageMaker
Large Language Models (LLMs)
Transformer architectures
Time series forecasting
Audio or signal processing
Computer vision
Statistical modeling
Experiment design and A/B testing
MLOps workflows
Cloud-based ML infrastructure
Docker and Kubernetes
Git and collaborative software development
Nice to Have
Research publications in leading Machine Learning, AI, or Data Science conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP, AAAI).
Experience working with large-scale datasets.
Familiarity with distributed training or GPU acceleration.
Open-source contributions to ML projects.
Ideal Candidate
The successful candidate is naturally curious, enjoys experimentation, and has a passion for applying machine learning research to solve challenging problems. They thrive in a collaborative research environment and are excited to explore new ideas while contributing to cutting-edge AI initiatives.
Technical Skills
Python
PyTorch
TensorFlow
Keras
Scikit-learn
AWS SageMaker
Machine Learning
Deep Learning
Data Science
Statistics
Neural Networks
Data Processing
Feature Engineering
Model Training
Model Evaluation
Time Series Analysis
Signal Processing
Audio Processing
Transformers
Large Language Models (LLMs)
SQL
Git
This role is an excellent opportunity for an early-career ML professional who wants to build a career focused on machine learning research, data science, and applied AI while working onsite with an experienced team in the Bay Area.