Data Science | 2+ Yrs. | Voice AI Innovation

• Design, build, train, test, and evaluate machine learning models for applied research and product use cases
• Develop, maintain, and improve data pipelines used for model training, testing, validation, and experimentation
• Collaborate with software developers and engineers on existing ML models, technical workflows, and codebases
• Perform data exploration, preprocessing, cleaning, feature engineering, and statistical analysis
• Experiment with model architectures ranging from traditional ML models to neural networks and transformer-based approaches
• Evaluate model performance using appropriate metrics, testing methods, and validation strategies
• Work with time series, signal, audio, or other complex data types when required
• Support model optimization, tuning, and iteration based on experimental results and product requirements
• Document research findings, model assumptions, experiments, methodologies, and technical decisions
• Contribute to the development of scalable, reliable, and maintainable ML workflows
• Support cloud-based ML development and experimentation using AWS SageMaker and related tools
• Partner with cross-functional team members to translate business, research, or product needs into ML solutions
• Stay current with emerging machine learning research, tools, frameworks, and best practices

Pay: $120,000.00 - $180,000.00 per year

Do you have experience in Validation design?, Do you have a Master's degree?, We are seeking a motivated and hands-on Data Scientist/ML Researcher to join our team in San Francisco, CA. In this role, you will be responsible for designing, training, testing, and improving data pipelines and machine learning models while collaborating closely with existing developers, engineers, and research team members on current models, codebases, and production-facing AI systems.

This role requires a strong foundation in data science, machine learning, and model development, with practical experience using Python and modern ML frameworks. You will work across the full ML lifecycle, including data preprocessing, feature engineering, model experimentation, training, evaluation, validation, and optimization. The ideal candidate has experience building and training models from the ground up, understands a range of architectures from simple neural networks to large-scale transformer models, and is comfortable working with real-world datasets.

Experience with time series, signal processing, and/or audio data is particularly valuable. The role is well suited for someone who enjoys applied research, hands-on experimentation, and working collaboratively with software developers to turn models and pipelines into reliable technical solutions.

This position is based in San Francisco and requires the ability to work in an onsite hybrid environment., * Bachelor's degree in Data Science, Computer Science, Machine Learning, Artificial Intelligence, Statistics, Mathematics, Electrical Engineering, or a related technical discipline

• Minimum 2+ years of experience in data science, machine learning research, applied ML engineering, or a related technical role
• Must be located in, or willing to work from, San Francisco, CA
• Must be able to work in an onsite hybrid environment
• Strong hands-on experience with Python
• Experience designing, training, testing, and evaluating machine learning models
• Experience developing or supporting data pipelines for ML model training, testing, and deployment
• Familiarity with modern ML frameworks and libraries such as PyTorch, TensorFlow/Keras, and Scikit-learn
• Experience with data preprocessing, feature engineering, model evaluation, and performance optimization
• Familiarity with cloud-based ML tools, especially AWS SageMaker
• Understanding of neural network architectures, including basic feed-forward networks, CNNs, RNNs, and/or transformer-based models
• Ability to collaborate with software developers and technical teams on existing models, codebases, and data workflows
• Strong analytical, problem-solving, and experimental design skills
• Strong written and verbal communication skills, including the ability to explain technical concepts to both technical and non-technical stakeholders, * Master's degree or PhD in Data Science, Machine Learning, Artificial Intelligence, Computer Science, Statistics, Electrical Engineering, or a related technical field
• Experience working with time series data
• Experience with audio data, acoustic modeling, or speech-related ML applications
• Knowledge of signal processing techniques
• Experience training custom models on large or complex datasets
• Experience with large-scale transformer models, LLMs, or foundation models
• Experience with MLOps, model monitoring, versioning, experiment tracking, CI/CD for ML, or automated ML workflows
• Experience deploying ML models in cloud environments
• Hands-on experience with AWS services beyond SageMaker, such as S3, Lambda, EC2, ECS, Glue, or Redshift
• Experience with data labeling, data quality assessment, and dataset curation
• Experience with model interpretability, explainability, validation, or bias evaluation
• Publications in major machine learning, AI, data science, signal processing, or related research journals or conferences
• Experience working in a fast-paced startup, research lab, or applied AI product environment, * Data science: 2 years (Required)