Machine Learning Researcher (Data Science & Applied AI)

Machine Learning Researcher – Applied AI & New Model Development

Position Overview

Job Title: Machine Learning Researcher
Location: Bay Area, CA
Work Arrangement: Onsite
Employment Type: Full-Time
Experience Level: Mid-Level to Senior-Level
Compensation: Competitive, based on experience, research depth, and technical fit

About the Opportunity

We are seeking a Machine Learning Researcher to join a growing applied AI research team in the Bay Area.

This role is focused on hands-on machine learning research, new model development, training models from scratch, experimental design, statistical analysis, model benchmarking, and applied AI problem solving. The ideal candidate has experience moving beyond off-the-shelf models and APIs, with a strong ability to design, train, evaluate, and improve custom machine learning models for complex real-world problems.

This opportunity is well suited for candidates ranging from strong early-career researchers to senior ML professionals with deep hands-on experience developing original models, testing new architectures, working with large datasets, and translating research ideas into practical machine learning systems.

The successful candidate will work closely with researchers, data scientists, ML engineers, and product-focused technical teams to explore novel approaches, build experimental pipelines, evaluate model performance, and help advance the company’s core AI capabilities.

Key Responsibilities

New Model Development & Research

• Design, develop, train, evaluate, and optimize new machine learning models from scratch
• Build custom model architectures rather than relying only on pre-trained models or third-party APIs
• Research and test new algorithms, model families, architectures, feature representations, and training strategies
• Develop experimental approaches for improving model accuracy, robustness, generalization, and interpretability
• Work with large, complex datasets to identify patterns, signals, and opportunities for model improvement
• Translate research ideas into working prototypes, experiments, and measurable model outputs

Model Training, Evaluation & Benchmarking

• Train and validate custom ML and deep learning models using modern frameworks
• Establish strong model evaluation practices, including baseline comparisons, error analysis, performance metrics, and robustness testing
• Benchmark models against existing approaches and identify meaningful performance improvements
• Conduct statistical analysis, exploratory data analysis, feature analysis, and model diagnostics
• Design experiments that clearly measure the impact of architecture changes, feature changes, training strategies, and data quality improvements
• Document results, trade-offs, limitations, and recommendations for future model development

Data Science, Feature Engineering & Signal Discovery

• Build scalable data processing, feature engineering, and analysis workflows
• Work with structured, semi-structured, and unstructured datasets
• Identify predictive signals, weak labels, useful representations, and model-ready features
• Support data cleaning, data validation, data transformation, labeling strategies, and dataset quality analysis
• Analyze model behavior across different data segments, edge cases, and real-world usage patterns
• Develop reusable research datasets and experimental pipelines

Applied AI Collaboration

• Collaborate with ML engineers, software engineers, data scientists, and research leads to integrate model research into applied AI initiatives
• Support model prototyping, model handoff, and research-to-product transition where appropriate
• Communicate research findings clearly to technical and non-technical stakeholders
• Contribute to research discussions, technical reviews, roadmap input, and model strategy
• Stay current with emerging research in machine learning, deep learning, generative AI, transformers, time-series modeling, signal processing, audio AI, and applied data science

Senior-Level Contribution

For senior candidates, responsibilities may also include:

• Leading model development workstreams from research question through trained model and evaluation
• Defining research direction, model strategy, and technical trade-offs
• Mentoring junior ML researchers or data scientists
• Establishing best practices for model training, experiment tracking, benchmarking, and reproducibility
• Reviewing model architectures, research plans, experiments, and technical documentation
• Helping determine whether to build, fine-tune, adapt, or replace existing models
• Driving original research initiatives that improve core product capabilities

Required Qualifications

• Bachelor’s, Master’s, or PhD in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Statistics, Applied Mathematics, Engineering, Physics, or a related quantitative field

• 3+ years of hands-on experience in machine learning, AI research, data science, applied AI, or related technical work

• Strong hands-on experience building, training, and evaluating machine learning models

• Demonstrated experience developing custom models, new architectures, or model pipelines from scratch

• Strong programming ability in Python

• Experience with one or more major ML frameworks, such as:

  • PyTorch
  • TensorFlow
  • Keras
  • Scikit-learn

• Strong understanding of:

  • Supervised learning
  • Unsupervised learning
  • Deep learning
  • Neural networks
  • Feature engineering
  • Model evaluation
  • Model optimization
  • Statistical analysis
  • Data preprocessing
  • Experimental design

• Experience conducting model experiments, analyzing results, and improving performance through iteration

• Strong analytical, research, problem-solving, and communication skills

• Ability to work onsite in the Bay Area

Preferred Experience

• 5–10+ years of machine learning, data science, or applied AI experience
• Experience training deep learning models from scratch
• Experience with time-series modeling, forecasting, sequential data, or temporal pattern recognition
• Experience with audio, voice, speech, signal processing, or human-behavior-related data
• Experience with transformer architectures, LLMs, embedding models, or representation learning
• Experience with statistical modeling, hypothesis testing, experiment design, and A/B testing
• Experience with large-scale datasets and complex data pipelines
• Experience with GPU-based model training, distributed training, or high-performance ML workloads
• Experience with MLOps workflows, model versioning, experiment tracking, and reproducible research
• Experience with AWS SageMaker or other cloud-based ML infrastructure
• Experience with Docker, Kubernetes, Git, and collaborative technical workflows

Additional Beneficial Experience

• PhD or research-intensive graduate degree in a relevant field
• Research publications in machine learning, AI, statistics, data science, speech, audio, NLP, computer vision, or related areas
• Experience with multimodal AI, including audio/text, image/text, or other cross-modal representations
• Experience with generative AI, RAG, fine-tuning, LoRA, PEFT, or foundation model adaptation
• Experience with model interpretability, explainability, fairness, bias analysis, or uncertainty estimation
• Experience with open-source ML contributions
• Experience building proof-of-concept models that later moved into production or product use
• Experience working in startup, research lab, or fast-moving applied AI environments

Technical Skills

Relevant technologies may include:

Python, PyTorch, TensorFlow, Keras, Scikit-learn, NumPy, Pandas, SQL, MATLAB, R, XGBoost, LightGBM, Hugging Face, Transformers, LSTMs, CNNs, neural networks, time-series analysis, forecasting, statistical modeling, signal processing, audio processing, feature engineering, model training, model evaluation, AWS SageMaker, cloud ML infrastructure, Docker, Kubernetes, Git, experiment tracking, MLOps, GPU acceleration, and large-scale data processing.

Ideal Candidate

The ideal candidate is a hands-on ML researcher who enjoys building models, not just using models.

They are curious, experimental, analytical, and comfortable working through ambiguity. They can start with a research question, explore the data, define a baseline, build a model, evaluate results, identify weaknesses, and iterate toward a stronger approach.

Strong candidates will have experience with some or all of the following:

• Building new ML models from scratch
• Training deep learning models
• Working with time-series, signal, audio, or sequential data
• Designing experiments and benchmarks
• Comparing model architectures
• Improving model performance through feature, data, and architecture changes
• Communicating findings clearly
• Turning research into practical applied AI capabilities

Strong performers will:

• Build custom models that improve core product performance
• Develop clear experimental baselines and measurable model improvements
• Identify meaningful signals in complex datasets
• Train and evaluate models with discipline and reproducibility
• Communicate technical findings clearly
• Contribute original thinking to the research roadmap
• Help the team move from promising research ideas to practical AI solutions
• Mentor or guide others where appropriate, especially at the senior level

Work Environment

This is an onsite role in the Bay Area, CA. The team works in a collaborative research and applied AI environment focused on solving complex real-world problems through machine learning, data science, and model development.

Equal Opportunity Employer

We are an equal opportunity employer and do not discriminate on the basis of race, color, religion, sex, gender identity, sexual orientation, national origin, disability, veteran status, age, or any other protected characteristic.

We value diverse perspectives and are committed to creating an inclusive, respectful, and collaborative workplace.