Machine Learning Researcher (Data Science & Applied AI)

About the CompanyWe 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.About the RoleMachine Learning Researcher – Applied AI & New Model Development Position OverviewResponsibilitiesNew Model Development & ResearchDesign, develop, train, evaluate, and optimize new machine learning models from scratchBuild custom model architectures rather than relying only on pre-trained models or third-party APIsResearch and test new algorithms, model families, architectures, feature representations, and training strategiesDevelop experimental approaches for improving model accuracy, robustness, generalization, and interpretabilityWork with large, complex datasets to identify patterns, signals, and opportunities for model improvementTranslate research ideas into working prototypes, experiments, and measurable model outputsModel Training, Evaluation & BenchmarkingTrain and validate custom ML and deep learning models using modern frameworksEstablish strong model evaluation practices, including baseline comparisons, error analysis, performance metrics, and robustness testingBenchmark models against existing approaches and identify meaningful performance improvementsConduct statistical analysis, exploratory data analysis, feature analysis, and model diagnosticsDesign experiments that clearly measure the impact of architecture changes, feature changes, training strategies, and data quality improvementsDocument results, trade-offs, limitations, and recommendations for future model developmentData Science, Feature Engineering & Signal DiscoveryBuild scalable data processing, feature engineering, and analysis workflowsWork with structured, semi-structured, and unstructured datasetsIdentify predictive signals, weak labels, useful representations, and model-ready featuresSupport data cleaning, data validation, data transformation, labeling strategies, and dataset quality analysisAnalyze model behavior across different data segments, edge cases, and real-world usage patternsDevelop reusable research datasets and experimental pipelinesApplied AI CollaborationCollaborate with ML engineers, software engineers, data scientists, and research leads to integrate model research into applied AI initiativesSupport model prototyping, model handoff, and research-to-product transition where appropriateCommunicate research findings clearly to technical and non-technical stakeholdersContribute to research discussions, technical reviews, roadmap input, and model strategyStay current with emerging research in machine learning, deep learning, generative AI, transformers, time-series modeling, signal processing, audio AI, and applied data scienceSenior-Level ContributionFor senior candidates, responsibilities may also include:Leading model development workstreams from research question through trained model and evaluationDefining research direction, model strategy, and technical trade-offsMentoring junior ML researchers or data scientistsEstablishing best practices for model training, experiment tracking, benchmarking, and reproducibilityReviewing model architectures, research plans, experiments, and technical documentationHelping determine whether to build, fine-tune, adapt, or replace existing modelsDriving original research initiatives that improve core product capabilitiesQualificationsBachelor’s, Master’s, or PhD in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Statistics, Applied Mathematics, Engineering, Physics, or a related quantitative field3+ years of hands-on experience in machine learning, AI research, data science, applied AI, or related technical workStrong hands-on experience building, training, and evaluating machine learning modelsDemonstrated experience developing custom models, new architectures, or model pipelines from scratchStrong programming ability in PythonExperience with one or more major ML frameworks, such as:PyTorchTensorFlowKerasScikit-learnStrong understanding of:Supervised learningUnsupervised learningDeep learningNeural networksFeature engineeringModel evaluationModel optimizationStatistical analysisData preprocessingExperimental designExperience conducting model experiments, analyzing results, and improving performance through iterationStrong analytical, research, problem-solving, and communication skillsAbility to work onsite in the Bay AreaRequired Skills5–10+ years of machine learning, data science, or applied AI experienceExperience training deep learning models from scratchExperience with time-series modeling, forecasting, sequential data, or temporal pattern recognition