{"schemaVersion":"jobsearcher.job.v1","id":"700438d777b8ddf5ef8da8a5","url":"https://jobsearcher.com/jobs/700438d777b8ddf5ef8da8a5","canonicalUrl":"https://jobsearcher.com/jobs/700438d777b8ddf5ef8da8a5","title":"Staff Machine Learning Engineer, Time Series & Statistical Methods","description":"OverviewAs Staff ML Engineer, you will advance Nominal's AI-driven hardware data tools by bringing rigorous time-series and statistical methods to high-rate telemetry and sensor data. You'll shape ML direction, balancing classical statistics with deep learning to power agents that reason over real hardware measurements. You'll translate methods into robust tools and collaborate with evals to ensure engineers can rely on your solutions. This role offers the chance to influence how hardware programs scale and accelerate innovations across aerospace, automotive, and defense domains.Compensation / Benefits100% coverage of medical, dental, and vision insuranceUnlimited PTO and sick leaveFree lunch, snacks, and coffeeProfessional Development StipendAnnual company retreatResponsibilitiesDevelop anomaly detection, change-point detection, and forecasting for high-rate telemetry and fleet data.Package methods as agent tools enabling feature engineering and statistics for real data.Create signal-processing and statistical techniques (frequency analysis, trend fitting, run-to-run comparison) robust to noisy data.Collaborate with evals to determine when methods are production-ready for engineers.Set the ML technical roadmap and evaluate when to invest in learned models vs. classical approaches.Key requirements8+ years in applied ML or statistics with production systems on time-series or sensor dataStrong grounding in statistics, signal processing, anomaly detection, and forecastingHands-on experience with deep learning for sequences and sensor data (transformers, embeddings)Solid software engineering to ship reliable, tested codeJudgment to choose simple statistical tests over neural nets when appropriateRecord of setting technical direction and elevating engineering qualityExperience with modern AI coding agents (e.g., Claude Code, Cursor, Codex) and openness to new toolscollaborativecuriousstrong judgmenttime-series and sensor data modelinganomaly detection and forecastingsignal processing (frequency analysis, trend fitting)","company":"Nominal","rawCompany":"nominal","city":"Baltimore","state":"MD","isRemote":false,"isActive":false,"createdAt":"2026-10-26T14:32:53.805Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"17-2112.02","title":"Validation Engineers","slug":"validation-engineers"},{"code":"15-2041.00","title":"Statisticians","slug":"statisticians"}],"industries":[{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"},{"code":"541990","title":"All Other Professional, Scientific, and Technical Services","slug":"all-other-professional-scientific-and-technical-services"},{"code":"541330","title":"Engineering Services","slug":"engineering-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Staff Machine Learning Engineer, Time Series & Statistical Methods","description":"OverviewAs Staff ML Engineer, you will advance Nominal's AI-driven hardware data tools by bringing rigorous time-series and statistical methods to high-rate telemetry and sensor data. You'll shape ML direction, balancing classical statistics with deep learning to power agents that reason over real hardware measurements. You'll translate methods into robust tools and collaborate with evals to ensure engineers can rely on your solutions. This role offers the chance to influence how hardware programs scale and accelerate innovations across aerospace, automotive, and defense domains.Compensation / Benefits100% coverage of medical, dental, and vision insuranceUnlimited PTO and sick leaveFree lunch, snacks, and coffeeProfessional Development StipendAnnual company retreatResponsibilitiesDevelop anomaly detection, change-point detection, and forecasting for high-rate telemetry and fleet data.Package methods as agent tools enabling feature engineering and statistics for real data.Create signal-processing and statistical techniques (frequency analysis, trend fitting, run-to-run comparison) robust to noisy data.Collaborate with evals to determine when methods are production-ready for engineers.Set the ML technical roadmap and evaluate when to invest in learned models vs. classical approaches.Key requirements8+ years in applied ML or statistics with production systems on time-series or sensor dataStrong grounding in statistics, signal processing, anomaly detection, and forecastingHands-on experience with deep learning for sequences and sensor data (transformers, embeddings)Solid software engineering to ship reliable, tested codeJudgment to choose simple statistical tests over neural nets when appropriateRecord of setting technical direction and elevating engineering qualityExperience with modern AI coding agents (e.g., Claude Code, Cursor, Codex) and openness to new toolscollaborativecuriousstrong judgmenttime-series and sensor data modelinganomaly detection and forecastingsignal processing (frequency analysis, trend fitting)","datePosted":"2026-10-26T14:32:53.805Z","dateModified":"2026-10-26T14:32:53.805Z","hiringOrganization":{"@type":"Organization","name":"Nominal","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Baltimore","addressRegion":"MD","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"700438d777b8ddf5ef8da8a5"},"url":"https://jobsearcher.com/jobs/700438d777b8ddf5ef8da8a5"}}