{"schemaVersion":"jobsearcher.job.v1","id":"5f3dd8ffc5bb8aad65ae065a","url":"https://jobsearcher.com/jobs/5f3dd8ffc5bb8aad65ae065a","canonicalUrl":"https://jobsearcher.com/jobs/5f3dd8ffc5bb8aad65ae065a","title":"Machine Learning Engineer","description":"Position : Machine Learning Engineer\r\nExperience : 9+yrs\r\nVisa : GC, USC, GCEAD, H4EAD, TN\r\nTax Term : W2\r\nClient : Tesla\r\nLocation : Fremont, CA, onsite\r\nProject DescriptionDesign, develop and implement critical machine learning models that operate on our factory and warehouse environments\r\nDuties/Day to Day Overview1. Translating Ambiguous Problems into ML SolutionsYou will take loosely defined or complex business and operational problems and determine how to solve them using machine learning. This involves clarifying requirements, designing an approach, and selecting the right algorithms and architectures (e.g., supervised learning, CNNs).\r\n2. Building End-to-End Machine Learning PipelinesYou will design, implement, and train ML models using frameworks likePyTorchandTensorFlow , leveraging data tools likePandasfor preprocessing and analysis. The process will include:\r\nData gathering\r\nCleaning and preprocessing\r\nModel training and evaluation\r\nOptimization for performance and efficiency\r\nDeployment to production environments\r\n3. Handling Complex, Multimodal DataYou will work with large and varied datasets — includingimages, multi-spectral sensor outputs, voice, text, and tabular data— and develop preprocessing strategies to make this data usable for machine learning models.\r\n4. Collaborating with Cross-Functional TeamsYou will partner withproduction, process, controls, and quality teamsto understand operational pain points and design ML-based solutions that integrate seamlessly into existing workflows and systems.\r\n5. Deploying, Monitoring, and Maintaining ModelsYou will own models after deployment, setting up robustalerting and monitoring systemsto track performance, detect issues, and initiate quick fixes when needed.\r\n6. Optimizing Algorithms for PerformanceYou will improve speed and efficiency throughquantization, pruning, and TensorRT conversion , ensuring that models meet performance requirements in real-world environments — including embedded or firmware-integrated contexts (leveraging C++ if needed).\r\n7. Applying Strong Theoretical FoundationsYou will use expertise inlinear algebra, geometry, probability theory, numerical optimization, and statisticsto design models, assess feasibility, and ensure rigorous evaluation.\r\n8. Specializing in High-Impact DomainsDepending on the project, you may work on problems incomputer vision, large language models, recommender systems, or operations research , applying domain-specific techniques to deliver maximum value.\r\n9. Writing High-Quality, Sustainable CodeYou will produceclean, modular, and maintainable codeto ensure that ML solutions are scalable and easy to update, supporting long-term sustainability of deployed systems.\r\nTop Requirements(Must haves)Algorithm Development & OptimizationRapid prototyping of algorithms forhigh-performance, data-intensive applications .\r\nOptimization forspeed, efficiency, and scalabilityin production environments.\r\n2. Programming & IntegrationPython– advanced expertise for data processing, ML model development, and automation.\r\nC++– desirable proficiency for integration withvehicle firmwareand full product lifecycle delivery.\r\n3. Mathematical & Statistical FoundationsStrong background in:\r\nLinear AlgebraandGeometry– essential for ML, graphics, and computer vision.\r\nProbability Theory– for modeling uncertainty and decision-making.\r\nNumerical Optimization– for training and refining models.\r\nStatistics– for model evaluation and performance analysis.\r\n4. Deep Learning FrameworksHands-on experience withPyTorchandTensorFlowfor model development and deployment.\r\n5. Model Optimization & DeploymentSkilled in performance-enhancing techniques:\r\nQuantization\r\nPruning\r\nTensorRT conversion\r\nDeploying and maintainingproduction machine learning use cases .\r\n6. Domain ExpertiseProficiency in at least one specialized area:\r\nComputer Vision\r\nLarge Language Models (LLMs)\r\nRecommender Systems\r\nOperations Research\r\n7. Software Engineering Best PracticesWritingclean, sustainable, and modular code .\r\nTranslatingresearch prototypesinto robust, production-ready systems.#J-18808-Ljbffr","company":"Talentify","rawCompany":"talentify","city":"Fremont","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-10-04T01:24:36.085Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541990","title":"All Other Professional, Scientific, and Technical Services","slug":"all-other-professional-scientific-and-technical-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Engineer","description":"Position : Machine Learning Engineer\r\nExperience : 9+yrs\r\nVisa : GC, USC, GCEAD, H4EAD, TN\r\nTax Term : W2\r\nClient : Tesla\r\nLocation : Fremont, CA, onsite\r\nProject DescriptionDesign, develop and implement critical machine learning models that operate on our factory and warehouse environments\r\nDuties/Day to Day Overview1. Translating Ambiguous Problems into ML SolutionsYou will take loosely defined or complex business and operational problems and determine how to solve them using machine learning. This involves clarifying requirements, designing an approach, and selecting the right algorithms and architectures (e.g., supervised learning, CNNs).\r\n2. Building End-to-End Machine Learning PipelinesYou will design, implement, and train ML models using frameworks likePyTorchandTensorFlow , leveraging data tools likePandasfor preprocessing and analysis. The process will include:\r\nData gathering\r\nCleaning and preprocessing\r\nModel training and evaluation\r\nOptimization for performance and efficiency\r\nDeployment to production environments\r\n3. Handling Complex, Multimodal DataYou will work with large and varied datasets — includingimages, multi-spectral sensor outputs, voice, text, and tabular data— and develop preprocessing strategies to make this data usable for machine learning models.\r\n4. Collaborating with Cross-Functional TeamsYou will partner withproduction, process, controls, and quality teamsto understand operational pain points and design ML-based solutions that integrate seamlessly into existing workflows and systems.\r\n5. Deploying, Monitoring, and Maintaining ModelsYou will own models after deployment, setting up robustalerting and monitoring systemsto track performance, detect issues, and initiate quick fixes when needed.\r\n6. Optimizing Algorithms for PerformanceYou will improve speed and efficiency throughquantization, pruning, and TensorRT conversion , ensuring that models meet performance requirements in real-world environments — including embedded or firmware-integrated contexts (leveraging C++ if needed).\r\n7. Applying Strong Theoretical FoundationsYou will use expertise inlinear algebra, geometry, probability theory, numerical optimization, and statisticsto design models, assess feasibility, and ensure rigorous evaluation.\r\n8. Specializing in High-Impact DomainsDepending on the project, you may work on problems incomputer vision, large language models, recommender systems, or operations research , applying domain-specific techniques to deliver maximum value.\r\n9. Writing High-Quality, Sustainable CodeYou will produceclean, modular, and maintainable codeto ensure that ML solutions are scalable and easy to update, supporting long-term sustainability of deployed systems.\r\nTop Requirements(Must haves)Algorithm Development & OptimizationRapid prototyping of algorithms forhigh-performance, data-intensive applications .\r\nOptimization forspeed, efficiency, and scalabilityin production environments.\r\n2. Programming & IntegrationPython– advanced expertise for data processing, ML model development, and automation.\r\nC++– desirable proficiency for integration withvehicle firmwareand full product lifecycle delivery.\r\n3. Mathematical & Statistical FoundationsStrong background in:\r\nLinear AlgebraandGeometry– essential for ML, graphics, and computer vision.\r\nProbability Theory– for modeling uncertainty and decision-making.\r\nNumerical Optimization– for training and refining models.\r\nStatistics– for model evaluation and performance analysis.\r\n4. Deep Learning FrameworksHands-on experience withPyTorchandTensorFlowfor model development and deployment.\r\n5. Model Optimization & DeploymentSkilled in performance-enhancing techniques:\r\nQuantization\r\nPruning\r\nTensorRT conversion\r\nDeploying and maintainingproduction machine learning use cases .\r\n6. Domain ExpertiseProficiency in at least one specialized area:\r\nComputer Vision\r\nLarge Language Models (LLMs)\r\nRecommender Systems\r\nOperations Research\r\n7. Software Engineering Best PracticesWritingclean, sustainable, and modular code .\r\nTranslatingresearch prototypesinto robust, production-ready systems.#J-18808-Ljbffr","datePosted":"2026-10-04T01:24:36.085Z","dateModified":"2026-10-04T01:24:36.085Z","hiringOrganization":{"@type":"Organization","name":"Talentify","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Fremont","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"5f3dd8ffc5bb8aad65ae065a"},"url":"https://jobsearcher.com/jobs/5f3dd8ffc5bb8aad65ae065a"}}