Top Machine Learning Jobs in Tijuana
In this role, you will lead and support the design and development of enterprise data products, manage multiple data engineering teams, influence data strategy, and mentor professionals in the data engineering community. You will leverage your technical expertise to transform data into actionable insights, overseeing the operational processes and ensuring efficiency and innovation.
The Research Scientist will apply scientific expertise to advance Google DeepMind's mission, focusing on developing methods for teaching large language models to graphically explain concepts, creating evaluations for performance, exploring multimodal AI across various domains, and contributing to training and publishing research on the Gemini project.
As a Senior Data Scientist at Rocket Money, you'll build machine learning-driven features in collaboration with engineering, product, and design teams. You'll also optimize product feedback loops, develop automation for budgeting tools, construct customer segmentation models, run experiments to gauge impact, and ensure accuracy in transaction categorization systems. You'll lead projects, drive strategy, and focus on enhancing customers' financial experiences.
The Senior Machine Learning Engineer will work on developing AI-powered software, fine-tuning large language models, conducting multimodal product research, and ensuring model alignment to improve user experience at Scribe.
The Machine Learning Engineer will work on AI-powered software development, focusing on multimodal capabilities including vision and audio. Responsibilities include designing data pipelines for model training, improving model performance through experimentation, and collaborating with various teams.
The Machine Learning Engineer at Scribe will design and implement advanced AI techniques, optimize model performance, and create benchmarking infrastructure. Responsibilities include translating AI innovations into features, working on memory management, and collaborating with engineering teams to enhance model quality.
As the Lead Data Scientist, you will establish a data science practice, collaborate with various teams to optimize systems, leverage data for strategic decisions, and utilize advanced analytics techniques to derive insights that drive business outcomes.
The Data Scientist will design and implement advanced machine learning models for real-time anomaly detection, automate data discovery from a global honeypot network, and enhance product features through AI/ML techniques. Collaboration with cross-functional teams and direct customer engagement will be essential.
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As a Senior Machine Learning Engineer at Enigma, you will develop scalable ML systems, enhance entity resolution algorithms, and improve machine learning infrastructure, directly impacting business decisions for customers. You will collaborate with data scientists and engineers to innovate and maintain high-performance systems.
The Head of Portfolio Risk Analytics will lead the team in developing and overseeing the portfolio risk analytics and monitoring frameworks, focusing on risk assessment, performance analysis, and compliance with regulations in consumer finance. Responsibilities include designing risk appetite frameworks, collaborating across functions, and implementing stress testing and sensitivity analysis.
As Head of Data Science, you will lead all aspects of data science including statistical methods and data analytics. You will oversee a team of data scientists, manage large complex datasets, and collaborate with product managers and engineering. Your role will enhance data-driven insights to inform decisions and drive product improvements.
As a Lead Data Scientist, you will focus on improving the Fingerprint Pro Identification Service by applying advanced data science and machine learning techniques. You will lead technical strategies, mentor engineers, and oversee data science projects from concept to deployment, integrating with the identification platform and enhancing the team’s technical expertise.
As a Data Scientist/Analyst at PayZen, you will develop statistical and machine learning models to improve patient engagement and financial processes. Responsibilities include collaborating with teams to optimize data strategies, designing AI/ML model infrastructure, and executing A/B tests to drive unbiased insights for better business outcomes.
The role involves using data analytics and machine learning to extract insights from data at a bank, improving efficiency and innovation. Responsibilities include data extraction, cleaning, developing data pipelines, conducting statistical analyses, and collaborating with stakeholders to add value from data.
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As a Data Science TLM Manager, you will shape Airbnb's search and personalization strategy by leading a team of data scientists, developing specialized models for the recommendation platform, and ensuring collaboration between the relevance team and product organization to enhance user experience.
The Machine Learning Research Scientist will develop and research state-of-the-art NLP models to enhance market research. The role involves predictive modeling, algorithm development for large-scale applications, and effective communication of technical concepts to non-technical stakeholders.
The role involves conducting research in AI and autonomous systems, focusing on developing robust models and techniques using driving data. The candidate will collaborate with research scientists and engineers, contributing to high-quality publications and real-world applications of autonomy technology.
Design, train, and deploy deep learning models utilizing extensive labeled and unlabeled data from a fleet of vehicles, focusing on generative and foundation models to advance autonomous driving capabilities.
As a Data Scientist at Adobe, you will prioritize and lead analytics initiatives, develop machine learning strategies, collaborate with product and marketing teams, conduct A/B experiments, and foster a data-driven culture to inform product roadmaps and business strategies.
The Lead ML Engineer will manage the deployment, monitoring, and management of machine learning models and data pipelines. Responsibilities include architecting end-to-end ML services, collaborating with cross-functional teams, and fine-tuning generative AI models for performance optimization.
The Staff Data Scientist will enhance data labeling capabilities at Pinterest by applying data science to improve throughput and quality of data collection. Responsibilities include identifying analytical opportunities, collaborating with engineers and customers, mentoring other data scientists, and evolving platform strategies in partnership with product and engineering leaders.
The Senior Data Scientist leads data science initiatives in healthcare, developing and implementing complex AI models. This role includes project management from design to deployment, collaboration with teams to ensure project success, and mentoring junior scientists. The position focuses on translating technical findings to actionable insights for stakeholders, optimizing models, and fostering a culture of data-driven innovation.
The Director of Data Science will lead the Data Science team, focusing on Big Data Analytics, AI, and ML to meet client needs. They will manage a portfolio of government projects, ensure team skills and resources are adequate, and drive operational policies and goals while maintaining high standards of service quality.
As a Data Scientist/Senior Data Scientist at Wisetack, you will drive data-driven decision-making in areas such as credit risk and fraud. Responsibilities include designing and deploying predictive models, conducting analyses, and collaborating with teams to integrate solutions into products, ultimately aiming to enhance consumer lending products.
The Senior Data Scientist will support product security efforts by developing machine learning solutions, building data pipelines, and integrating models into game development while collaborating with various departments. The role involves communicating analysis results to both technical and non-technical teams.
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