Applied AI ML [Multiple Positions Available]
J.P. Morgan
Software Engineering, Data Science
Palo Alto, CA, USA
USD 177,500-260k / year + Equity
DESCRIPTION:
Duties: Build and train production-grade Machine Learning on large-scale datasets to solve various business use cases for Global Banking. Use large scale data processing frameworks for feature engineering and be proficient across various data, both structured and unstructured. Use Deep Learning models and Generative AI techniques for solving various business use cases including multi-source data fusion, information retrieval, question-answering, forecasting and anomaly detection. Build ML models across Public and Private clouds including container-based Kubernetes environments. Develop end-to-end ML pipelines necessary to transform existing applications and business processes into robust Artificial Intelligence systems. Build both batch and real-time model prediction pipelines with existing application and front-end integrations. Collaborate to develop large-scale data modeling experiments, evaluating against strong baselines, and extracting key statistical insights and cause and effect relationships.
QUALIFICATIONS:
Minimum education and experience required: Bachelor's degree in Computer Engineering, Computer Science, Information Systems, Data Science, Artificial Intelligence, Machine Learning, or related field of study plus 3 years (36 months) of experience in the job offered or as Applied AI ML Associate, Software Engineer, Software Engineering Program Analyst, or related occupation. The employer will alternatively accept a Master's degree in Computer Engineering, Computer Science, Information Systems, Data Science, Artificial Intelligence, Machine Learning, or related field of study plus 1 year (12 months) of experience in the job offered or as Applied AI ML Associate, Software Engineer, Software Engineering Program Analyst, or related occupation.
Skills Required: This position requires experience with the following: Managing and transforming structured and unstructured data using Pythonic implementations of normalization, aggregation, data cleaning, enrichment, entity extraction, or sentiment analysis; Validating data quality and completeness; Implementing anomaly detection methods and building robust, reusable features for AI/ML teams; Conducting data analysis, modeling, and engineering tasks required in end-to-end AI/ML solution design and implementation using Python, SQL, and PySpark; Exploring, visualizing, and analyzing data to create and validate proof-of-concept AI/ML engineering solutions; Applying machine learning and statistical techniques including regression, classification, clustering, time series analysis, dimensionality reduction, and mathematical optimization to address business challenges; Developing, evaluating, and training AI/ML models, taking ownership of the full implementation lifecycle including hyperparameter tuning, model selection, and performance evaluation; Writing optimized code in SQL, Spark, and Python leveraging cloud technologies and cloud analytics platforms; Designing and deploying scalable, distributed statistical and AI/ML models in production environments using containerization and CI/CD pipelines; Building and maintaining end-to-end data pipelines for model deployment and operationalization, with embedded performance monitoring; Communicating, documenting, and presenting machine learning results and insights to non- technical audiences; Delivering AI/ML solutions with cross-functional teams such as Data Engineering, Model Operations, or Design; Conducting research and development work to evaluate emerging AI/ML methods, algorithms, and best practices; Translating academic research into use- case specific implementations and practical solutions; Demonstrating proficiency in MLOps practices, model monitoring, automated retraining, data privacy and compliance in AI/ML workflows, version control systems, RESTful API development for model serving, distributed computing frameworks, and experiment tracking tools.
Job Location: 3223 Hanover St, Palo Alto, CA 94304.
Full-Time. Salary: $177,500 - $260,000 per year.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
J.P. Morgan’s Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.
Build and train production-grade Machine Learning on large-scale datasets to solve various business use cases for Global Banking. Use large scale data processing frameworks for feature engineering and be proficient across various data, both structured and unstructured.