Applied AI ML [Multiple Positions Available]

JPMorgan Chase & Co.; Palo Alto (CA); On-site; USD 215,000 - 260,000; Full time

Job summary

JPMorgan Chase & Co. in Palo Alto seeks an AI/ML expert to design automation-based data analysis systems and production-ready NLP and speech recognition models.

You will research ML methods, develop models for NLP, speech, time-series, reinforcement learning and recommendations, and collaborate across teams to deploy solutions at scale. Responsibilities include building large-scale frameworks to accelerate model deployment across business areas and applying ML techniques to solve real-world

Qualifications

• Master's degree plus 1 year of relevant experience in Applied AI/ML, Software Engineering, or R&D.
• Experience deploying NLP and speech recognition systems in production.
• Experience with ML experimentation, model training, evaluation and monitoring.
• Proficiency with distributed DL, data processing and model training at scale.

Responsibilities

• Design and develop automation-based systems for data analysis, document management and client intelligence.
• Research ML methods for data processing and analysis.
• Develop ML models for NLP, speech, time-series, RL, and recommendations.
• Collaborate with partner teams to deploy solutions into production.
• Develop large-scale ML frameworks to accelerate model deployment across business areas.

Skills

NLP systems

Speech recognition

ML frameworks

Distributed DL

AWS/GCP/Azure

PyTorch

TensorFlow

HuggingFace

Keras

Spark

Education

Master's degree in Computational Data Science, Computer Science, Electrical Engineering, Mathematics, Operations Research, Data Science or related field

Tools

Docker

Job description

DESCRIPTION:

Duties: Design and develop automation-based systems for data analysis, document management, and client intelligence. Research machine learning methods for data processing and analysis. Develop machine learning models to solve real-world problems and apply it to tasks such as Natural Language Processing, speech recognition, time-series predictions, reinforcement learning, and recommendation systems. Collaborate with multiple partner teams to deploy solutions into production. Develop large-scale frameworks to accelerate the application of machine learning models across different areas of the business.

QUALIFICATIONS:

Minimum education and experience required: Master's degree in Computational Data Science, Computer Science, Electrical Engineering, Mathematics, Operations Research, or Data Science or related field of study plus 1 year of experience in the job offered or as Applied AI ML, Software Engineer, Analyst in Engineering Division, Research and Development Team, or related occupation.

Skills Required: This position requires experience with the following: Developing and deploying production-ready NLP and speech recognition systems; Designing and developing machine learning experiments and frameworks, including data ingestion, augmentation, generation, feature extraction, end-to-end model training, fine-tuning, performance evaluation and monitoring, robustness and integration testing, A/B testing, interpretability analysis, and visualization; Outlining and evaluating intrinsic and extrinsic model performance metrics aligned with business goals, including deriving extrinsic KPIs incorporating cost per model and cost savings from automation; Preparing and analyzing data quality metrics, including completeness and consistency; Using distributed deep learning, data processing, and model training frameworks, including PyTorch, TensorFlow, HuggingFace, Keras, SKlearn, NLTK, spaCy, Rasa NLU, Numpy, Pandas, and Spark; Processing large datasets, training scalable models, and deploying models on multiple CPUs and GPUs using Cloud-Native Managed Services including as AWS, Azure, and GCP; Applying machine learning techniques, including logistic regression, gradient-boosted trees, and deep learning approaches including RNN and CNN to NLP and Speech applications; Employing Elasticsearch inverted-index, BM25 ranking, and custom analyzers; Leveraging pretrained Transformer encoders including BERT, Sentence-Transformers, and spaCy for text understanding and extraction; Performing A/B testing and data-driven product development, including hypothesis and metrics design, randomization, statistical testing, multiple comparisons, error controls, and experiment monitoring; Implementing continuous integration for ML models, including pipeline definition and triggering using Jenkins, GitLab CI/CD, GitHub Actions; Artifact packaging, containerizing and deployment using Docker; Developing unit tests for ML, including module-level test cases, edge-case and invariant tests, coverage and static analysis, and test parameterization and fixtures; Automating hyperparameter searches using TensorFlow, PyTorch, and Keras; Tracking training runs and metrics using TensorBoard.

Job Location: 3203 Hanover St, Palo Alto, CA 94304.

Full-Time. Salary: $215,000 - $260,000 per year.