Applied AI ML - Sr. Associate - Machine Learning Engineer
LONDON, LONDON, United Kingdom
JPMorgan Chase & Co.
As a Sr. Associate - Machine Learning Engineer in the Applied AI ML team at JPMorgan Corporate Investment Bank, you will be at the forefront of combining cutting-edge AI techniques with the company's unique data assets to optimize business decisions and automate processes. You will have the opportunity to advance the state-of-the-art in AI as applied to financial services, leveraging the latest research from fields of Natural Language Processing, Computer Vision, and statistical machine learning. You will be instrumental in building products that automate processes, help experts prioritize their time, and make better decisions. We have a growing portfolio of AI–powered products and services and increasing opportunity for re-use of foundational components through careful design of libraries and services to be leveraged across the team. This role offers a unique blend of scientific research and software engineering, requiring a deep understanding of both mindsets.
Job responsibilities
- Build robust Data Science capabilities which can be scaled across multiple business use cases
- Collaborate with software engineering team to design and deploy Machine Learning services that can be integrated with strategic systems
- Research and analyse data sets using a variety of statistical and machine learning techniques
- Communicate AI capabilities and results to both technical and non-technical audiences
- Document approaches taken, techniques used and processes followed to comply with industry regulation
- Collaborate closely with cloud and SRE teams while taking a leading role in the design and delivery of the production architectures for our solutions.
Required qualifications, capabilities, and skills
- Hands on experience in an ML engineering role
- PhD in a quantitative discipline, e.g. Computer Science, Mathematics, Statistics
- Track record of developing, deploying business critical machine learning models
- Broad knowledge of MLOps tooling – for versioning, reproducibility, observability etc
- Experience monitoring, maintaining, enhancing existing models over an extended time period
- Specialism in NLP or Computer Vision
- Solid understanding of fundamentals of statistics, optimization and ML theory
- Extensive experience with pytorch, numpy, pandas
- Familiarity with popular deep learning architectures (transformers, CNN, autoencoders etc.)
- Excellent grasp of comp sci fundamentals and dev best practice
- Able to communicate technical information and ideas at all levels; convey information clearly and create trust with stakeholders.
Preferred qualifications, capabilities, and skills
- Experience designing/ implementing pipelines using DAGs (e.g. Kubeflow, DVC, Ray)
- Experience of big data technologies (e.g. Spark, Hadoop)
- Hands-on experience in implementing distributed/multi-threaded/scalable applications (incl. frameworks such as Ray, Horovod, DeepSpeed, etc.)
- Knowledge of open source datasets and benchmarks in NLP / Computer Vision
- Have constructed batch and streaming microservices exposed as REST/gRPC endpoints
- Familiarity with GraphQL
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.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Architecture Big Data Computer Science Computer Vision Deep Learning Engineering GraphQL Hadoop Horovod Kubeflow Machine Learning Mathematics Microservices ML models MLOps NLP NumPy Open Source Pandas PhD Pipelines PyTorch Research Spark Statistics Streaming Transformers
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