Sr Machine Learning Python / DevOps Engineer

Morgan Stanley•New York, NY

1d•$150,000 - $210,000

About The Position

In the Technology division, we leverage innovation to build the connections and capabilities that power our Firm, enabling our clients and colleagues to redefine markets and shape the future of our communities. This is a Lead Software Engineering position at the Vice President level, which is part of the job family responsible for developing and maintaining software solutions that support business needs. Morgan Stanley is an industry leader in financial services, known for mobilizing capital to help governments, corporations, institutions, and individuals around the world achieve their financial goals. Interested in joining a team that’s eager to create, innovate and make an impact on the world? Read on. The Machine Learning Research group is looking for a senior ML developer and DevOps/MLOps engineer. An individual who can sit down with fellow developers, ML researchers, and clients to build out and deploy bespoke but maintainable solutions across heterogeneous technologies. What you’ll do in the role: Work with ML researchers to develop, productionize, and deploy ML based project for clients. Dig into client systems to help retrofit with the team’s creations. Maintain and support production systems. Onboard and develop systems to help us manage and reuse ML models across the Firm with a focus on ML Ops. Hack away at compiling and repackaging tricky libraries used by researchers. Find tooling and platform solutions to real-world problems and bring them into the Firm as quickly as possible with adherence to our security policies. Remain up to date on ML tools, libraries, and techniques across the Open Source and vendor landscape. Build and maintain tooling and systems to promote ML development within the firm. Create and maintain code samples to bootstrap ML practitioners so that the work done to help one team will help the next. What you’ll bring to the role: Minimum 10 years of related technology experience. Strong background in Python and Java/C/C++. Development, packaging, patching, etc. Debugging, profiling, and performance engineering. Familiarity with trading systems. Understanding of core infrastructure (hardware and software) and how it can be used to make our job easier, e.g., processor architectures, memory, load balancers, reverse proxies, automation frameworks, etc. Experience with container technologies (Docker, podman, buildah, Kubernetes, etc.) Both with regards to packaging and runtime. At least some familiarity with cloud and cloud enablement technologies (AWS, Azure, Terraform.) Understanding how to use modern and traditional data tiers, e.g., relational databases, object stores, graph databases. Experience designing ETL pipelines. Linux (system level understanding, building software, debugging, etc.) Self-starter capable of taking an idea and seeing it all the way from research to execution. Ability to clearly illustrate complex ideas using documentation and diagrams.

Requirements

Minimum 10 years of related technology experience.
Strong background in Python and Java/C/C++.
Development, packaging, patching, etc.
Debugging, profiling, and performance engineering.
Familiarity with trading systems.
Understanding of core infrastructure (hardware and software) and how it can be used to make our job easier, e.g., processor architectures, memory, load balancers, reverse proxies, automation frameworks, etc.
Experience with container technologies (Docker, podman, buildah, Kubernetes, etc.) Both with regards to packaging and runtime.
At least some familiarity with cloud and cloud enablement technologies (AWS, Azure, Terraform.)
Understanding how to use modern and traditional data tiers, e.g., relational databases, object stores, graph databases.
Experience designing ETL pipelines.
Linux (system level understanding, building software, debugging, etc.)
Self-starter capable of taking an idea and seeing it all the way from research to execution.
Ability to clearly illustrate complex ideas using documentation and diagrams.

Nice To Haves

Dask, Ray, Spark, and other distributed compute tech.
OSS development.
Mathematics, e.g., statistics, linear algebra.
Background in data science.
Knowledge of the latest deep learning deployment technologies (jax, openxla, etc.)
Knowledge of model monitoring tools (mlflow, tensorboard, etc.)
Experience with CUDA, ROCm, etc.
Exposure to the financial sector (equities, fixed income, etc.)
Knowledge of GitHub (general PR workflow, Actions, etc.)

Responsibilities

Work with ML researchers to develop, productionize, and deploy ML based project for clients.
Dig into client systems to help retrofit with the team’s creations.
Maintain and support production systems.
Onboard and develop systems to help us manage and reuse ML models across the Firm with a focus on ML Ops.
Hack away at compiling and repackaging tricky libraries used by researchers.
Find tooling and platform solutions to real-world problems and bring them into the Firm as quickly as possible with adherence to our security policies.
Remain up to date on ML tools, libraries, and techniques across the Open Source and vendor landscape.
Build and maintain tooling and systems to promote ML development within the firm.
Create and maintain code samples to bootstrap ML practitioners so that the work done to help one team will help the next.

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