Full-Time

Principal Software Engineer

AI Platform, Parametric

Updated on 9/4/2026

Morgan Stanley

Morgan Stanley

10,001+ employees

Global financial services; wealth management

Compensation Overview

$115k - $225k/yr

+ Annual incentive compensation + Discretionary bonus

Company Does Not Provide H1B Sponsorship

Minneapolis, MN, USA

Hybrid

Three days on-site per week required.

Bachelor's, Master's

Category
Software Engineering (2)
,
Required Skills
LLM
Datadog
Agile
Python
GitHub Actions
Software Testing
TensorFlow
Git
PyTorch
Machine Learning
OpenAI
Docker
RAG
AWS
Terraform
REST APIs
LangChain

Get referred to Morgan Stanley

See people who can refer or advise you

Requirements
  • A bachelor's degree in Computer Science, Machine Learning, or a related field of study is required.
  • At least 10 years of hands-on object-oriented design and development experience is required.
  • At least 3 years of hands-on Python software design and development experience is required.
  • At least 3 years of experience managing software development teams and providing direct guidance, mentorship, project leadership, and team development while fostering an engaging, collaborative environment and building strong cross-team relationships is required.
  • Experience translating business problems into technical requirements, assessing data readiness, and scoping feasible artificial intelligence solutions.
  • Demonstrated experience with the artificial intelligence development lifecycle, including problem framing, data assessment, model selection, prompt engineering, evaluation metrics design, discriminative testing, regression testing, bias testing, and deployment.
  • Strong conceptual and practical understanding of machine learning, natural language processing, transformer architectures, and large language models, including model architectures, training and fine-tuning paradigms, evaluation methodologies, and common failure modes.
  • Proficiency with large language model application programming interfaces, including OpenAI, Azure OpenAI, Anthropic, and open-source model serving, and frameworks such as LangChain or LlamaIndex.
  • Familiarity with machine learning and deep learning frameworks such as PyTorch, TensorFlow, or Hugging Face Transformers.
  • Proven experience building cloud-based solutions, preferably on Amazon Web Services.
  • Experience with microservice architectures, event-driven architectures, containerization using Docker, and Representational State Transfer application programming interface design.
  • Experience with retrieval-augmented generation architectures, vector databases, and retrieval-augmented workflows.
  • Experience maintaining high-quality codebases and developing and enforcing strong development standards, including Agile development practices, code reviews, and Git-based version control.
  • Experience using artificial intelligence coding tools such as GitHub Copilot or Claude Code.
Responsibilities
  • Provide technical leadership by directly managing a team of engineers, guiding and mentoring them to achieve their highest potential while collectively advancing the goals and projects of the engineering team.
  • Drive vision and strategy for the team while exemplifying leadership through communication, consensus building, elevated standards, ownership, accountability, and project management.
  • Lead generative artificial intelligence development projects across the development lifecycle, from requirements gathering to deployment, bug fixes, enhancements, and escalated support.
  • Work directly with business users and engineering teams to build relationships, understand requirements, scope solutions, and communicate technical trade-offs in accessible terms.
  • Design, develop, and maintain high-quality, flexible platform-level technical solutions to generative artificial intelligence engineering problems, including reusable components, governance processes, model evaluation, services, and application programming interfaces.
  • Design, develop, and maintain platform-level generative artificial intelligence applications using prompt engineering, retrieval-augmented generation pipelines, fine-tuning, and agentic workflows.
  • Translate business problems into technical solutions by assessing data availability, feasibility, and alignment with generative artificial intelligence capabilities.
  • Support the artificial intelligence development lifecycle end-to-end, including problem scoping, data assessment, solution design, implementation, evaluation, deployment, and iteration.
  • Design and execute artificial intelligence system evaluation frameworks, including quantitative metric definition, discriminative testing such as A/B testing and sensitivity analysis, regression testing, and continuous quality monitoring of generative artificial intelligence outputs.
  • Apply knowledge of machine learning, natural language processing, transformer architectures, and large language model internals, including tokenization, attention, decoding strategies, and fine-tuning paradigms, to select, adapt, and optimize foundation models such as OpenAI, Llama, and open-source models.
  • Conduct code and design reviews for developers working on generative artificial intelligence solutions, ensuring adherence to engineering best practices and evaluation standards.
  • Implement and manage cloud deployments using Amazon Web Services services, ensuring high availability, scalability, and observability.
  • Maintain and enhance existing artificial intelligence applications to improve model performance, reliability, and user experience through data-driven iteration.
  • Follow generative artificial intelligence and related technology trends and recommend improvements to systems when appropriate.
Desired Qualifications
  • A master's degree is preferred.
  • Financial services industry experience.
  • Experience building libraries, low-level services, and other shared platform-level components.
  • Experience deploying resources using infrastructure as code technologies such as Terraform.
  • Experience developing continuous integration and continuous delivery pipelines using tools such as GitLab CI or GitHub Actions.
  • Familiarity with observability and monitoring of artificial intelligence systems in production, such as Datadog.
  • Experience building evaluation and benchmarking frameworks for generative artificial intelligence outputs, including golden sets, human-in-the-loop review, and automated scoring.
  • Experience presenting technical contributions and innovations to technical colleagues.

Morgan Stanley is a global financial services firm offering investment banking, securities, wealth management, and investment management services to individuals, families, institutions, and governments. It helps clients raise, manage, and distribute capital through advisory services, asset management, trading, and financing activities, with revenue from advisory fees, asset management fees, trading commissions, and interest income. The company differentiates itself through its large, worldwide platform that provides a full suite of services across markets and client segments, a focus on client needs and long-term relationships, and a strong emphasis on institutional expertise and capital markets capabilities. Its goal is to help clients achieve their financial objectives by delivering tailored financial solutions and maintaining enduring client partnerships.

Company Size

10,001+

Company Stage

IPO

Headquarters

New York City, New York

Founded

1935

Get referred to Morgan Stanley

See people who can refer or advise you

Simplify Jobs

Simplify's Take

What believers are saying

  • Q2 2026 revenue hit $21.35 billion, driven by 69% equities growth and 58% banking growth.
  • Wealth Management added $148 billion net new assets; over half came from IPO workplace flows.
  • Morgan Stanley won Anthropic and OpenAI underwriting roles as AI financing accelerates.

What critics are saying

  • Reuters reported a May 2026 FINRA probe into Morgan Stanley's Budapest bankers and licensing controls.
  • Fee dependence on IPOs leaves wealth inflows vulnerable when 2026 issuance cools.
  • A large client-supervision scandal would shatter the wealth franchise that drives valuation.

What makes Morgan Stanley unique

  • Ted Pick's integrated model links IPO origination to wealth flows, reaching $10 trillion in July 2026.
  • Morgan Stanley at Work opens ShareWorks to AI agents, a first-mover platform advantage.
  • Its equities franchise monetizes AI volatility, delivering $6.3 billion trading revenue in Q2 2026.

Help us improve and share your feedback! Did you find this helpful?

Benefits

Health Insurance

Dental Insurance

Vision Insurance

Life Insurance

Disability Insurance

Health Savings Account/Flexible Spending Account

Unlimited Paid Time Off

Paid Vacation

Paid Sick Leave

Paid Holidays

Hybrid Work Options

401(k) Retirement Plan

401(k) Company Match

Mental Health Support

Wellness Program

Company News

Yahoo Finance
Sep 5th, 2026
Morgan Stanley raises Snowflake price target to $470 on AI-powered growth

Morgan Stanley analyst Sanjit Singh raised his price target on Snowflake to $470 from $300, maintaining an "Overweight" rating. The cloud data company is valued at $106 billion market cap, with shares trading at $356. The upgrade follows Snowflake's third consecutive quarter of accelerating revenue growth. Product revenue reached $1.49 billion, up 37% year over year, beating guidance and consensus estimates. The company added 692 net new customers, up 32% year over year, bringing its total to 14,554. Customers spending over $1 million annually increased 27% to 828. Singh attributed the momentum to an "AI-powered growth flywheel" driven by Snowflake's CoCo and CoWork tools. Management estimates AI products contributed roughly half of the quarter's growth acceleration.

Newsbytes
Sep 4th, 2026
Anthropic secures $15B credit facility, expects $65B revenue ahead of IPO

Anthropic, the developer behind Claude AI chatbot, has secured a $15 billion credit facility ahead of its initial public offering. Major financial institutions including Morgan Stanley, Goldman Sachs, JPMorgan Chase, and Citigroup are backing the arrangement. The facility significantly exceeds last year's $2.5 billion loan and surpasses the company's roughly $10 billion target. Anthropic now expects over $65 billion in annualised revenue, representing more than a sevenfold increase from its pace at the end of last year. Additional banks including Barclays and Bank of America have joined the arrangement. The timing coincides with renewed activity in the US IPO market, positioning Anthropic for a substantial market debut.

TechCrunch
Sep 2nd, 2026
HiddenLayer nabs $100M as enterprises rush to secure their AI deployments | TechCrunch

Security companies are scrambling to build products that can monitor not just agents but also the tools and add-ons they use.

PR Newswire
Sep 2nd, 2026
HiddenLayer raises $100M Series B to secure AI agents and autonomous coding systems

HiddenLayer, an AI security company, has raised $100 million in Series B funding led by Delta-v Capital. Participants include Ten Eleven Ventures, Morgan Stanley, M12, and Booz Allen Ventures. The Austin-based firm secures agentic, generative, and predictive AI applications. It will use the capital to expand its enterprise platform, including Agentic Runtime Security and Agent Harness Security, which protects autonomous coding agents at runtime. HiddenLayer's annual recurring revenue grew more than tenfold, and it signed over 50 new platform customers across sectors including banking, pharmaceuticals, and US defence. The company supports a frontier model provider serving more than 700 million weekly users. HiddenLayer's research team holds 39 granted patents and 65 pending patents in adversarial detection and model protection. The firm recently appointed Mike Gesnaldo as chief revenue officer.

Third News
Sep 1st, 2026
Manulife Financial Announces Pricing of $750 Million U.S. Public Offering of Subordinated Notes - Third News

Manulife Financial Corporation reveals its plans for a $750 million public offering of subordinated notes, aimed to strengthen regulatory capital.