Senior AI Software Engineer at Arrowstreet Capital

Boston, Massachusetts, United States

Arrowstreet Capital Logo
Not SpecifiedCompensation
Senior (5 to 8 years)Experience Level
Full TimeJob Type
UnknownVisa
FinanceIndustries

Requirements

  • Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field
  • Minimum of 5 years of programming experience, including substantial hands-on experience with Python
  • Demonstrated expertise in designing and deploying machine learning, GenAI, or LLM-based solutions
  • Strong analytical, problem-solving, and critical-thinking skills
  • Experience with integrating structured and unstructured data sources into AI/ML models
  • Familiarity with standard AI/ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face)
  • Experience developing and maintaining data pipelines and RESTful APIs
  • Strong background in data structures, algorithms, and layered architecture
  • Experience with container technologies such as Docker, Kubernetes
  • Experience with AWS or other public cloud services, especially for deploying AI/ML workloads

Responsibilities

  • Design, develop, and deploy Agentic AI solutions leveraging industry-standard AI frameworks
  • Integrate diverse structured and unstructured data sources into GenAI models to broaden functionality and deliver actionable insights
  • Utilize MCP tooling to ingest disparate data sources for agent-driven applications
  • Collaborate with cross-functional teams (Product, Data, Engineering, Compliance) to scope and implement new GenAI use cases and solutions
  • Develop, test, and maintain robust APIs and data pipelines for efficient data ingestion, transformation, and output within GenAI frameworks
  • Continuously monitor advancements in Generative AI and Large Language Models (LLMs) to proactively recommend and implement relevant updates
  • Document AI system designs, experiments, code, and processes for reproducibility, transparency, and knowledge sharing across the team
  • Optimize GenAI and agentic solutions for performance, scalability, and responsible/ethical AI practices
  • Write unit tests and perform code reviews to ensure high quality, reliable, and maintainable code
  • Stay current with emerging trends and technologies in GenAI, Agentic AI, and related fields, applying them to enhance projects and infrastructure

Skills

GenAI
Agentic AI
LLMs
AI frameworks
data pipelines
APIs
MCP tooling
unit tests
code reviews

Arrowstreet Capital

Investment management for global equity strategies

About Arrowstreet Capital

Arrowstreet Capital specializes in managing global and international equity investments for institutional clients, including pension plans and foundations. Their investment strategies include long-only, alpha extension, and long/short approaches, utilizing various financial instruments like swaps and futures. The company employs quantitative methods to analyze investment signals and develop proprietary models for return, risk, and transaction costs. This structured investment process aims to create diversified equity portfolios that seek to outperform specific benchmarks by identifying opportunities across different companies, sectors, and countries. With around $100 billion in assets under management, Arrowstreet Capital serves over 200 clients across North America, Europe, and the Asia-Pacific region.

Boston, MassachusettsHeadquarters
1999Year Founded
SECONDARY_PRIVATECompany Stage
Quantitative Finance, Financial ServicesIndustries
201-500Employees

Risks

Indictment of former executive for trade secrets theft may impact client trust.
Market volatility and geopolitical tensions could affect portfolio performance.
Rise of passive investment strategies may increase competition and pressure on fees.

Differentiation

Utilizes quantitative methods for investment signals in proprietary models.
Manages $100 billion for over 200 global clients.
Offers diverse equity strategies including long-only, alpha extension, and long/short.

Upsides

Advancements in AI enhance quantitative model capabilities for market trend prediction.
Increased interest in ESG investing can be leveraged by integrating ESG metrics.
Thematic investing trends offer opportunities for specialized equity strategies.

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