Design and build solutions that enable investment professionals to effortlessly extract insights from large, complex structured and unstructured data sets by building robust end-to-end data pipelines and tools.
Compensation
The annual base salary range for this role is $250,000-$300,000, which does not include discretionary bonus compensation or our comprehensive benefits package.
Responsibilities
Develop and support big data processing pipelines including ingestion, transformation, and end-customer delivery
Build out cloud-based infrastructure using distributed techniques for other data engineers / data scientists / researchers
Be at the forefront of new technology developments with respect to the handling and processing of big data and conduct proof of concepts evaluations of new technologies
Build and support visualization and exploration capabilities around our big data sets
Work with investment professionals, researchers, and data scientists to design, build, and launch robust end-to-end data pipelines
Develop and improve counterparty credit risk models, investigate derivatives pricing, source and analyse trade and market data, develop production implementations in Python, interpret regulatory requirements, write documentation and support validation and approval processes.
Responsibilities
Develop and improve counterparty credit risk models
Investigate derivatives pricing and exposure behaviour
Source, reconcile and analyse trade and market data
Develop production implementations in Python
Interpret relevant regulatory requirements
Requirements
Bachelor's or higher degree in mathematical finance, mathematics, statistics, physics, engineering or econometrics
Design, build, and maintain data lineage and Gen AI/LLM-powered systems for large, multi-language codebases, owning backend services, deployment pipelines, and user-facing interfaces.
Compensation
$94,300.00 - $141,500.00
Responsibilities
Design and build engines that parse multiple programming and query languages to extract accurate, column-level data lineage.
Build Gen AI/LLM-powered retrieval and assistant capabilities on top of lineage and metadata.
Develop backend services and interfaces that expose them, from data processing through to user-facing views.
Own the delivery pipeline—build, test, deployment, and production support across environments.
Translate ambiguous requirements into clean, efficient, well-structured designs.
Requirements
Bachelor’s degree/University degree or equivalent experience
The Software Engineer will operate in a fast‑paced innovation environment, rapidly developing and validating proof‑of‑concepts for advanced AI and machine learning use cases.
Compensation
$86,000 - $138,000. This represents the typical salary range for this position. Salary is determined by various factors, including but not limited to, the scope and responsibilities of the position, the individual’s experience, education, knowledge, skills, and competencies, as well as geographic location and business and contract considerations. Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay.
Responsibilities
Hands‑on data exploration
Prototyping
Workflow automation
Contributing to secure, well‑governed MLOps practices
Evaluating emerging tools
Requirements
Bachelor's degree in Computer Science or related field
Master's degree in Computer Science or related field
The role involves defining methodologies, tools, and automation for automotive microcontroller software development and validation, focusing on Python-based frameworks and GenAI solutions.
Responsibilities
Define and evolve development and validation methodologies for automotive microcontroller software, and turn them into practical tools, templates, and automated workflows.
Gather requirements from software, validation, and platform teams, and translate them into architecture, detailed design, and high-quality implementation.
Design, develop, and maintain Python-based frameworks, CLI tools, libraries, and services that automate build, configuration, code generation, test, and validation activities.
Design and build agentic AI pipelines that automate engineering tasks
Build GenAI solutions using prompt engineering, retrieval-augmented generation (RAG) over specifications and code bases, embeddings and vector search, and tool-calling and multi-agent orchestration.
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