Location: Remote - US (EST is the preference)
Remote | Full-time
Compensation: $200K - $275K
We are hiring on behalf of our client who is operating in the quantitative trading and investment sector, they are seeking a Research & Model Intelligence Lead. This is a senior leadership position with comprehensive ownership of the systems and science powering the organization’s competitive edge. The role involves overseeing model research, fine-tuning, inference optimization, evaluation frameworks, and the AI agent intelligence layer.
The successful candidate will lead a multidisciplinary team of Machine Learning (ML) engineers, Natural Language Processing (NLP) specialists, and quantitative researchers. This individual will be responsible for setting the research direction and ensuring that advancements in model performance translate directly into measurable trading results. The position requires a systems-oriented leader who utilizes modern AI tooling to accelerate experimentation and iteration while maintaining rigorous scientific standards.
Key Responsibilities
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Domain Ownership: Take end-to-end responsibility for systems generating and serving trading intelligence, including fine-tuned LLMs, prompt engineering pipelines, NLP signal extraction, and agent architectures.
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Team Leadership: Lead and scale a technical team; establish research priorities, coach performance, and facilitate clear communication across ML engineering and quantitative research functions.
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Research-to-Production Integration: Bridge the gap between hypothesis and deployment. Manage the full pipeline from initial research through evaluation to production serving, ensuring reliability and speed.
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Stakeholder Partnership: Collaborate with senior trading strategists, execution engineers, and platform leads to align research priorities with evolving trading needs.
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Workflow Optimization: Implement AI-native and agentic workflows to accelerate research processes, including experiment design, evaluation automation, and knowledge management.
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System Architecture: Design model serving and agent orchestration systems that are observable, reproducible, and optimized for both capability and performance.
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Operational Standards: Define the technical and operational standards for experiment design, result evaluation, and production deployment.
Requirements
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Technical Leadership: Proven experience leading technical teams through complex delivery cycles and managing senior cross-functional stakeholders.
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Systems Expertise: Deep understanding of the full lifecycle from initial research to production inference and continuous monitoring.
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Execution Mindset: Strong project management instincts with the ability to sequence research bets and manage rigorous evaluation cycles.
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Technical Proficiency: Professional experience with LLM fine-tuning, prompt optimization, and inference serving (such as quantization or GPU orchestration).
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Problem-Solving: Ability to operate effectively in ambiguous environments by bringing structure through systematic experimentation.
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Educational/Professional Background: A strong background in applied ML or quantitative research with a focus on production deployment.
Benefits
- Competitive base salary.
- Comprehensive benefits package.
- Performance-related bonus.
Interview Process
The following stages outline the evaluation process for this role:
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Hiring Manager Interview: Initial screening to discuss the role and candidate background.
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Technical Assessment (Part I): Two technical discussions with three engineers who would serve as the role's future direct reports.
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Technical Assessment (Part II): A continuation of technical deep dives with the engineering team.
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Final Interview: A concluding session with the Head of Strategy to discuss long-term alignment and organizational goals.