Research & Model Intelligence Lead
Ant-Tech · United States
- Hybrid
- Full-time
- $180,000 / year
- United States
Job highlights
- Lead AI research and model intelligence systems.
- Manage and scale a team of specialists.
- Bridge model research and trading performance.
- Partner with senior trading and platform leads.
- Architect for high-performance AI inference.
About the role
Research & Model Intelligence Lead
Ant-Tech is seeking a Research & Model Intelligence Lead to take full ownership of the systems and science powering our competitive edge. This senior leadership role involves overseeing model research, fine-tuning, inference optimization, evaluation frameworks, and the AI agent intelligence layer. You will lead a team of ML engineers, NLP specialists, and quantitative researchers, setting research direction, managing end-to-end evaluation cycles, and ensuring model improvements translate to trading performance.
What You'll Do
- Own the research and model intelligence domain, including fine-tuned LLMs, prompt engineering pipelines, NLP signal extraction, agent architectures, and evaluation infrastructure.
- Lead and scale a team, acting as a people leader by setting research direction, coaching performance, and creating clarity.
- Bridge research and production by owning the full pipeline from hypothesis through evaluation to production serving, ensuring speed, reliability, and measurability.
- Partner with senior stakeholders including trading strategists, execution engineers, and platform leads, translating trading needs into research priorities.
- Build with an AI-first mindset, leveraging LLMs and agentic workflows to accelerate research processes.
- Architect for inference quality and speed, designing observable, reproducible, and performance-optimized model serving, evaluation, and agent orchestration systems.
- Set the standard for how the team designs experiments, evaluates results, reviews model performance, and ships to production.
About You
- Strong people leader with experience running technical teams through real delivery.
- Systems thinker who understands the full lifecycle from research to production inference.
- Comfortable operating in ambiguity and bringing structure through experimentation.
- Strong project management instincts, able to sequence research bets and manage evaluation cycles.
- AI-native and scrappy, focused on leverage, automation, and modern tooling.
- Values rigor, measurable outcomes, and impact over complexity or consensus.
Experience That Helps
- Leading ML, NLP, or research teams shipping models in high-stakes or latency-sensitive environments.
- Ownership of evaluation and continuous monitoring systems for deployed models.
- Experience with LLM fine-tuning, prompt optimization, inference serving, or agent architectures.
- Strong applied ML or quantitative research background with a focus on production deployment.
- Curiosity, learning speed, and ownership mindset.
Nice to Have
- Managing senior cross-functional stakeholders and driving alignment.
- Background in trading, finance, crypto, or similar domains.
- Familiarity with NLP for news or event-driven signal extraction.
- Ideally based within ±3 hours of EST.
Compensation & Package
Base Salary + Benefits Package + Performance related bonus (TBD on %)
Key skills/competency
- Research & Model Intelligence Lead
- Machine Learning
- NLP
- Quantitative Research
- AI Agents
- LLM Fine-tuning
- Prompt Engineering
- Inference Optimization
- Evaluation Frameworks
- Team Leadership
Skills & topics
- Research & Model Intelligence Lead
- Machine Learning
- NLP
- Quantitative Research
- AI Agents
- LLM
- Prompt Engineering
- Inference Optimization
- Evaluation
- Team Leadership
- Python
- Cloud
- Finance
- Trading
- Deep Learning
How to get hired
- Tailor your resume: Highlight leadership, ML, NLP, and production systems experience.
- Showcase AI-native skills: Emphasize LLM, agentic workflows, and automation experience.
- Demonstrate impact: Quantify achievements in model deployment and performance improvement.
- Prepare for technical interviews: Focus on ML, NLP, inference, and evaluation concepts.
- Understand the culture: Research Ant-Tech's focus on rigor and measurable outcomes.
Technical preparation
Master LLM fine-tuning and prompt engineering techniques.,Practice designing and implementing agent architectures.,Optimize inference serving for speed and quality.,Develop robust model evaluation frameworks.
Behavioral questions
Describe a time you led a technical team.,How do you handle ambiguity in research?,Explain your approach to ML project management.,How do you foster AI-native practices in a team?
Frequently asked questions
- What is the primary focus of the Research & Model Intelligence Lead role at Ant-Tech?
- The primary focus is to lead the research and development of AI models and intelligence systems that support Ant-Tech's investment activities, ensuring their performance and integration into trading strategies.
- What kind of team will the Research & Model Intelligence Lead manage at Ant-Tech?
- You will lead a team comprising ML engineers, NLP specialists, and quantitative researchers, guiding their research direction and development efforts.
- How does Ant-Tech measure the success of model improvements in this role?
- Success is measured by how effectively model improvements translate into tangible trading performance, emphasizing rigor, measurable outcomes, and impact.
- What AI-native capabilities are most valued for the Research & Model Intelligence Lead position?
- Ant-Tech highly values individuals who instinctively use LLMs and modern tooling to accelerate experimentation, evaluation, and iteration, creating leverage through models and people.
- Is financial market experience a strict requirement for the Research & Model Intelligence Lead role?
- No, financial market experience is not a strict requirement. The role values curiosity, learning speed, and an ownership mindset over domain-specific experience.
- What are the key responsibilities regarding production deployment for this role?
- You will own the full pipeline from hypothesis through evaluation to production serving, ensuring that models are deployed quickly, reliably, and measurably.
- What is the ideal location for the Research & Model Intelligence Lead candidate?
- While not strictly required, candidates ideally located within ±3 hours of EST are preferred.
- What technical areas are critical for success in this Research & Model Intelligence Lead role?
- Key technical areas include LLM fine-tuning, prompt optimization, inference serving, agent architectures, and evaluation frameworks for ML and NLP models.