Research Engineer
Job Summary
This founding research engineer role sits at the intersection of cutting-edge AI research and production software systems for financial applications. You will partner directly with founders to define a multi-year research agenda, develop formally verifiable accounting logic, and build trustworthy agentic systems that meet audit-grade standards. The position demands both deep theoretical grounding in machine learning and the engineering skill to ship production infrastructure that finance teams can rely on.
Essential Functions
- Collaborate with founders to shape the long-term research direction in finance AI and translate strategic theses into concrete research agendas
- Apply formal-verification techniques using tools such as Lean 4 or Catala to create provably correct representations of accounting standards
- Design evaluation frameworks, synthetic data generators, and reliability harnesses that ensure agent outputs meet audit-grade requirements for groundedness and correctness
- Bridge research prototypes to production by implementing training and inference infrastructure for large-scale deployment
- Design, train, and fine-tune large language models and agentic systems optimized for domain-specific financial tasks
- Architect scalable, distributed pipelines capable of supporting high-volume operational workloads
- Independently own and drive impactful research projects from ideation through to shipped features or publications
Required Qualifications
- PhD in Computer Science, Machine Learning, AI, NLP or a closely related field (completed or expected prior to start date)
- Strong publication record in top-tier AI venues including NeurIPS, ICML, ICLR, ACL or EMNLP
- Hands-on experience training and fine-tuning large language models with deep proficiency in Python and PyTorch
- Demonstrated software engineering ability to design and build complex production systems
- 1-3 years of applied AI or ML research engineering experience post-PhD
- Experience working with AI agents, agent reliability frameworks, or agentic systems
- Ability to work on-site in New York City five days per week
Preferred Qualifications
- Publications or production experience focused on LLM agents, tool use, planning, multi-step reasoning or orchestration
- Prior experience at an AI startup or in industrial NLP research
- Hands-on background with RLHF, DPO, model distillation or related optimization techniques
- PhD from a top-tier program such as CMU, Stanford, MIT, Berkeley or Harvard
- Experience building synthetic data pipelines or formal verification systems for domain-specific logic
Technical Skills
- Python
- PyTorch
- Large Language Models (LLMs)
- Lean 4
- Catala
- Retrieval-Augmented Generation (RAG)
- RLHF and DPO
- AI agent frameworks
- Formal verification methods
- Distributed training and inference infrastructure
Education & Certifications
PhD in Computer Science, Machine Learning, AI or a related technical field from a top-tier program is required. Degree must be completed or near completion prior to start date.
Compensation & Benefits
Base salary range of $220,000 to $270,000 depending on experience and qualifications. Relocation support is available for candidates outside the New York area.