Senior AI Researcher

About Slice Global Equity

Slice Global Equity is transforming how companies manage equity, compliance, and tax across borders.

Headquartered in New York and Tel Aviv, with R&D based in Tel Aviv, and backed by TLV Partners, Insight partners, and Jibe Ventures.

We’re building an AI-Native platform that automates global compliance in real time.

We sit at the intersection of Legal-AI and fintech company, solving one of the hardest infrastructure problems in global business: real-time equity and compliance automation.

Join a high-energy, fast-growing company building the future of global equity management.

We’re looking for an experienced, hands-on Senior AI Researcher to lead research efforts at Slice and help shape the next generation of AI capabilities across our platform.

You’ll explore complex, domain-specific challenges, define research directions, and translate promising ideas into practical product capabilities. Your work will span applied research, experimentation, and model development—including building and adapting large and small language models using proprietary data, domain knowledge, and modern training techniques.

You’ll work with complex financial documents, structured and unstructured financial data, and legal and compliance data and knowledge, developing approaches that help AI interpret and reason across these sources.

Working closely with the CTO, engineering teams, and domain experts, you’ll have significant ownership over both the questions we investigate and the approaches we take. We’re looking for someone who combines strong research fundamentals with the drive to build, test, and bring ideas into production.

What You’ll Do

• Lead applied AI research efforts. Identify meaningful opportunities, shape the research roadmap, and drive initiatives from initial exploration through validation and implementation.
• Build and improve models. Develop, train, and adapt models using approaches such as fine-tuning, post-training, distillation, and other relevant techniques.
• Work with complex documents and data. Explore methods for understanding and reasoning over financial documents, structured business data, and legal and compliance information, in collaboration with domain experts.
• Drive rigorous experimentation. Formulate hypotheses, build datasets and evaluation frameworks, and systematically measure performance, reliability, and tradeoffs.
• Connect research to real-world applications. Collaborate with engineers and domain experts to turn research outcomes into scalable, production-ready capabilities.
• Advance our research practices. Evaluate emerging methods, share findings, and help establish strong standards for experimentation, model development, and responsible use of data.
  
  

• 6+ years of experience in AI/ML research or applied science, with a strong background in deep learning and recent hands-on experience with language models.
• Proven experience building, training, or adapting models, including ownership of data preparation, experimentation, and evaluation—not just integrating third-party model APIs.
• Strong proficiency in Python and modern deep learning frameworks, such as PyTorch, and familiarity with the language-model training ecosystem.
• A solid understanding of model architectures, optimization, training methodologies, and evaluation..
• Strong research judgment: the ability to frame ambiguous problems, design meaningful experiments, analyze results, and make evidence-based decisions.
• The ability to work independently, take technical ownership, and collaborate effectively with engineering, product, and domain experts.
  
  

• An M.Sc. or Ph.D. in Computer Science, Machine Learning, or a related field, or an equivalent research track record.
• Experience with advanced post-training methods, reinforcement learning, synthetic data, knowledge distillation, or efficient model training and inference.
• Experience in document understanding, information extraction, or reasoning across structured and unstructured data, particularly in financial, legal, or compliance domains.
• Research publications, meaningful open-source contributions, or a track record of bringing research into production.
  
  

• Build with purpose: Apply AI to solve global compliance at scale.
• High ownership: Work directly on product-critical systems with real customer impact.
• Technical depth: Work across architecture, UX, and infrastructure.
• Collaboration: Partner with top engineers and alumni of elite technology units.
• Team culture: Collaborative, ambitious, and impact-driven.
• Startup energy: Join a high-growth company backed by top investors, scaling globally, and redefining fintech infrastructure.