Quantitative Software Engineer: Techniques Engineering

Position Summary

Two Sigma is a leading quantitative investment management and trading firm. The company applies a scientific approach to investing, combining cutting-edge technology, artificial intelligence, data science, and quantitative research with rigorous human inquiry to capitalize on market opportunities and deliver alpha for investors. Our team of engineers, quantitative researchers and data scientists looks beyond the traditional to test hypotheses and develop creative solutions to some of the world’s most complex economic problems. We’re seeking an experienced Machine Learning/AI Engineer who is passionate about bridging innovative research with production-level deployment.

Responsibilities

• Collaborate with ML researchers to explore model architectures, training methodologies, and experimental objectives
• Translate prototypes into reusable, maintainable, and production-ready code, ensuring research insights scale seamlessly into operation
• Design, implement, and optimize fully automated training pipelines and evaluation workflows
• Optimize models for production—improving efficiency through techniques like quantization, distillation, and intelligent batching
• Continuously assess and fine-tune both research and production workflows, integrating user feedback and automated monitoring systems
• Identify and implement opportunities for process improvements to reduce bottlenecks and improve overall modeler efficiency
• Provide technical guidance and mentorship to peers, ensuring the team uses new tools and techniques that drive innovation

Qualifications

• BS in Computer Science, Mathematics, Physics, or related technical subject area
• Minimum 1 year of experience required; 7-15 years of experience preferred
• Strong software engineering skills using Python, version control, testing frameworks, and CI/CD practices
• Ability to work across functions and influence technical strategy while balancing research innovation with operational excellence
• A phenomenal teammate that can work efficiently with modeling and engineering partners
• Experience with leading ML frameworks (e.g., PyTorch, TensorFlow, HuggingFace) and familiarity with model serving/deployment tools, distributed systems and data infrastructures
• Experience with the following are preferred but not required: Working with LLMs, multimodal models, or time-series forecasting, ML Ops platforms
• Background in research or a proven understanding of ML theory

Benefits

• Core Benefits: Fully paid medical and dental insurance premiums for employees and dependents, competitive 401k match, employer-paid life & disability insurance
• Perks: Onsite gyms with laundry service, wellness activities, casual dress, snacks, game rooms
• Learning: Tuition reimbursement, conference and training sponsorship
• Time Off: Generous vacation and unlimited sick days, competitive paid caregiver leaves
• Hybrid Work Policy: Flexible in-office days with budget for home office setup

Pay

The base pay for this role will be between $165,000 and $300,000. This role may also be eligible for other forms of compensation and benefits, such as a discretionary bonus, health, dental and other wellness plans and 401(k) contributions. Discretionary bonus can be a significant portion of total compensation. Actual compensation for successful candidates will be carefully determined based on a number of factors, including their skills, qualifications and experience.