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.
Two Sigma is building a new team to drive the firm's strategic transition from CPU-centric to GPU-accelerated computation. Accelerated Compute sits within AI Innovation and operates at the intersection of quantitative modeling workflows, GPU performance engineering, and infrastructure strategy.
You deeply understand the quantitative modeling lifecycle: feature engineering, simulation, backtesting, model validation, and the tooling built around it. You will work directly with modelers to identify CPU-bound workflows that are candidates for GPU acceleration, re-engineer pipelines, and ensure that the transition delivers genuine improvement rather than complexity.
You will take on the following responsibilities: - Analyze existing CPU-based modeling workflows to identify high-impact GPU acceleration opportunities
- Re-engineer modeling tooling and pipelines to exploit GPU parallelism
- Partner with modelers to validate that GPU-accelerated workflows maintain correctness and improve throughput
- Inform GPU infrastructure requirements based on actual workload characteristics
- Bridge between modeling teams and GPU specialists, translating domain requirements into technical specifications
You should possess the following qualifications: - BS or MS in Science, Technology, Engineering or Math
- Minimum 1 year of experience required; 2-10 years of experience preferred
- Deep experience with quantitative modeling platforms and tooling - you've built or maintained the systems that modelers use daily
- Understanding of the full modeling lifecycle: feature engineering, simulation, model training, validation, and production deployment
- Familiarity with Python scientific computing ecosystem (NumPy, Pandas, scikit-learn)
- Strong relationship-building skills - you'll spend significant time working directly with modelers
Preferred qualifications: - Experience profiling and optimizing computational workloads
- Familiarity with NVIDIA GPU-accelerated scientific computing ecosystem (CuPy, RAPIDS, cuDF)
- Background in distributed computing or HPC
You will enjoy the following 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
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.
We are proud to be an equal opportunity workplace. We do not discriminate based upon race, religion, color, national origin, sex, sexual orientation, gender identity/expression, age, status as a protected veteran, status as an individual with a disability, or any other applicable legally protected characteristics.
Two Sigma is committed to providing reasonable accommodations to qualified individuals in accordance with applicable federal, state, and local laws.
If you believe you need an accommodation, please visit our website for additional information.
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