- Company Name
- Two Sigma
- Job Title
- Quantitative Software Engineer: Techniques Engineering
- Job Description
-
**Job Title**
Quantitative Software Engineer – Techniques Engineering
**Role Summary**
Deploy and scale cutting‑edge machine learning research into robust, production‑ready systems. Partner with ML researchers, data teams, and platform engineers to build automated training pipelines, model serving infrastructure, and continuous integration pipelines that deliver high‑performance models to portfolio managers.
**Expectations**
- Translate experimental prototypes into reusable, maintainable code.
- Optimize models for latency, throughput, and resource efficiency.
- Maintain highest code quality standards (testing, CI/CD, version control).
- Mentor junior engineers and influence technical strategy across functions.
**Key Responsibilities**
- Collaborate with researchers to evaluate model architectures, training methods, and experimental objectives.
- Prototype, refactor, and prod‑grade ML solutions, ensuring scalable and maintainable codebases.
- Design, implement, and optimize fully automated training pipelines and evaluation workflows.
- Employ model optimization techniques (quantization, distillation, batching) for production deployment.
- Continuously monitor, debug, and fine‑tune research and production workflows using automated monitoring and user feedback loops.
- Identify bottlenecks, propose process improvements, and implement automation to increase developer and modeler efficiency.
- Stay current with emerging ML technologies and integrate advanced methodologies into production systems.
**Required Skills**
- Proficient in Python, version control (Git), automated testing, and CI/CD practices.
- Hands‑on experience with leading ML frameworks: PyTorch, TensorFlow, or HuggingFace.
- Familiarity with model serving and deployment tools, distributed systems, and data infrastructure.
- Strong software engineering mindset: clean code, reusable components, performance profiling.
- Ability to balance research creativity with operational reliability and scalability.
- Excellent collaboration and communication skills across data science, engineering, and operations teams.
**Preferred Experience**
- Knowledge of large language models, multimodal systems, or time‑series forecasting.
- Exposure to MLOps platforms and ML lifecycle tooling.
- Background in ML research or solid understanding of ML theory.
**Required Education & Certifications**
- Bachelor’s degree (or equivalent) in Computer Science, Mathematics, Physics, or related technical discipline.
- Minimum 1 year of professional software engineering/ML production experience; 7–15 years preferred.
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