Quantitative Software Engineer: Generative AI
Two Sigma · New York, United States · 4d ago
Mid levelOn-siteQuant Research & Development
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.
The News Engineering team is looking for a machine learning engineer with a strong background in NLP and LLMs. Our team’s vision is to collect, organize, and monetize the world’s unstructured data, including news, social media, and regulatory filings. To that end, we develop:
- A platform for interactive research, backed by a one-petabyte corpus of unstructured data
- A system for automatic signal mining, evaluating research hypotheses end-to-end
- NLP and LLM services, using a variety of techniques across our research tools and production pipelines
- Data pipelines, delivering millions of events to production systems every day
We work closely with our partners in Modeling, making this position a strong choice for anybody looking to leverage proven NLP and software engineering skills in an area close to the firm’s core business.
You will take on the following responsibilities:
- Lead applied research initiatives using ground breaking generative AI technologies
- Implement and maintain scalable machine learning and NLP systems in data-intensive production environments
- Collaborate with modeling partners to prototype and iterate in emerging NLP research areas
You should possess the following qualifications:
- Minimum 1 year of experience required; 3-10 years of experience preferred developing and deploying machine learning solutions, preferably in the areas of NLP and LLMs
- BS in Computer Science, Machine Learning, or related technical field (advanced degrees and/or ML publications highly desired)
- Demonstrated success translating research publications into practical solutions
- Proficiency with popular natural language processing and deep learning frameworks in Python (PyTorch, Tensorflow, etc.)
- Proven track record of thriving in multi-functional, fast-paced environments
- Ongoing engagement with emerging trends, research, and applications in generative AI
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.