Applied Researcher

Goldman Sachs interviews typically start with an online coding test focusing on data structures, algorithms and basic ML concepts, followed by two technical rounds that dive deep into NLP fundamentals, model design, and system architecture. The final HR round assesses cultural fit, communication skills, and motivation. Candidates should practice coding under time pressure, review transformer architectures, and be ready to discuss real‑world finance use‑cases.

Goldman Sachs promotes a high‑performance culture that balances rigorous analytical work with strong mentorship and collaborative teamwork. Employees enjoy clear growth trajectories, exposure to global clients, and a supportive environment that encourages continuous learning and work‑life balance through flexible policies.

Bachelor’s, Master’s or PhD in Computer Science, Machine Learning, Mathematics, Engineering or related fields. Minimum CGPA 6.5/10 (or equivalent). For Bachelor’s/Master’s candidates: at least 3 years of AI/ML industry experience; for PhD candidates: minimum 1 year of relevant experience focusing on language models and NLP. No active backlogs; internships or projects in NLP/ML are a plus. Open to candidates graduating in 2025, 2026 or 2027.

Round 1: Online coding assessment (Data structures, algorithms, and basic ML problems) → Round 2: Technical interview (NLP concepts, model design, coding on a whiteboard) → Round 3: System design & research discussion (designing LLM pipelines for finance) → Round 4: HR interview (fit, motivations, cultural alignment)

Goldman Sachs, founded in 1869, is a premier global investment banking, securities and investment management firm with a presence in more than 60 cities worldwide. The firm prides itself on delivering innovative financial solutions, leveraging deep industry expertise and cutting‑edge technology to serve a diverse client base ranging from corporations and governments to high‑net‑worth individuals. With a strong culture of meritocracy, collaboration, and continuous learning, Goldman Sachs has consistently been recognized for its leadership in finance, its commitment to diversity and inclusion, and its robust employee development programs. The firm’s Indian operations, spread across major financial hubs such as Mumbai, Bengaluru and Hyderabad, play a critical role in research, technology, and client service, offering a vibrant environment for fresh talent to grow. The Applied Researcher role sits within the AI Research team, a high‑impact group that builds next‑generation language models and natural language processing (NLP) solutions tailored for the financial domain. As an Applied Researcher, you will bridge the gap between cutting‑edge academic research and production‑grade systems, designing, developing, and evaluating large language models (LLMs) that can ingest, interpret, and act upon massive volumes of financial documents, market data, and regulatory filings. You will collaborate with product owners, data engineers, quant analysts, and business stakeholders to translate complex financial use‑cases into scalable AI solutions that drive better decision‑making and operational efficiency. Key responsibilities include: 1. Design and implement scalable NLP pipelines that extract insights from earnings calls, SEC filings, research reports, and news articles. 2. Conduct rigorous experiments to benchmark model performance, fine‑tune hyper‑parameters, and improve inference latency. 3. Adapt state‑of‑the‑art transformer architectures (e.g., BERT, GPT, T5) for domain‑specific tasks such as sentiment analysis, entity recognition, and question answering. 4. Write clean, modular, and production‑ready Python code, adhering to software engineering best practices and version‑control standards. 5. Lead cross‑functional projects, coordinating with data scientists, engineers, and business teams to deliver end‑to‑end AI solutions. 6. Publish technical findings internally and represent Goldman Sachs at conferences, workshops, and open‑source communities. 7. Mentor junior team members and contribute to the team’s knowledge base through documentation and code reviews. 8. Stay abreast of the latest research in NLP, deep learning, and finance‑specific AI applications, proposing innovative ideas to keep the firm at the forefront of technology. 9. Ensure compliance with data security, privacy, and regulatory requirements throughout the model development lifecycle. 10. Participate in continuous integration and deployment pipelines to monitor model performance in production. Tech stack: Python, PyTorch/TensorFlow/Keras, Hugging Face Transformers, Docker, Kubernetes, AWS/GCP, SQL/NoSQL databases, Git, CI/CD tools (Jenkins, GitHub Actions), and data processing frameworks such as Spark. Growth path: Starting as an Applied Researcher, high performers can progress to Senior Researcher, Lead AI Scientist, or AI Platform Manager, with opportunities to move into product leadership, quantitative research, or strategy roles across the firm’s global network. Why join Goldman Sachs? The firm offers unparalleled exposure to real‑world financial problems, access to massive datasets, and the chance to work alongside world‑class researchers and industry veterans. Employees benefit from a strong mentorship culture, competitive compensation, comprehensive learning resources, and a clear pathway to leadership in both technology and finance. If you are passionate about pushing the boundaries of AI in finance, Goldman Sachs provides the platform, resources, and global reach to turn ambitious ideas into impactful solutions.