Software Engineer(Intern)- Backend
** About Merkle Science**
Merkle Science specializes in providing advanced blockchain transaction monitoring and intelligence solutions to a range of organizations, including web3 companies, financial institutions, law enforcement, and government agencies. The primary goal is to identify, investigate, and prevent any illicit activities related to cryptocurrencies, ultimately ensuring the safety and compliance of the cryptocurrency ecosystem.
Merkle Science has its main headquarters in New York, with additional offices located in Singapore, Bangalore, and London. The professional team boasts a wealth of combined experience from major industry players such as Bank of America, Paypal, Luno, Thomson Reuters, and Amazon. The company has successfully secured over $27 million in funding from reputable investors like SIG, Beco, Republic, DCG, Kenetic, GGV, and others.
Responsibilities:
Develop, construct, and uphold backend services and REST APIs utilizing Python, specifically Django or FastAPI frameworks.
Skillfully manage data models and execute efficient queries in PostgreSQL databases.
Utilize Redis for caching, improving service performance, and managing queues.
Collaborate closely with frontend and Quality Assurance (QA) engineers to deliver end-to-end features.
Write well-documented, clean, and tested code and actively participate in code reviews.
Identify and rectify performance and scalability issues in existing services through debugging and optimization.
Manage and work with extensive datasets related to blockchain transactions, with exposure to tools like ClickHouse being beneficial.
Required Skills:
Solid foundational knowledge in Python and object-oriented programming concepts.
Proficiency in Django/FastAPI or other Python web frameworks.
Familiarity with relational databases, preferably PostgreSQL, encompassing schema design, indexing, and query optimization.
Basic understanding of caching principles and tools like Redis.
Knowledge of REST API design principles.
Strong problem-solving skills and understanding of Data Structures and Algorithms (DSAs) including arrays, trees, graphs, and hashing.
Familiarity with Git and collaborative version control workflows.
Good to Have:
Exposure to Docker/containers and familiarity with message queue systems like Celery, Kafka, or RabbitMQ.
Inclination towards personal projects, open-source contributions, or competitive programming experience.
Interest in blockchain/crypto technologies (prior knowledge not mandatory).
Exposure to relevant AI tools such as GitHub Copilot, Claude, Cursor, or other AI-assisted coding tools.
Eligibility Criteria:
Must be a final-year student pursuing B.Tech/B.E./Dual Degree with an expected graduation date of no later than 2027.
Limited to students from prestigious institutions like IIT, NIT, or IIIT with a concentration in CSE, IT, or closely related computer science branches.
Strong academic record and demonstrated coding abilities through projects, competitive programming, or coursework.
Candidates with open-source contributions, personal projects beyond academics, and prior internship experience will be given preference.
Wellbeing, Compensation, and Benefits:
Merkle Science places significant emphasis on the wellbeing of its employees. In addition to offering comprehensive health insurance, the company provides flexible time off, various learning and development opportunities, and a balanced work-life schedule. Team-building exercises are regularly conducted, and discussions on mental health are encouraged.
Talent recognition and appreciation are core values at Merkle Science. The company offers competitive compensation packages along with generous equity to acknowledge the contributions of its employees. With continuous business growth, there are abundant prospects for career advancement within the organization.
AI Usage Disclaimer:
Merkle Science employs artificial intelligence (AI) tools to support various stages of the hiring process, including application reviews, resume analysis, and response assessments. These tools assist the recruitment team but do not override human judgment. Ultimately, hiring decisions are made by human evaluators. For further information on data processing methods, feel free to reach out to our team.