Data and AI Founding Engineer

Are you passionate about leveraging the power of AI to transform industries and make a real impact? SciPub+ is an early-stage startup on a mission to reimagine the research dissemination landscape. We're building the first AI-native platform designed to streamline the entire research lifecycle, from literature review to publication and beyond. Our AI-powered writing assistants have already gained significant traction, serving thousands of researchers and growing daily. Our vision is to make research more efficient, equitable, and impactful for researchers worldwide. 

As a Data and AI Founding Engineer, you'll play a pivotal role in shaping the future of research publishing. You'll work alongside a talented team of AI and academic experts to develop cutting-edge solutions that empower researchers and accelerate knowledge dissemination. This is a unique opportunity to make a significant contribution to the world of science and technology while building a successful and impactful company.

Who We’re Looking For:

Experience:

• A minimum of 4-5 years in data engineering and/or ML and microservices development

AI Data Ingestion/Processing and ML Development:

• Proficient with data extraction and ingestion, event processing, (Kinesis, or Azure Events), data pipelines, NoSQL databases (MongoDB or Redis or DynamoDB), data warehouses and big data query engines (SQL, Spark SQL, BigQuery).
• Proficient in Python, Java/Scala, and Docker
• Process unstructured data from raw csv, json, text or pdf files into semi structured formats and index them in data warehouses or NoSQL datastores, ensuring scale and high quality. Familiarity with data schemas and row/columnar file formats, such as parquet, avro, etc.
• Familiarity with AI/LLM orchestration frameworks such as LLamaIndex or LangChain. Optimize and integrate AI models and APIs, including OpenAI's latest models, into our platform.
• Familiarity with the cloud ML toolset, including Sagemaker, BYOC, and inference systems. Tensorflow, Pytorch and Huggingface ecosystem experience is a plus.
• Familiarity with indexing and retrieval techniques, vector databases, tf-idf, ranking and relevance, and/or graph databases
• Ideally, exposure or prior experience with multi-stage recommendation systems for large scale applications, both offline and online serving.
• Familiarity with cloud, and cloud ML tooling, preferably from AWS, or Azure

Codebase Management:

• Proficient in Python and Java, nice to have Javascript 
• Familiarity with Docker, Git, Git Pipelines and CI/CD

Your Role:

• Design and implement data pipelines and architectures to support AI/ML model development and deployment.
• Process and transform unstructured data into formats suitable for machine learning.
• Develop and optimize machine learning models for various tasks within the SciPub+ platform.
• Integrate AI models and APIs, ensuring scalability and performance.
• Collaborate with the team to define and implement data strategies.
• Contribute to the development and maintenance of our codebase.

Why Join Us:

• Be a part of a mission-driven team revolutionizing scientific communication.
• Work with cutting-edge AI and ML technologies.
• Have a significant impact on the future of SciPub+.
• Grow in a collaborative and innovative work environment.
• Enjoy remote and flexible working conditions. 

Getting in Touch:

If you're passionate about data, AI, and scientific publishing, we encourage you to apply. Please send your resume and a cover letter explaining your relevant experience and interest in SciPub+ to [email protected].

Let's build the future of scientific discovery together!