Senior Machine Learning Engineer, Recommendations
8 Months ago • 5 Years + • $180,000 PA - $250,000 PA
Research Development
Job Description
As a Senior Machine Learning Engineer at Inkitt, you will collaborate with cross-functional teams to design, implement, and optimize scalable recommendation systems. This involves developing and deploying machine learning models (collaborative filtering, content-based filtering, deep learning) and building robust infrastructure for model training and inference. You will conduct A/B tests to improve key metrics like user engagement and retention, contributing to API and service development using Python, Go, and TypeScript. The role requires ensuring high performance, reliability, and scalability for millions of users across Inkitt, Galatea, and GalateaTV apps. Experience with distributed systems and cloud infrastructure is essential.
Good To Have:
- Familiarity with Go API and TypeScript
Must Have:
- 5+ years experience in developing and deploying recommendation systems at scale
- Proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn)
- Deep understanding of ML algorithms for personalized recommendations
- Ability to translate theoretical models into practical, scalable applications
- Experience with distributed systems and cloud infrastructure (AWS, GCP, Azure)
Inkitt is building the Disney of the 21st Century, standing at the forefront of technology and entertainment. Leveraging AI and predictive algorithms, Inkitt discovers unknown stories and turns them into blockbuster hits, producing a new $1M ebook every 4 weeks and selling directly to consumers through its Galatea app. Inkitt has become the 11th most bestseller-generating publisher in the world, boasting a 40x higher hit-rate than traditional publishers. Recently raising a Series C and backed by some of the top VC’s such as: Khosla, Kleiner Perkins, and NEA Ventures, our recent expansion into GalateaTV is only the beginning of our journey to becoming the next-gen entertainment powerhouse.
What You'll Do:
- Collaborate with cross-functional teams, including Product, Data Science, and Engineering, to design and implement scalable recommendation systems that deliver hyper-personalized experiences to users.
- Develop and optimize machine learning models for recommendation engines, utilizing techniques such as collaborative filtering, content-based filtering, and deep learning.
- Build and maintain infrastructure for model training, deployment, and real-time/batch inference, ensuring high performance and reliability.
- Conduct A/B tests and analyze experiment results to iterate on recommendation strategies and improve key metrics such as user engagement and retention.
- Contribute to the design and implementation of robust APIs and services, primarily in Python, Go, and TypeScript, to support recommendation features across our apps (Inkitt, Galatea, and GalateaTV).
- Ensure code and systems meet stringent reliability and performance standards, scaling seamlessly to support millions of users.
What You'll Bring:
- Master’s or PhD in Computer Science, Machine Learning, or a related field.
- 5+ years of experience in developing and deploying recommendation systems at scale.
- Proficiency in Python and experience with frameworks such as TensorFlow, PyTorch, or Scikit-learn. Familiarity with GoAPI and TypeScript is a plus.
- Deep understanding of machine learning algorithms and their application in personalized content recommendations.
- Proven ability to move from theoretical models to practical, scalable application logic in production environments.
- Experience with distributed systems and cloud infrastructure, such as AWS, GCP, or Azure.
- A strong focus on reliability, performance, and maintainability in engineering practices.
180,000 - 250,000 a year
At Inkitt, we strive to build a company culture and provide employment opportunities based on diversity and inclusion. We believe every author should have an equal opportunity to succeed, as should our team members. As a growing team from 20+ countries, we welcome everyone to apply.
We look forward to hearing from you!
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