Senior Machine Learning Engineer, Recommendations at Inkitt

San Francisco, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Entertainment, BiotechnologyIndustries

Requirements

Candidates should possess a Master’s or PhD in Computer Science, Machine Learning, or a related field, along with 5 or more years of experience in developing and deploying recommendation systems at scale. Proficiency in Python, experience with frameworks like TensorFlow, PyTorch, or Scikit-learn, and familiarity with GoAPI and TypeScript are required. A deep understanding of machine learning algorithms and their application to personalized content recommendations, coupled with experience in distributed systems and cloud infrastructure (AWS, GCP, or Azure), is also necessary.

Responsibilities

The Senior Machine Learning Engineer will collaborate with cross-functional teams to design and implement scalable recommendation systems, develop and optimize machine learning models for recommendation engines, build and maintain infrastructure for model training and deployment, conduct A/B tests and analyze experiment results, and contribute to the design and implementation of robust APIs and services. They will also ensure code and systems meet reliability and performance standards, scaling seamlessly to support millions of users, and focus on reliability, performance, and maintainability in engineering practices.

Skills

Machine Learning

Recommendation Systems

TensorFlow

Python

TypeScript

Collaborative Filtering

Content-Based Filtering

Deep Learning

A/B Testing

API Design

Model Deployment

Scalability

Performance Optimization

Inkitt

Reader-powered digital publishing platform

About Inkitt

Inkitt operates as a reader-powered publisher, offering a platform where readers can access a diverse range of books for free across various genres like fantasy, sci-fi, romance, and more. The platform allows readers to provide feedback to authors, influencing the development of stories. Authors can submit their works, which Inkitt evaluates based on reader engagement metrics such as reading time and feedback. Successful stories are then offered publishing deals, including distribution on Inkitt's sister app, GALATEA. This data-driven approach helps Inkitt reduce risk by focusing on stories that have already garnered reader interest. The company generates revenue through publishing deals, sharing profits with authors, and potentially from premium content. Inkitt's goal is to connect readers with new literary talent while providing authors a platform to showcase their work.

San Francisco, CaliforniaHeadquarters

2014Year Founded

$113.7MTotal Funding

SERIES_CCompany Stage

Data & Analytics, Consumer Goods, EntertainmentIndustries

201-500Employees

Benefits

401(k) Retirement Plan

Health Insurance

Dental Insurance

Vision Insurance

Risks

Over-reliance on AI may lead to story homogenization, alienating readers.

Rapid TV series expansion could strain resources, affecting quality.

Privacy concerns over hyper-personalization may lead to regulatory scrutiny.

Differentiation

Inkitt uses AI-driven ReadRank to predict bestsellers, enhancing story selection.

GalateaTV transforms e-books into short-form TV series, expanding content reach.

Inkitt's Author Subscription Program offers authors 100% revenue, incentivizing content creation.

Upsides

Inkitt's $37M funding supports AI development for content optimization.

Interactive storytelling trends could enhance Inkitt's reader engagement.

Cross-media franchising offers potential for increased brand reach and revenue.

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