Principal Machine Learning Scientist - Search & Recommendations
Remote
Applications have closed
The ideal candidate will have experience leading efforts researching, developing, and deploying cutting-edge machine learning search, recommendations, and advanced personalization algorithms into production systems. You should keep up with the latest academic research and developments. You should have strong engineering skills and experience with integrating machine learning and engineering in a dynamic production environment.
With billions of data points ingested daily and the ability to impact and automate millions of critical decisions that drive the core of our business, this is a unique opportunity to build new advanced ML and AI capabilities at a company that still innovates at the pace of a startup but has the data and scale of a successful billion dollar company!
The Role You Will Have
- Provide both hands-on/individual contributor support and technical thought leadership in the development of highly scalable algorithms based on state-of-the-art machine learning models to build products delivering new recommendations, search, personalization, and dynamic content decisioning capabilities
- Leverage knowledge of software engineering principles to guide development and deployment of production machine learning solutions that can scale to millions of requests per second with millisecond latency
- Maintain expert knowledge in the latest in machine learning with an emphasis on emerging techniques in natural language understanding, semantic search, image recommendations, and optimal ranking
- Actively contribute to the wider internal and external data science and machine learning communities by sharing and publishing research and findings
- Work with cross-functional teams of engineers, scientists, and analysts, to devise implementation and experimentation strategies
- Utilize broad and deep knowledge of machine learning to contribute to the development of System1’s roadmap for core machine learning capabilities
- Coach and mentor data scientists to elevate the talent bar across System1
What You Will Bring
- Bachelor's degree (Master's or Ph.D preferred) in a highly quantitative field (e.g. Computer Science, Engineering, Physics, Math, Operations Research, etc) or equivalent work experience
- 5+ years of experience researching & developing algorithms for search and/or dynamic content recommendations
- Strong machine learning and algorithmic background with a deep understanding of advanced language models, recommendations and search systems, and advanced deep learning techniques
- Hands-on experience using high performance machine learning libraries and/or deep learning frameworks (e.g. PyTorch, Tensorflow, RAPIDs, etc)
- Hands-on experience building ML driven products
- Experience leading technical research and production grade development efforts/teams
- Fluency with at least one data science/analytics programming language (e.g. Python, R, Julia)
- Strong software design and implementation skills with a general-purpose programming language (e.g. Rust, Scala, C++/C#, Java, Haskell, etc)
- Experience building scalable distributed products to implement batch, real-time, and streaming machine learning products
- Strong functional programming development skills (preferred)
- Ability to effectively communicate technical information to a wide spectrum of cross-functional team members
- Experience mentoring and coaching junior team members
What We Have to Offer
- Competitive salary + bonus + equity
- Generous PTO + 11 Company Holidays
- Untracked sick time
- 100% covered Medical, Dental, Vision for employees
- 401k w/match
- Paid professional development
- Leadership & growth opportunities
- Virtual company and team building events
- #BI-Remote
- #LI-Remote
Tags: Computer Science Deep Learning Engineering Haskell Julia Machine Learning Mathematics ML models Physics Python PyTorch R Research Rust Scala Streaming TensorFlow
Perks/benefits: 401(k) matching Career development Competitive pay Equity Flex vacation Health care Salary bonus Startup environment Team events
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