Job Description
We, at Turing, are looking for talented ML engineers who can build some of the most optimized product features by applying advanced ML modeling techniques. Join forces with the top 1% of ML engineers and grow with the best minds.
Key Responsibilities
- Building backend infrastructure, data pipelines, and/or machine learning models for our AI-backed product.
- Building effective ranking models and automating modeling pipelines.
- Implementing new features to solve complex data management problems.
- Deploying machine learning models to end-users and conducting experiments.
- Building high-quality ML models using fundamentals such as data structures, algorithms, programming languages, distributed systems, and information retrieval.
Basic Requirements
- 2+ years of experience in engineering and machine learning methods.
- In-depth understanding of applied machine learning algorithms, especially NLP, and statistics.
- Experience in deploying models and algorithms in production.
- Proficiency in Python.
- Experience with both SQL and NoSQL databases.
- Good testing skills.
- Comfort with both data science and the engineering processes to bring models into production.
About Turing.com
Turing’s mission is to unleash the world’s untapped human potential. We leverage AI to source, evaluate, hire, onboard, and manage engineers remotely via our platform called the “Talent Cloud”.
- Achieved unicorn status with a valuation of $1.1B.
- Raised over $140M across four funding rounds.
- Over 900 companies, including Johnson & Johnson, Pepsi, Dell, Disney+, and Coinbase, have hired Turing developers.
Job Highlights
Qualifications
- 2+ years of experience in engineering and machine learning methods
- In-depth understanding of applied machine learning algorithms, especially NLP, and statistics
- Experience in deploying models and algorithms in production
- Proficiency in Python
- Experience with both SQL and NoSQL databases
- Good testing skills
- Comfortable with data science and engineering processes
Responsibilities
- Building backend infrastructure, data pipelines, and/or ML models
- Building ranking models and automating pipelines
- Implementing features for complex data management
- Deploying models and running experiments
- Building ML models with fundamentals of computer science such as data structures, algorithms, distributed systems, and information retrieval.