Staff Machine Learning Engineer
We're looking for a talented and passionate Staff Machine Learning Engineer to join Wildlife's DSP team.
The DSP is building a brand new commercial product that helps mobile-first companies scale distribution while maintaining a target return. By integrating post-installation data from its clients, the DSP is able to optimize in real-time the ad spend that goes to different campaigns across hundreds of mobile advertising channels. We leverage information from 2 billion device profiles and handle 1,000,000 bid requests per second with a single digit ms response time. We train several ML models and serve them online to directly control millions of dollars of ad spend.
About the Role
We'll need you to familiarize yourself with the domain and become a reference for technical implementation. To achieve this, we expect you to work on tasks such as: scaling both our current and new ML implementations to deal with PBs of data, optimizing for time and computational efficiency, helping create new data and model training pipelines to tackle novel problematics, and designing and developing tools and frameworks to help the Data Science and Data Engineering teams accelerate their development and experimentation cycle, adopting MLOps best practices.
Since we are always looking for new tools and technologies to better solve our problems, we value professionals that like to learn new things, are autonomous and proactive to bring and implement their ideas.
What you'll do
- Evolve a scalable ML platform to train large models on billions of samples
- Evolve a cutting edge, high performance, high throughput, low latency serving and experimentation platform
- Solve extremely hard problems from scratch, building new ML pipelines, exploring and possibly adopting new technologies
- Design and implement ML tooling and frameworks to accelerate the development and experimentation cycle
- Coach other teammates and become a reference for technical implementation
- Prioritize the value generated to the business, beyond the performance of the solutions
What you'll need
- 5+ years of professional experience in Applied Machine Learning
- Experience in Extreme Scaling ML, running distributed training and validation techniques on large models (+100 MM params) fit on PBs of data
- Experience deploying, serving, and running experimentation on a high throughput – low latency environment
- Advanced knowledge of Machine Learning toolkits (scikit-learn, MLLib, TensorFlow, PyTorch)
- Excellent analytical, problem-solving and critical thinking skills
- Proficiency with MLOps technologies and stacks (Airflow, Kedros, Spark, Databricks, MLFlow, TensorFlow Extended (TFX), KubeFlow, Jenkins, Gitlab CI/CD, Terraform)
- Great coding skills, proficiency with distributed computing frameworks (Spark, Presto, Hive, Horovod)
- Great communication skills both written and oral
- College degree in Computer Science, Statistics, Mathematics, a related field, or equivalent relevant experience
Nice to Have
- 7+ years of professional experience working as a Applied Machine Learning
- Experience developing systems in the ad tech ecosystem
- Proficiency with Cloud based ML platforms (AWS Sagemaker, GC AutoML, Azure ML, Databricks)
- Experience in time series, hierarchical models, previous experience with product analytics
- Active contribution to ML open source projects
- Active participant on the ML community
Wildlife is one of the leading mobile game developers and publishers in the world. We have released more than 60 titles, reaching billions of people around the globe. Today, we have offices in Brazil, Argentina, Ireland, and the United States. Here, we create games that will excite, intrigue, and engage our players for years to come!
Equal Opportunity & Affirmative Action
Wildlife is proud to be an Equal Opportunity and Affirmative Action employer. We do not discriminate based upon race, colour, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local law.
We're committed to providing accommodations for candidates with disabilities in our recruiting process. If you need any assistance, please let us know at firstname.lastname@example.org.
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