Summary
Join StackAdapt's Data Science team as a Senior or Staff Machine Learning Engineer and contribute to building and optimizing our digital advertising platform. You will design and implement real-time data pipelines, develop custom ML algorithms, and work on microservice architectures. The role requires expertise in algorithm and software design, distributed systems, and machine learning. StackAdapt offers a remote-first work environment and a comprehensive benefits package. The company is known for its supportive culture and has received numerous awards for its workplace and products. This position is open to candidates located anywhere in the UK.
Requirements
- Have the ability to take an ambiguously defined task, and break it down into actionable steps
- Ability to follow through complex projects to completion, both by independent implementation and by coordinating others
- Have deep understanding of algorithm and software design, concurrency, and data structures
- Experience in implementing probabilistic or machine learning algorithms
- Experience in designing scalable distributed systems
- A high GPA from a well-respected Computer Science program or equivalent experience in a competitive, innovative, tech company
- Enjoy working in a friendly, collaborative environment with others
Responsibilities
- Design modular and scalable real time data pipelines to handle huge datasets
- Suggest, implement, and coordinate architectural improvements for big data ML pipelines
- Understand and implement custom ML algorithms in a low latency environment
- Work on microservice architectures that run training, inference, and monitoring on thousands of ML models concurrently
Benefits
- Competitive salary
- Private Medical Insurance cover
- Auto-enrolment into the company pension scheme
- Work from home reimbursements
- Coverage and support of personal development initiatives (conferences, courses, etc)
- An awesome parental leave policy
- A friendly, welcoming, and supportive culture
- Our social and team events (virtually!)
- Take part in our walk and wander policy and work anywhere in the world for up to 90 days a year
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