17 авг

machine learning engineer for high-load microservices

ориентир по рынку
вакансия зп не указана
в среднем 206 031 ₽
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описание

StackAdapt is a technology company that empowers marketers to reach, engage, and convert audiences through its AI-powered marketing platform for programmatic advertising and performance marketing. The platform supports high-impact campaigns and measurable results across the customer journey.

задачи

  • Design modular and scalable real-time data pipelines for huge datasets;
  • Implement custom ML algorithms in a low-latency environment;
  • Work on microservice architectures supporting training, inference, and monitoring for thousands of ML models concurrently.

требования

  • Break down ambiguously defined tasks into actionable steps;
  • Have a deep understanding of algorithms, software design, concurrency, and data structures;
  • Have experience implementing probabilistic or machine learning algorithms;
  • Be interested in designing scalable distributed systems;
  • Have a high GPA from a well-respected Computer Science program;
  • Enjoy working in a friendly, collaborative environment.

условия

  • Highly competitive salary;
  • Retirement, 401K, and pension savings globally;
  • Competitive paid time off packages, including birthdays off;
  • Access to a comprehensive mental health care program;
  • Health benefits from day one of employment;
  • Work-from-home reimbursements;
  • Optional global WeWork membership and hubs in London and Toronto;
  • Robust training and onboarding program;
  • Coverage and support for personal development initiatives, including conferences, courses, and books;
  • Access to StackAdapt programmatic courses and certifications;
  • Parental leave program;
  • Social and team events;
  • Benefits and perks may vary depending on the country of employment and the nature of the engagement;
  • In locations where StackAdapt does not have a legal entity, employment and benefits are administered according to local regulations and partner policies.

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