Senior Software Engineer

Przegląd oferty

Lokalizacja
Remote, Remote
Rodzaj pracy
Pełny etat
Wynagrodzenie netto
19,163 zł - 29,193 zł Za MiesiÄ…c
Data opublikowania
3 lat temu

Szczegóły

ID oferty
4944
Typ pracy
Zdalnie
Rozmiar firmy
ponad 200
Wynagrodzenie
Umieszczone w ofercie
Wykorzystywane technologie
Python, Java, Scala, C++
Typ umowy
B2B
Rekrutacja
Online
Rekrutacja w języku
Polski
Korzyści
Finansowanie kursów, Międzynarodowe projekty, Małe zespoły, Elastyczne godziny pracy,
Poziom doświadczenia
Senior Expert
Wymagana
|C++| Java Python Scala

Opis oferty

  • Distributed Systems
  • Problem solving
  • Good coding skills

Optional:

  • GPU Programing
  • Postgres

Opis oferty

We are developing a large-scale (5M+ requests per sec., 500M+ model evaluations per sec., 200TB+ data per day, 2K+ servers, 4 datacenters) in-house product with a high level of complexity. The team consists mostly of senior developers who are happy to share their knowledge and ideas. As a Senior Software Engineer you will have a decisive influence on the efficiency and scalability of our Machine Learning infrastructure, as well as the real-time bidding platform. You will be designing, implementing and testing the distributed system using a wide variety of languages (Python, C++, Scala, Java) and technologies (PyTorch, CUDA, NumPy, Spark, GCP, Kubernetes, Cython, JNI, WebAssembly etc.). We are looking for experienced developers (proficient in one of the mentioned programming languages and Linux) who will join our team. Previous experience in the field of ML is not required. If you would like to work in a technically challenging and fascinating area, this could be a project for you!

Zakres obowiązków

  1. Developing the ML training platform and the part of the bidding infrastructure that evaluates ML models in the production environment
  2. Identifying performance bottlenecks, optimizing critical, low-level parts of the software and system in terms of efficiency and resource consumption
  3. Ensuring the reliability and scalability of the implementations, creating performance and correctness tests for new system components
  4. Testing and benchmarking open-source Big Data and ML technologies in terms of their applicability in the production environment