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4 дня назад

Software Engineer (Inference Platform, AI)

Π€ΠΎΡ€ΠΌΠ°Ρ‚ Ρ€Π°Π±ΠΎΡ‚Ρ‹
hybrid
Π’ΠΈΠΏ Ρ€Π°Π±ΠΎΡ‚Ρ‹
fulltime
Английский
b2
Π‘Ρ‚Ρ€Π°Π½Π°
UK
Вакансия ΠΈΠ· списка Hirify.GlobalВакансия ΠΈΠ· Hirify Global, списка ΠΌΠ΅ΠΆΠ΄ΡƒΠ½Π°Ρ€ΠΎΠ΄Π½Ρ‹Ρ… tech-ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉ
Для мэтча ΠΈ ΠΎΡ‚ΠΊΠ»ΠΈΠΊΠ° Π½ΡƒΠΆΠ΅Π½ Plus

ΠœΡΡ‚Ρ‡ & Π‘ΠΎΠΏΡ€ΠΎΠ²ΠΎΠ΄

Для мэтча с этой вакансиСй Π½ΡƒΠΆΠ΅Π½ Plus

ОписаниС вакансии

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TL;DR
Software Engineer (Inference Platform, AI) (Kubernetes/ML Serving): Develop and operate an inference platform serving fleets of machine learning models to scientific applications with an accent on scalability, reliability, and high-throughput performance. Focus on optimizing distributed inference services, designing cloud-native infrastructure, and supporting larger models and growing data volumes.

Location: London, United Kingdom. Hybrid working requires coming into the office 3 days a week.

Company

hirify.global applies AI and machine learning to drug discovery, building predictive and generative models based on and beyond AlphaFold to develop innovative medicines.

What you will do

  • Develop and operate an inference platform serving fleets of machine learning models to scientific applications.
  • Optimize inference services to solve scaling limits and deliver high-throughput performance across the model-serving stack.
  • Work with research and applied ML teams to improve infrastructure stability, reliability, and scalability.
  • Contribute to tooling and architectural design decisions.
  • Deliver high-quality, well-tested, user-focused features.

Requirements

  • Hands-on experience deploying and scaling inference frameworks such as KServe or Seldon within Kubernetes.
  • Strong understanding of cloud-native machine learning lifecycle management.
  • Strong programming skills and a reliability-first approach to software development.
  • Experience developing, operating, and debugging large-scale distributed systems and high-throughput architectures.
  • Understanding of Infrastructure as Code, CI/CD pipelines, observability, and modern DevOps practices.
  • Ability to work from the London office 3 days per week.

Nice to have

  • Experience with Google Cloud Platform.
  • Familiarity with workload scheduling, ML efficiency research, and hardware benchmarking.
  • Experience with GPU-aware infrastructure and specialized inference servers such as Triton or TF Serving.

Culture & Benefits

  • Collaborative, interdisciplinary environment spanning drug discovery, research, and applied machine learning.
  • Values focused on curiosity, creativity, care, initiative, integrity, determination, and cross-field collaboration.
  • Hybrid working model designed to support knowledge sharing and in-person collaboration.
  • Commitment to equal employment opportunities and reasonable accommodations for disabilities or additional needs.

Π‘ΡƒΠ΄ΡŒΡ‚Π΅ остороТны: Ссли Ρ€Π°Π±ΠΎΡ‚ΠΎΠ΄Π°Ρ‚Π΅Π»ΡŒ просит Π²ΠΎΠΉΡ‚ΠΈ Π² ΠΈΡ… систСму, ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΡ iCloud/Google, ΠΏΡ€ΠΈΡΠ»Π°Ρ‚ΡŒ ΠΊΠΎΠ΄/ΠΏΠ°Ρ€ΠΎΠ»ΡŒ, Π·Π°ΠΏΡƒΡΡ‚ΠΈΡ‚ΡŒ ΠΊΠΎΠ΄/ПО, Π½Π΅ Π΄Π΅Π»Π°ΠΉΡ‚Π΅ этого - это мошСнники. ΠžΠ±ΡΠ·Π°Ρ‚Π΅Π»ΡŒΠ½ΠΎ ΠΆΠΌΠΈΡ‚Π΅ "ΠŸΠΎΠΆΠ°Π»ΠΎΠ²Π°Ρ‚ΡŒΡΡ" ΠΈΠ»ΠΈ ΠΏΠΈΡˆΠΈΡ‚Π΅ Π² ΠΏΠΎΠ΄Π΄Π΅Ρ€ΠΆΠΊΡƒ. ΠŸΠΎΠ΄Ρ€ΠΎΠ±Π½Π΅Π΅ Π² Π³Π°ΠΉΠ΄Π΅ β†’