Senior Backend Engineer — Machine Learning Infrastructure

6 666 – 9 166 $

на 94% выше медианы рынка

≈ 559,7 тыс.–769,7 тыс. ₽

🌍 УдалённоSeniorПолная занятостьАнглийский C1

6 666 – 9 166 $≈ 559,7 тыс.–769,7 тыс. ₽

Есть о чём спросить

За такой навык на рынке платят больше. Но верх вилки на 56% ниже медианы грейда.

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Вакансия на английскомПереведёт заголовок и описание вакансии на русский

Наша компания

Launched in 2019, Constructor is an AI-first e-commerce search and discovery platform that helps shoppers find the right products at the right time and enables leading global e-commerce brands to drive meaningful revenue and conversion gains.

Чем предстоит заниматься

The ML Infrastructure team builds and operates the shared backend services and platform capabilities that Constructor's ML and product teams rely on to develop, deploy, and run machine learning at scale—from model serving (including LLMs) to the specialized storages and data pipelines behind it

We are a service team by nature: our roadmap is shaped by the needs of product teams, and we succeed when they can move faster with less friction. We're looking for a Senior Backend Engineer who is comfortable with and motivated by this dynamic, and who will take end-to-end ownership of core services

In the near and mid-term, our focus areas include

Making ML workloads easy to run, scale, and operate for every team

Providing self-service, configurable infrastructure components to ML and product teams

Owning and evolving core ML services and related pipelines with a high bar for reliability and performance

Наши требования

5+ years of professional experience in backend or platform engineering

Extensive Python knowledge (our primary language)

Hands-on experience developing on a public cloud (AWS, GCP, or Azure) or with self-managed Kubernetes—AWS is our primary platform, but we're not limited to it

Experience designing and building distributed, high-load services and APIs

Strong knowledge of data structures, algorithms, and their trade-offs

Design-driven engineering mindset: we're a coding-assistant-friendly environment and expect you to use modern AI tools—but you lead with system design and a strong understanding of their applicability and trade-offs

Ownership and proactivity: you can't close your eyes to problems, but are ready to solve them and take responsibility for outcomes

You are friendly and willing to help your teammates & others

Experience with Rust (or C/C++/Go)—a strong plus

Experience developing or contributing to ML platforms or ML infrastructure—a strong plus

Experience setting up and operating any vector database (Qdrant, Milvus, Weaviate, OpenSearch, pgvector, etc.)—a strong plus

Experience with model serving / inference infrastructure, including LLMs

Experience with Infrastructure as Code (Terraform or similar)

Мы предлагаем

Unlimited vacation time—we strongly encourage all of our employees to take at least 3 weeks per year

Fully remote team—choose where you live

Work from home stipend! We want you to have the resources you need to set up your home office

Apple laptops provided for new employees

Training and development budget for every employee, refreshed each year

Maternity & Paternity leave for qualified employees

Work with smart people who will help you grow and make a meaningful impact

Base salary: $80k–$120k USD, depending on knowledge, skills, experience, and interview results

Stock options—offered in addition to the base salary

Regular team offsites to connect and collaborate

Diversity, Equity, and Inclusion at Constructor

Studies have shown that women and people of color may be less likely to apply for jobs unless they meet every one of the qualifications listed. Our primary interest is in finding the best candidate for the job. We encourage you to apply even if you don’t meet all of our listed qualifications

Дополнительно

Design, build, and operate high-load distributed backend services that power the company's ML infrastructure

Take end-to-end ownership of core ML services and related data pipelines: from design and implementation to deployment, observability, and continuous improvement

Partner with ML and product teams to understand their needs and turn them into reliable, reusable platform capabilities

Make and defend technical decisions: evaluate trade-offs, design first, and own the outcome

Be responsible for what you and your team do