Synthesia26 days ago

Инженер платформы машинного обучения (ML Platform Engineer)

Salary not specified

MARKET

15,900 ₽median for this role

Data Scientist · 115 jobs with disclosed pay

5,000half of the offers: 12,796–20,50043,793

The employer didn't disclose pay — compare with the market yourself.

Полная занятостьУдалёнка

Responsibilities

01Design and improve the platform systems that support model training, evaluation, and production serving
02Build infrastructure and tooling that make ML workloads more reliable, scalable, and cost-efficient
03Develop internal tools and workflows that are easy to operate both by humans and by agents
04Work on the architecture behind how models are deployed, served, and operated across research and product environments
05Improve how we schedule, monitor, and debug workloads running on GPUs and cloud infrastructure
06Develop internal tools and abstractions and agentic systems that reduce operational overhead for researchers and engineers
07Drive improvements across observability, automation, reliability, and developer experience
08Collaborate closely with researchers and product engineers to understand pain points and turn them into robust platform capabilities
09Contribute to technical direction and make pragmatic architectural tradeoffs as the platform grows

01Strong experience building or operating production systems with a focus on reliability, scalability, and maintainability
02A systems mindset: you naturally think in terms of bottlenecks, failure modes, interfaces, resource usage, and long-term operability
03Solid hands-on experience with cloud infrastructure, Linux, and infrastructure automation
04Experience with Kubernetes and operating distributed workloads in production
05Strong coding skills, ideally in Python or similar languages used for backend systems and tooling
06Strong judgment around where automation adds leverage, and where human control and reliability matter most
07Experience building internal platforms, developer tooling, or infrastructure abstractions used by other engineers
08Comfort working in ambiguous environments and taking ownership of open-ended technical problems
09A pragmatic approach: you care about solving the right problem well, not over-engineering
10Operating ML infrastructure or model serving systems in production
11Supporting research or data-intensive workloads
12Working with GPU-based systems or other performance-sensitive infrastructure
13Experience with observability and debugging in distributed systems