Senior Software Engineer: Backend and Infrastructure (AI Platform)

Das ist der Job

We are an ETH Zurich Spin-off founded by machine learning PhDs and ex-AWS engineers and supported by ETH AI Center.

Darum lohnt es sich

We are a small team turning frontier research into products experts actually use. You'll be able to take the lead in driving technical execution, helping shape architectural decisions, and empower the rest of the team to deliver high-quality AI systems efficiently.

We at Uthereal are building the AI infrastructure for knowledge-machine interfaces, the layer that lets specialist knowledge owners turn their expertise into systems others can query, learn from, and build on, all on their own terms. Early enough that what you ship defines the platform.

Role This is a full-stack engineering role , but backend and infrastructure are where you should shine. You will own the systems that make expert AI work in production.

This includes designing and operating cloud-native services, distributed processing workflows, data-intensive backend systems, and the infrastructure needed to support AI workloads at scale.

You will be responsible for turning complex product and research requirements into robust backend architecture that is scalable, secure, observable, and cost-aware.

You should be able to understand product and customer needs, contribute across the stack when necessary, and translate ambiguity into solid backend architecture and systems that customers can rely on.

We are looking for someone who can reason through system tradeoffs, optimize quickly to meet different requirements, identify failure modes, and make pragmatic technical decisions under uncertainty. This is an exciting opportunity for someone who is looking for this step-up to own and drive progress in an AI startup.

Who you are 5+ years of software engineering experience, ideally in fast-moving product or startup environments. Strong full-stack background with clear depth in backend and infrastructure, including hands-on experience building and operating systems on AWS. Comfort operating in ambiguity, with a discover-and-learn mindset and high ownership.

Strong critical thinking: challenging assumptions, finding the real problem, and pushing back when needed.

Your required skills Distributed systems engineering, ETL pipeline orchestration, async job processing, concurrency control and failure recovery Database architecture and operations, query performance management, multi-tenant data infrastructure, data maintenance, backup tooling Observability platform engineering, OpenTelemetry instrumentation, SLO and alerting design AWS ECS operations, instance/worker autoscaling, containerized workload operations, queue-based distributed processing, capacity planning, cost management What you need will need to solve Experience with cloud-native development, including containerized workload operations, instance/worker autoscaling, distributed processing, and capacity planning.

You should be comfortable reasoning about scalability, reliability, security, observability, and cost in production systems. Backend experience with APIs, multi-tenant data boundaries, and distributed system design. Experience building and operating production applications end-to-end, not just prototypes or specific features.

You should be able to translate ambiguous product or research requirements into clear technical execution. Hands-on experience with LLM-powered systems, RAG, or agentic workflows is a strong plus. What We Offer A high-ownership role in a fast-growing AI startup. The opportunity to build core product and platform infrastructure from an early stage.

Direct impact on product direction and user experience. Collaborative research environment in partnership with ETH Zurich, University of Zurich, and leading AI experts Opportunity to contribute to groundbreaking AI technologies that reshape expert knowledge access Competitive compensation. #J-18808-Ljbffr