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AI Summary
About the Role
We’re looking for an Infrastructure Engineering Intern who wants to scale real production systems—not just manage cloud resources.
You’ll work on designing and optimising infrastructure for high-throughput speech workloads, balancing performance, cost, and reliability in a fast-moving environment. This includes compute, storage, networking, and preparing systems to scale across regions.
You’ll collaborate closely with SREs and engineers to ensure systems are efficient, resilient, and ready for growth. Your work will directly impact latency, cost efficiency, and system stability.
If you enjoy thinking about systems at scale, debugging performance issues, and improving how infrastructure behaves under load, this role will push you to grow quickly.
What You Will Do
Contribute to cloud architecture design and operations (AWS, GCP, or Azure)
Work with Kubernetes to manage and scale workloads
Optimise GPU and CPU scheduling for performance and cost
Profile system performance and identify bottlenecks
Help prepare systems for multi-region and high-availability setups
Collaborate on capacity planning with SRE and engineering teams
Document infrastructure decisions, architectures, and best practices
What We’re Looking For
Basic understanding of cloud platforms (AWS, GCP, or Azure)
Familiarity with Kubernetes and container orchestration
Exposure to distributed systems concepts
Awareness of performance and resource optimisation basics
Comfort with debugging and profiling system performance
Ability to operate in fast-changing, ambiguous environments
Founding Mindset
You think in terms of user-perceived reliability, not just dashboards
You ask “what does good reliability look like?” before adding metrics
You take ownership of uptime and graceful degradation
You balance speed of change with system stability
You proactively identify reliability gaps before they cause incidents
Bonus
Experience with observability tools like Prometheus, Grafana, or OpenTelemetry
Exposure to incident management or on-call workflows
Experience with resilience testing or chaos engineering
Familiarity with tracing and structured logging
What Success Looks Like
Within 4–6 weeks, you should be able to:
Own monitoring and alerting for a subset of services
Reduce noisy alerts and improve signal quality
Contribute to incident retrospectives with actionable insights
Improve reliability, visibility, or response time in a measurable way
What You’ll Get
Hands-on experience building reliability practices from the ground up
Direct collaboration with founders and core engineering teams
Real ownership beyond a typical SRE internship
A portfolio of dashboards, runbooks, and system improvements
A strong pathway into SRE, platform, or production engineering roles
Who This Is Not For
If you want predictable systems with no incidents to handle
If you avoid ambiguity or cross-team problem solving
If you prefer rigid, siloed environments with predefined processes
Who Will Thrive Here
Builders who want to actively own system reliability
Engineers who think in terms of end-to-end systems
Calm debuggers of intermittent and complex failures
High-agency individuals who care about keeping systems running reliably
About the Company
We’re building the speech intelligence layer for Southeast Asia—turning real-world, accented, code-switched speech into structured, usable outputs for businesses.
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