MaaS Backend Engineer

Bitdeer is a world-leading technology company for AI and Bitcoin mining infrastructure.

Bitdeer is committed to providing comprehensive Bitcoin mining solutions for its customers and building AI computational infrastructure to support the AI revolution. Bitdeer handles complex processes involved in computing such as equipment procurement, transport logistics, data center design and construction, equipment management, and daily operations. Bitdeer also offers advanced cloud capabilities to customers with high demand for artificial intelligence.

Headquartered in Singapore, Bitdeer has deployed data centers across multiple countries, including the United States, Norway, Bhutan, and Ethiopia.

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We are seeking a Staff Backend Engineer to take our Model-as-a-Service (MaaS) working system and re-architect it into a commercial, globally distributed, multi-tenant token service — one that sustains at least millions of monthly active users, high sustained token throughput per GPU, and invoice-grade accounting, with scalability, reliability, and observability engineered in deliberately rather than absorbed under load. This role works directly with the principal architect: co-owning the MaaS system design as the deepest backend voice in that conversation, and owning the implementation end to end — the code that ships, the migrations that land, and the service that stays up.

Design authority is shared; delivery accountability is not. The mandate is explicit: measure what exists, find where it breaks before it breaks in front of a paying customer, and carry the platform there incrementally — with each step independently shippable, reversible, and non-disruptive to the tenants already on it. The role is deeply hands-on: it reads and rewrites the existing Go services, owns SLOs and on-call for a revenue-bearing service, and sets the backend engineering standard for the MaaS team.

• Architecture, Strategy, and Leadership:
  Co-own the end-to-end MaaS system design with the Principal Architect, authoring decision records and defending technical trade-offs. Drive the platform through its maturity roadmap by delivering operable, measurable capabilities rather than mere demos. Lead technical execution by setting stringent Go and API standards, mentoring engineers, and aligning cross-functional teams.
• Inference Gateway and API Surface:
  Own the wire compatibility contract for major formats (OpenAI, Anthropic), supporting advanced features like streaming, tool calling, and structured output. Evolve the routing tier to handle load-aware, model-aware, and prefix-cache-aware endpoint selection with robust circuit breaking and fallback mechanisms. Run versioning and deprecation as a published contract to guarantee external customer code stability across underlying changes.
• Performance Optimization and Model Lifecycle:
  Maximize platform economics and performance by optimizing token throughput, KV cache tiering, and time-to-first-token (TTFT) latency at the p95/p99 levels. Mature the model serving control plane by integrating deployment tooling, LoRA multiplexing, and cold-start-aware autoscaling directly with the Kubernetes fleet. Treat regressions in cost-per-million-tokens or latency metrics as critical system incidents.
• Global Topology and Reliability (SLOs):
  Scale the platform to a globally distributed architecture featuring regional inference pools, capacity-aware failovers, and an active-active control plane. Define, publish, and rigorously defend strict Service Level Objectives (SLOs) baselined against actual system performance rather than aspirations. Ensure operational resilience through peak-concurrency load testing, robust on-call runbooks, and…