Top Tech Jobs & Startup Jobs in Singapore

Own technical documentation for an AI Cloud Kubernetes platform, including API, SDK, CLI, Terraform, onboarding, and release documentation. Validate examples against source code and live environments, maintain a docs-as-code pipeline, and publish documentation with product releases. Analyze support-ticket trends to identify documentation gaps, communicate breaking changes and migrations, and partner closely with engineering and product teams to ensure new interfaces are documented and self-service workflows are effective.

Own end-to-end R&D delivery for a multi-region, 10,000+ GPU cloud platform. Responsibilities include integrated planning, scheduling, milestone and critical-path tracking, dependency and risk management, delivery-process improvement, program rituals, leadership reporting, escalation, and coordinating regional rollouts with engineering teams.

Build and operate provider adapters connecting an AI Model-as-a-Service platform with third-party inference providers. Normalize APIs, streaming, capabilities, errors, authentication, and rate limits; implement cost controls, circuit breakers, monitoring, contract tests, and failover. Reconcile provider invoices with usage telemetry, evaluate new vendors, and diagnose upstream reliability issues using observability tools. The role requires backend engineering experience, production API integration expertise, LLM provider knowledge, and usage-billing monitoring experience.

Manage financial reporting, monthly close, audits, SEC filings, accounting research, internal controls, and reporting processes. Prepare financial statements, management reports, accounting memoranda, policies, and supporting documentation while ensuring compliance with IFRS, US GAAP, and applicable reporting requirements. Coordinate with auditors and internal teams, analyze operating results, and improve financial reporting processes.

Own the full B2B sales cycle for regional Model-as-a-Service opportunities, from qualified handoff through pricing, negotiation, contracting, and signature. Responsibilities include managing quota, self-sourcing opportunities, forecasting pipeline in CRM, coordinating with Legal, Commercial, and Solutions Engineering, and refining commercial guardrails. The role targets candidates with 5–8 years of quota-carrying sales experience, preferably in cloud infrastructure, SaaS, AI/ML, or usage-based cloud offerings.

Leads full-cycle recruitment for semiconductor, engineering, and technology roles. Develops sourcing strategies, engages passive candidates, maps technical talent markets, manages recruitment channels, advises hiring leaders, assesses candidates, supports employer branding, ensures legal compliance, and improves talent acquisition processes. The role requires strong semiconductor recruiting expertise, international hiring experience, stakeholder management, market intelligence, and proficiency with recruiting technology.

Lead product strategy, portfolio, commercialization, and organizational development for a global AI cloud infrastructure platform. Own GPU compute, bare metal, containers, networking, storage, AI training and inference products, including pricing, metering, SLAs, utilization, revenue, and margins. Partner across engineering, infrastructure, operations, sales, and solutions teams while building a high-performing product organization and scaling standardized cloud capabilities for developers and enterprise customers.

Own frontend development for Bitdeer’s AI Cloud console, instance management, and Kubernetes cluster management modules. Design frontend architecture, improve performance and stability, establish engineering standards, review code, document best practices, and collaborate with product, design, and backend teams to deliver reliable cloud infrastructure experiences.

Lead productization of high-performance computing chips across silicon, boards, firmware, drivers, and servers. Own architecture, silicon bring-up, cross-layer debugging, validation, system performance, stability, production readiness, and customer delivery. Translate inference workloads into system requirements, define testing strategies, coordinate ODM/JDM and engineering teams, resolve root causes, and balance schedule, quality, cost, and performance.

Design and implement scalable Kubernetes control plane architectures (Cluster API, Operators, CRDs), optimize etcd, ensure secure multi-tenant API isolation, enable zero-downtime upgrades and multi-cluster federation, integrate with GPU orchestration, lead design reviews, mentor engineers, and drive platform reliability.