Bitdeer (NASDAQ: BTDR)
AI Engineering – New College Graduate 2027
San Jose, California · Junior
Sponsorship not specifiedDetected 13 days ago
PythonJavaGoRustC++Distributed SystemsFull-Stack DevelopmentPostgreSQLRedisCloud PlatformsDockerKubernetesTerraformHelmPrometheusGrafanaSite Reliability EngineeringPlatform EngineeringMachine LearningLLMsRAGAgentic AILLMOpsMLOps
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odds of building a lasting career here
37Risky
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
Thin sponsorship signal and lottery-bound. A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.
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About the role
- At Bitdeer AI, you will architect the actual backbone of global artificial intelligence.
- Your work will directly power the global AI ecosystem, from ambitious startups and elite research institutions to global enterprises.
- Artificial intelligence is reshaping human potential, but the future of AI relies on real-world compute, reliable infrastructure and engineering, and massive power.
Nice to have
- Cloud Platform Engineering
- AI Operations, MLOps & Site Reliability Engineering
- MaaS, Model Serving & AI Training Infrastructure
- AI Agent & Application Engineering
- AI & Cloud Security Engineering
- Fresh graduates (Bachelors, Masters, PhD) from all disciplines and/or candidates with up to 2 years of related work experience.
- Strong programming ability in one or more languages, including Go, Python, C++, Java, Rust, or related technologies.
- Experience with Kubernetes, Docker, Helm, Terraform, OpenStack, Prometheus, Grafana, Argo, Istio, PostgreSQL, Redis, message queues, distributed storage, or other cloud-native technologies is highly preferred.
Company info
- We are seeking 2027 New College Graduates: Why Start Your Career at Bitdeer AI?
- Opportunities to rotate across different roles will also be provided as part of career development.
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This listing is sourced directly from Bitdeer (NASDAQ: BTDR)'s careers page and normalized into a canonical job model.