採用
必須スキル
HPC
Systems design
Performance tuning
Security
Observability
Product thinking
Engineering thinking
Roadmapping
Prioritization
Stakeholder Management
Communication
Career Category
Engineering
Job Description
Position Overview
The GCF5 Site Reliability Engineer is the senior technical leader for the HPC Enablement pillar. They define and socialize operational standards and patterns, lead multi-team delivery, mentor GCF4 engineers, and translate researcher needs into scalable compute enablement designs. They own pillar-level reliability, performance, cost efficiency, and SLA/SLO outcomes, and influence cross-team engineering quality.
This role reports to the GCF7 leader and partners closely with peer GCF5 domain leads across SCIP to ensure cohesive, scalable platform evolution.
Core Responsibilities
- Own the compute reliability and enablement roadmap within SCIP.
- Define onboarding playbooks and golden paths for HPC workloads.
- Establish containerization and reproducible runtime standards.
- Optimize scheduler configuration and resource allocation policies.
- Conduct workload profiling and performance tuning.
- Define and manage SLOs, reliability standards, and operational guardrails.
- Lead incident response and reduce recurring failures.
- Mentor engineers and elevate reliability practices.
- Partner with scientific teams to translate compute requirements into scalable infrastructure patterns.
Core Competencies
- Deep expertise in HPC Enablement (HPC) with evidence of standard‑setting and reuse.
- Systems design at scale (HPC); performance, security, and observability fundamentals.
- Product/engineering thinking: road mapping, prioritization, and outcome‑oriented delivery.
- Stakeholder influence across science, engineering, and governance forums; crisp written/verbal communication.
Core Success Measures
- HPC job success rate improvement.
- Reduction in MTTR for compute incidents.
- Performance improvements relative to baseline.
- Time-to-onboard new scientific workloads.
- Improvement in cost-per-compute-hour efficiency.
- Reduction in operational toil via automation.
Key Relationships
- Collaborates with GCF6 Group Lead and cross‑functional leaders (R&D/PD/Dev).
- Mentors and develops GCF4 Data and Software Engineers, partners with platform, data, ML, and research teams.
- Interfaces with governance (architecture, security, compliance) and vendor/partner teams.
Decision Authority
- Approve designs within the pillar; define and waive standards/patterns with rationale.
- Recommend buy‑vs‑build; commit pillar resources to meet SLAs/SLOs; escalate risks.
- Prioritize pillar backlog and roadmap in alignment with strategy and OKRs.
Qualifications
Basic Qualifications:
- BS+8 / MS+6 / PhD in CS/Engineering/Data disciplines.
- Demonstrated production delivery experience in HPC at scale.
- Demonstrated literacy in a relevant scientific domain (e.g., biology, chemistry, therapeutic discovery).
Preferred Qualifications:
- Depth in HPC Enablement (HPC).
- Kubernetes and continuous integration/continuous delivery (CI/CD) at scale; observability, performance tuning, and security-by-design.
- Evidence of standard‑setting and cross‑team influence; mentoring experience.
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総閲覧数
1
応募クリック数
0
模擬応募者数
0
スクラップ
0
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Amgenについて

Amgen
PublicA biotechnology company that develops and manufactures human therapeutics for various illnesses and diseases.
10,001+
従業員数
Thousand Oaks
本社所在地
$138B
企業価値
レビュー
3.6
10件のレビュー
ワークライフバランス
3.2
報酬
4.1
企業文化
3.4
キャリア
2.8
経営陣
3.5
65%
友人に勧める
良い点
Excellent benefits and health benefits
Good pay and compensation
Supportive management and strong leadership
改善点
Limited career growth and promotion opportunities
Work-life balance challenges and long hours
Bureaucratic processes
給与レンジ
1,244件のデータ
L2
L3
L4
L5
L6
L2 · Financial Analyst L2
0件のレポート
$94,068
年収総額
基本給
$37,627
ストック
$47,034
ボーナス
$9,407
$65,848
$122,288
面接体験
5件の面接
難易度
3.0
/ 5
期間
14-28週間
内定率
40%
体験
ポジティブ 20%
普通 80%
ネガティブ 0%
面接プロセス
1
Application Review
2
HR Screen
3
Hiring Manager Interview
4
Technical/Role-Specific Interview
5
Panel Interview
6
Offer
よくある質問
Technical Knowledge
Behavioral/STAR
Past Experience
Data Analysis/Statistics
Culture Fit
ニュース&話題
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