Engineering Manager, Storage
Lead high-performing engineering teams to scale storage performance for massive AI workloads at CoreWeave.
Posted by employer 1 month ago
First seen on Joblaze 1 month ago
Last verified on the company career page 1 day ago
What you'll build
- Scale foundational file and block and object storage systems
- Drive end-to-end design and operational excellence
- Solve complex engineering challenges
- Lead systems engineering teams
- Align engineering execution with strategic business goals
Must have
- 3+ years managing engineering teams
- 7+ years in hands-on systems or infrastructure engineering
- Deep technical expertise in storage performance tuning
- Proficiency in systems programming with Go and Rust
Role intensity
10% coding — mostly leadership/strategy
Requirements
- Experience
- 10+ years
Not disclosed in this posting: work arrangement, visa sponsorship.
Benefits
Joblaze summary
In the role of Engineering Manager for Storage at CoreWeave, the individual will lead teams focused on developing and scaling high-throughput distributed storage systems to support AI workloads. Key skills include expertise in cloud storage technologies and proficiency in programming languages like Go and Rust, with a strong emphasis on performance tuning and latency optimization. This position is suited for someone with significant experience in systems engineering and team management, particularly within hyper-scale cloud environments. CoreWeave promotes a culture of autonomy and collaboration, aiming to tackle complex engineering challenges in AI infrastructure.
Joblaze insights
- Listed about a month ago — first seen on Joblaze September 11, 2026. Last confirmed on Coreweave's careers page October 27, 2026.
- Salary band is in line with the typical range for Management roles (median ~$172,000).
- Starts above 17% of 23 comparable lead management roles in United States that list Go we track (median $200,000 across 13 companies). See Go salary trends
- Go appears in 6% of 587 comparable lead management roles in United States; Rust appears in 2.6% of 587 comparable lead management roles in United States.
Quick facts
- What's the salary range?
- Coreweave lists $182,000–$242,000 for this role.
- How much experience is required?
- At least 10 years of relevant experience for this Engineering Manager, Storage role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: Ceph, GPUDirect Storage, Go, Lustre, MinIO, Rust.
- What seniority level is this role?
- Coreweave targets lead candidates for this position.
- Is this full-time or contract?
- Full-time for this Engineering Manager, Storage role at Coreweave.
From the original posting
About the Role
As an Engineering Manager, Storage, you will lead high-performing systems engineering teams driving high-throughput distributed storage architectures and tackling the challenge of scaling storage performance by 10x for massive AI workloads. You will oversee file and block and object storage development to maximize GPU utilization across AI cloud infrastructure. In this role, you will balance technical guidance with strategic execution, performance management and cross-functional leadership in a fast-paced environment.
What You’ll Do
- Scale foundational file and block and object storage systems powering high-performance computing AI infrastructure.
- Drive end-to-end design and operational excellence across file and block and object storage services built in Go and Rust to maximize GPU utilization.
- Solve complex engineering challenges to achieve 10x performance scaling in file and block and object storage systems.
- Lead systems engineering teams in a coding-focused, meeting-light culture with high autonomy and impact.
- Provide technical mentorship, oversee performance management and foster innovation.
- Align engineering execution with strategic business goals through effective cross-team leadership.
Who You Are
- Deep technical expertise in storage performance tuning, latency optimization and benchmarking for file and block and object storage architectures.
- 3+ years managing engineering teams plus 7+ years in hands-on systems or infrastructure engineering.
- Technical expertise in cloud storage technologies and distributed systems such as Ceph, Lustre, MinIO and GPUDirect Storage.
- Proven track record scaling storage engineering teams within hyper-scale cloud environments.
- Demonstrated success operating ultra-low-latency systems across high-performance computing or AI infrastructure.
- Proficiency in systems programming with Go and Rust.
- Demonstrated capability in technical recruitment and team growth.
Wondering if you’re a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk.
- You are driven to solve complex low-latency systems challenges and scale top-tier engineering teams.
- You are eager to push the boundaries of GPU optimization, next-generation distributed systems and massive-scale AI infrastructure.
- You are an expert in cloud storage, file and block and object storage systems and low-level systems programming.
Why CoreWeave?
We care deeply about how we build our products and how we collaborate, anchored by our core values:
We foster an environment of high autonomy and collaboration to solve complex technical challenges. You will work alongside industry leaders driving breakthrough engineering in AI cloud infrastructure.
The base salary range for this role is $182,000 to $242,000. The starting salary will be determined by job-related knowledge, skills, experience and the market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards and a comprehensive benefits program (all based on eligibility).
Standard company text repeated across Coreweave's postings is omitted here.