ZipRecruiter

Log In

2

Full Time Topology Optimization Jobs (NOW HIRING)

Futran Tech Solutions Pvt. Ltd.

Oracle Apps DBA

Sunnyvale, CA · On-site

$60.50 - $82.25/hr

Oracle Apps DBA Sunnyvale, CA (Onsite 3days/week) Full-Time Technical skills: * Thorough and In ... Perform database performance tuning, including query optimization and indexing. * In-Depth ...

WSSC Water

Sr. Data Network Engineer

Laurel, MD · On-site

$103K - $141K/yr

... Full Time If Part Time how many hours per week Regular or Temporary Regular Position End Date (if ... optimization * Develops and maintains documentation, including addressing plans, network topology ...

New

next page

Showing results 1-20

All JobsTopology Optimization JobsFull Time Topology Optimization Jobs

Full Time Topology Optimization information

See salary details

$16K

$55.8K

$102K

How much do full time topology optimization jobs pay per year?

As of Jun 13, 2026, the average yearly pay for full time topology optimization in the United States is $55,794.00, according to ZipRecruiter salary data. Most workers in this role earn between $36,000.00 and $72,500.00 per year, depending on experience, location, and employer.

What are some of the common challenges faced by professionals working in topology optimization, and how do they typically address them?

Professionals in topology optimization often encounter challenges such as balancing computational efficiency with design accuracy, managing complex simulation data, and interpreting results to create manufacturable solutions. Working closely with multidisciplinary teams—including design engineers and manufacturing specialists—helps address these challenges by ensuring that optimized designs are both practical and innovative. Staying up to date with the latest software tools and optimization algorithms, as well as collaborating on iterative prototyping, are also crucial strategies for overcoming common obstacles in this field.

What is the difference between Full Time Topology Optimization vs Structural Engineer?

AspectFull Time Topology OptimizationStructural Engineer
CredentialsEngineering degree, optimization software skillsEngineering degree, structural analysis certifications
Work EnvironmentDesign firms, R&D departments, manufacturingConstruction sites, design offices, consulting firms
Industry UsageProduct design, aerospace, automotive, manufacturingBuilding design, infrastructure, bridges, buildings

Full Time Topology Optimization specialists focus on optimizing material layouts using computational methods, often within product development and manufacturing sectors. Structural Engineers design and analyze physical structures like buildings and bridges, ensuring safety and compliance. While both roles require engineering knowledge, they differ in focus: topology optimization emphasizes computational design, whereas structural engineering emphasizes physical structure safety and integrity.

What are the key skills and qualifications needed to thrive as a Topology Optimization Engineer, and why are they important?

To thrive as a Topology Optimization Engineer, you generally need a strong background in mechanical engineering, applied mathematics, or a related field, often supported by an advanced degree. Proficiency in simulation software such as ANSYS, Abaqus, or Altair OptiStruct, as well as experience with CAD tools and programming languages like Python or MATLAB, is typically required. Strong problem-solving abilities, creativity, and effective communication skills help you interpret complex data and collaborate with multidisciplinary teams. These combined skills are essential for developing innovative, efficient designs that meet performance and manufacturing constraints.

What is a Full Time Topology Optimization Engineer?

A Full Time Topology Optimization Engineer is a professional who specializes in using mathematical and computational methods to design structures and components with optimal material layouts. This role typically involves utilizing software tools to analyze and improve parts for performance, weight reduction, and material efficiency, often in industries such as aerospace, automotive, and manufacturing. The engineer collaborates with design and engineering teams to ensure products meet performance, cost, and manufacturability goals. This is a full-time position, meaning the engineer works standard business hours and is fully dedicated to topology optimization projects within their organization.
More about Full Time Topology Optimization jobs
What cities are hiring for Full Time Topology Optimization jobs? Cities with the most Full Time Topology Optimization job openings:
What are the most commonly searched types of Topology Optimization jobs? The most popular types of Topology Optimization jobs are:
What states have the most Full Time Topology Optimization jobs? States with the most job openings for Full Time Topology Optimization jobs include:
What job categories do people searching Full Time Topology Optimization jobs look for? The top searched job categories for Full Time Topology Optimization jobs are:
Full Time Topology Optimization jobs near you

Senior Site Reliability Engineer - AI Infrastructure

Andromeda Cluster, Inc

San Francisco, CA • On-site, Remote

$67.25 - $89.25/hr

Full-time

Posted 21 days ago

Job description

Senior Site Reliability Engineer - AI Infrastructure
Location: Global Remote / San Francisco • Full-Time
About Andromeda
Andromeda Cluster was founded by Nat Friedman and Daniel Gross to give early-stage startups access to the kind of scaled AI infrastructure once reserved only for hyperscalers.
We began with a single managed cluster - but it filled almost instantly. Since then, we've been quietly building the systems, network, and orchestration layer that makes the world's AI infrastructure more accessible.
Today, Andromeda works with leading AI labs, data centers, and cloud providers to deliver compute when and where it's needed most. Our platform routes training and inference jobs across global supply, unlocking flexibility and efficiency in one of the fastest-growing markets on earth.
Our long-term vision is to build the liquidity layer for global AI compute - a marketplace that moves the infrastructure and workloads powering AGI not dissimilar to the flows of capital in the world's financial markets.
We are expanding to new frontiers to find the brightest that work in AI infrastructure, research and engineering.
The Role
This is not a generalist SRE role.
You will design, operate, and debug large-scale GPU infrastructure used for distributed training and inference, working directly with customers pushing the limits of modern AI systems.
We're looking for engineers who have personally run GPU clusters in production, understand the failure modes of distributed training, and can reason about performance from network fabric → kernel → framework.
What You'll Own
  • GPU Cluster Architecture: Design and evolve multi-provider, multi-region GPU compute clusters optimized for large-scale training. Make topology-aware scheduling, networking, and storage decisions that directly impact training throughput and cost efficiency.
  • Customer Technical Partnership: Serve as the primary technical point of contact for customers running large-scale training workloads. Onboard, troubleshoot, and optimize, often in real time.
  • Reliability & Performance Engineering: Define SLOs and error budgets that account for the unique failure modes of GPU infrastructure (ECC errors, NVLink degradation, NCCL timeouts). Own capacity planning across heterogeneous GPU fleets optimized for training throughput.
  • Networking & Fabric Health: Ensure the health and performance of high-speed interconnects (InfiniBand, RoCE, NVLink) that underpin distributed training. Diagnose and resolve fabric-level issues that degrade collective operations.
  • Observability: Build deep visibility into GPU utilization, memory pressure, interconnect throughput, training job performance, and hardware health. Go well beyond standard infrastructure metrics.
  • Automation & Tooling: Build production-grade automation for cluster provisioning, GPU health checks, job scheduling, self-healing, and firmware/driver lifecycle management.
  • Incident Leadership: Lead incident response for complex, multi-layer failures spanning hardware, networking, orchestration, and ML frameworks. Drive blameless postmortems and systemic fixes.

What We're Looking For
  • GPU Systems Expertise: Deep, hands-on experience operating large-scale GPU clusters (NVIDIA A100/H100/B200 or equivalent). You understand GPU memory hierarchies, ECC behavior, thermal throttling, and hardware failure modes from direct experience not documentation.
  • High-Performance Networking: Production experience with InfiniBand, RoCE, or NVLink fabrics in the context of distributed training. You can diagnose why an all-reduce is slow, identify a degraded link in a fat-tree topology, and reason about congestion control at scale.
  • Distributed Training & ML Frameworks: Working knowledge of how large training jobs actually run - NCCL, CUDA, PyTorch distributed, DeepSpeed, Megatron, FSDP, or similar. You don't need to write the models, but you need to understand what's happening at the systems level when a 1,000-GPU training run stalls.
  • Linux & Systems Internals: Expert-level Linux knowledge: kernel tuning, driver management (NVIDIA drivers, CUDA toolkit), cgroup/namespace internals, performance profiling at the syscall and hardware level.
  • Kubernetes & Orchestration: Strong experience running Kubernetes in production with GPU workloads, including device plugins, topology-aware scheduling, multi-cluster federation, and custom operators. Experience with Slurm or other HPC schedulers is equally valued.
  • Automation & Software Engineering: Strong engineering skills in Python, Go, or Bash. You build production-grade tools and services, not just scripts. Infrastructure-as-Code proficiency (Terraform, Helm, Ansible, or equivalent).
  • Observability & Monitoring: Hands-on experience building monitoring and alerting for GPU infrastructure, not just Prometheus/Grafana basics, but GPU-specific telemetry (DCGM, nvidia-smi, fabric manager metrics) integrated into actionable dashboards.
  • Incident Management: Proven track record leading incident response for complex distributed systems where the failure could be in hardware, firmware, networking, drivers, orchestration, or application code and you need to narrow it down fast.

Strong Candidates May Have
  • Distributed Storage: Experience with high-performance parallel file systems (VAST, Weka, Lustre, GPFS) and the checkpoint I/O and data-loading bottlenecks that come with large training runs.
  • Training Optimization: Experience profiling and optimizing distributed training performance: identifying stragglers, tuning collective communication strategies, improving MFU (Model FLOPs Utilization), and reducing idle GPU time across large runs.
  • Cluster Buildout & Hardware: Experience involved in physical cluster design - rack layout, power/cooling constraints, network topology design, and hardware validation/burn-in at scale.
  • Team Leadership: Experience leading or mentoring a team of infrastructure engineers. We're growing and need people who raise the bar for everyone around them.

Why You'll Love It Here
This is a high-impact, senior builder's role. You'll have significant ownership and autonomy to shape how our systems run at a foundational level, working directly with customers and providers while architecting the infrastructure backbone for reliable, scalable AI compute. You'll influence technical direction and help define what world-class AI infrastructure operations look like.
Andromeda Cluster is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Apply