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Full Time Topology Optimization Jobs (NOW HIRING)

Figure

Helix AI Engineer, Training Performance

San Jose, CA · On-site

$200K - $400K/yr

... optimizing GPU kernels, exploring the relative gains of different accelerator types and co ... Collaborate with the broader team on accelerator choice, cluster topology, scheduling, and hardware ...

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Full Time Topology Optimization information

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$55.8K

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

As of Aug 16, 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?

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.
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Full Time Topology Optimization jobs near you
Infographic showing various Full Time Topology Optimization job openings in the United States as of August 2026, with employment types broken down into 1% Internship, 86% Full Time, 8% Part Time, and 5% Contract. Highlights an 80% Physical, 5% Hybrid, and 15% Remote job distribution, with an average salary of $55,794 per year, or $26.8 per hour.

Helix AI Engineer, Training Performance

Figure

San Jose, CA • On-site

$200K - $400K/yr

Full-time

Posted 3 days ago

New

Job description

Figure is an AI robotics company developing autonomous general-purpose humanoid robots. The goal of the company is to ship humanoid robots with human level intelligence. Its robots are engineered to perform a variety of tasks in the home and commercial markets. Figure is headquartered in San Jose, CA.
Figure's vision is to deploy autonomous humanoids at a global scale. Our Helix team is looking for an experienced AI Training Performance Engineer to take our model training to the next level. This role is focused on improving distributed training frameworks for large scale model training, optimizing GPU kernels, exploring the relative gains of different accelerator types and co-designing our models to maximize utilization of our hardware.
Responsibilities
  • Optimize training performance for a 100B+ parameter models across 100k+ GPUs.
  • Collaborate with the broader team on accelerator choice, cluster topology, scheduling, and hardware procurement decisions to inform future scaling.
  • Write and optimize custom kernels (Triton/CUDA)
  • Build tooling and dashboards for continuous performance monitoring, regression detection, and root-cause analysis across training jobs
  • Optimize data loading and preprocessing pipelines so I/O never gates the accelerators
  • Improve checkpointing, fault tolerance, and elastic restart so large jobs recover quickly from node failures without losing significant wall-clock time
  • Partner with researchers to co-design model architectures and training recipes that are performant at scale (e.g., activation checkpointing strategies, mixed precision, sequence packing)
  • Extend and contribute to kernel compilers (e.g., Triton, Gluon) to improve iteration speed and enable targeting of custom/non-NVIDIA accelerators
  • Build and extend agentic systems that automatically generate, benchmark, and iterate on custom kernels
  • Evaluate emerging accelerator architectures (AMD, TPU, SRAM-based ASICs, and other novel hardware) for fit with our training workloads, and lead proof-of-concept ports/benchmarks
  • Explore different model/data parallelisms (FSDP, context parallel, expert parallel, etc.) to determine optimal configuration per model size.

Requirements
  • Bachelor's or Master's degree in Computer Science, Computer/Electrical Engineering, or a related field
  • 3+ years in AI performance engineering, with significant time leading large-scale performance improvement projects
  • Deep understanding of GPU architecture and performance characteristics (memory bandwidth, compute-bound vs. memory-bound ops, occupancy)
  • Proficiency with profiling tools (Nsight Systems/Compute, PyTorch Profiler, HTA, or similar) and ability to translate traces into concrete optimizations
  • Solid grasp of collective communication (NCCL) and modern networking concepts (RDMA, NVLink, InfiniBand/RoCE, topology-aware placement).
  • Strong Python and CUDA/C++ skills; comfortable reading and modifying framework internals
  • Experience debugging performance regressions and instability at scale (stragglers, hangs, OOMs, numerical divergence)
  • Experience defining and reasoning about hardware-efficiency metrics (MFU/HFU) and using them to drive optimization priorities

Bonus Qualifications
  • Experience with heterogeneous or multi-datacenter training setups and cross-cluster orchestration
  • Contributions to open-source ML systems projects (PyTorch, Megatron-LM, vLLM, DeepSpeed, JAX, etc.)
  • Exposure to non-NVIDIA accelerators (AMD GPUs, TPU/Trainium/Inferentia, or custom silicon) and heterogeneous fleet management.

The US base salary range for this full-time position is between $200,000 - $400,000 annually.
The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.

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