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Remote Fpga Engineer Jobs in Nevada (NOW HIRING)

Qualus

Lead Engineer - Substation Physical Design

Las Vegas, NV · On-site +1

$152K - $159K/yr

Substation Design Engineers are responsible for designing substations utilizing applicable codes and client standards. Typical design to include general arrangement, equipment and electrical layout ...

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Remote Fpga Engineer information

See Nevada salary details

$86.6K

$150K

$202.1K

How much do remote fpga engineer jobs pay per year?

As of Sep 4, 2026, the average yearly pay for remote fpga engineer in Nevada is $150,012.00, according to ZipRecruiter salary data. Most workers in this role earn between $132,400.00 and $170,100.00 per year, depending on experience, location, and employer.

What is a remote FPGA engineer?

A Remote FPGA Engineer designs, develops, and optimizes FPGA (Field-Programmable Gate Array) solutions while working remotely. They use hardware description languages (HDLs) like VHDL or Verilog to program FPGA devices for applications such as signal processing, embedded systems, and high-speed computing. Responsibilities typically include simulation, synthesis, debugging, and collaboration with hardware and software teams. Remote FPGA Engineers communicate through digital tools and often work across different time zones. This job requires strong analytical skills, FPGA design experience, and proficiency in development tools like Xilinx Vivado or Intel Quartus.

What are the key skills and qualifications needed to thrive as a remote FPGA engineer?

To thrive as a Remote FPGA Engineer, you need a solid background in digital logic design, proficiency in hardware description languages (HDLs) like VHDL or Verilog, and a relevant engineering degree. Familiarity with industry-standard FPGA development tools such as Xilinx Vivado, Intel Quartus, and simulation software, as well as experience with version control systems, is highly valuable. Strong problem-solving abilities, effective written communication, and self-motivation are essential soft skills in remote settings. These competencies ensure the successful design, verification, and implementation of FPGA solutions while enabling productive collaboration in distributed teams.

What are some common challenges faced by remote FPGA engineers, and how can they be managed effectively?

Remote FPGA Engineers often encounter challenges such as coordinating with distributed teams across different time zones, accessing specialized hardware for testing, and maintaining clear communication around project requirements. To address these, companies typically provide remote access to lab equipment, schedule regular video meetings, and utilize project management platforms for effective collaboration. Building strong documentation habits and proactive communication skills can also help streamline workflows and minimize misunderstandings. By staying organized and leveraging your team's support and resources, you can overcome these challenges and maintain productivity from a remote setting.

What are the most commonly searched types of Fpga Engineer jobs in Nevada?

The most popular types of Fpga Engineer jobs in Nevada are:

What are popular job titles related to Remote Fpga Engineer jobs in Nevada?

For Remote Fpga Engineer jobs in Nevada, the most frequently searched job titles are:

What job categories do people searching Remote Fpga Engineer jobs in Nevada look for?

The top searched job categories for Remote Fpga Engineer jobs in Nevada are:

What cities in Nevada are hiring for Remote Fpga Engineer jobs?

Cities in Nevada with the most Remote Fpga Engineer job openings:

Remote Fpga Engineer jobs near you
Infographic showing various Remote Fpga Engineer job openings in Nevada as of August 2026, with employment types broken down into 91% Full Time, 5% Part Time, and 4% Contract. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution, with an average salary of $150,012 per year, or $72.1 per hour.

Software Engineer, ML Dev Enablement

Motional

Las Vegas, NV • On-site, Remote

Full-time

Re-posted 6 days ago

Job description

Mission Summary:

We are looking for a Software Engineer to join our ML Infrastructure: Dev Enablement Team. Our mission is to build a frictionless development environment that empowers our researchers and engineers to rapidly innovate on deep learning models for autonomous driving.

We manage a high-scale Cloud Development Environment (CDE) platform that provides standardized, high-performance workspaces for ML development. As we evolve, in this role, you'll spearhead high-impact initiatives: designing multi-cloud setups to maximize GPU availability, driving deep-level model optimization, and building next-generation Agentic AI toolings. You will play a pivotal role in ensuring our training ecosystem remains cutting-edge, resilient and highly efficient.

What You'll Be Doing:

  • Build Agentic AI Tooling: Design, develop, and enhance Agentic AI tools and systems to automate workflows, streamline the ML lifecycle, and empower developer productivity.
  • Scale Core Infrastructure: Drive the continuous development of our core ML infrastructure and existing CDE platform, leveraging Kubernetes to build robust, high-scale distributed solutions.
  • System-Level ML Optimization: Partner closely with ML Researchers to profile and optimize distributed training jobs (PyTorch/DDP) and data pipelines. Focus on resolving system-level bottlenecks-such as data loading (I/O), memory management, and network communication overhead-to maximize GPU utilization and training throughput.
  • Collaborate Cross-Functionally: Partner with ML engineers and data scientists to understand their complex needs, bridging the gap between underlying infrastructure and model development.

What We're Looking For:

  • BS or MS in Computer Science or related field
  • Strong knowledge of software engineering principles and distributed systems.
  • Strong proficiency with Python or Go or C++
  • Experience with building on AWS services or other Cloud platforms and container orchestration using Kubernetes.
  • Experience with the various stages of the ML development lifecycle
Bonus Points:
  • Hands-on experience with ML model profiling and performance optimization for distributed training.
  • Experience managing or working with high-performance compute resources (GPUs).
  • Experience with ML frameworks such as PyTorch or Ray.
  • Experience building, integrating, or enhancing Agentic AI systems and LLM-driven developer tools.

 We encourage a hybrid schedule with in-office time at our Las Vegas location to support collaboration, or this role can be fully remote.

Apply