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Fpga Deep Learning Jobs (NOW HIRING)

K2 Space

Senior FPGA Engineer

Los Angeles, CA · On-site

$120K - $225K/yr

... deep space. The rise of heavy-lift launch vehicles is shifting the industry from an era of mass ... For now, this does not include front-end, artificial intelligence, or machine learning development.

K2 Space

Senior FPGA Engineer

Los Angeles, CA · On-site

$120K - $225K/yr

... deep space. The rise of heavy-lift launch vehicles is shifting the industry from an era of mass ... For now, this does not include front-end, artificial intelligence, or machine learning development.

Etched

Performance Modeling Engineer

San Jose, CA · On-site

The Performance Modeling Engineer will develop performance models and analyze deep learning ... FPGA, or CGRA-based accelerator development and hardware/software co-design principles • ...

E-Space

Staff FPGA Engineer (DSP)

Saratoga, CA · On-site

$100K - $200K/yr

... candidates with passion, deep knowledge and direct experience on LEO satellite component ... learning and development • Health and wellness care options • Financial solutions for the ...

E-Space

Staff FPGA Engineer (DSP)

Saratoga, CA

$100K - $200K/yr

... candidates with passion, deep knowledge and direct experience on LEO satellite component ... learning and development • Health and wellness care options • Financial solutions for the ...

E-Space

Staff FPGA Engineer (DSP)

Saratoga, CA

$100K - $200K/yr

... candidates with passion, deep knowledge and direct experience on LEO satellite component ... learning and development Health and wellness care options Financial solutions for the future ...

E-Space

FPGA / VLSI Engineer

Saratoga, CA · On-site

$120K - $220K/yr

... candidates with passion, deep knowledge and direct experience on LEO satellite component ... learning and development • Health and wellness care options • Financial solutions for the ...

E-Space

FPGA / VLSI Engineer

Saratoga, CA · On-site

$120K - $220K/yr

... candidates with passion, deep knowledge and direct experience on LEO satellite component ... learning and development Health and wellness care options Financial solutions for the future ...

E-Space

FPGA / VLSI Engineer

Saratoga, CA

$120K - $220K/yr

... candidates with passion, deep knowledge and direct experience on LEO satellite component ... learning and development • Health and wellness care options • Financial solutions for the ...

HPR

Senior FPGA Design Engineer

Needham, MA · On-site

$159K - $215K/yr

Lead and mentor junior engineers, promoting our culture of continuous learning and collaboration ... Deep understanding of computer architecture and digital design concepts * Experience with industry ...

HPR

Senior FPGA Design Engineer

Needham, MA · On-site

$159K - $215K/yr

Lead and mentor junior engineers, promoting our culture of continuous learning and collaboration ... Deep understanding of computer architecture and digital design concepts * Experience with industry ...

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All JobsFpga Deep Learning Jobs

Fpga Deep Learning information

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$70K

$147.1K

$210.5K

How much do fpga deep learning jobs pay per year?

As of Jun 7, 2026, the average yearly pay for fpga deep learning in the United States is $147,056.00, according to ZipRecruiter salary data. Most workers in this role earn between $123,000.00 and $169,500.00 per year, depending on experience, location, and employer.

What are FPGA Deep Learning engineers?

FPGA Deep Learning engineers are professionals who design, implement, and optimize deep learning models to run efficiently on Field-Programmable Gate Arrays (FPGAs). FPGAs are specialized hardware chips that can be programmed to perform specific computational tasks at high speeds and low power consumption. These engineers bridge the gap between artificial intelligence algorithms and hardware, ensuring that neural networks and AI applications can leverage FPGA advantages such as parallelism and flexibility. Their work is crucial in industries requiring real-time data processing, like autonomous vehicles, robotics, and edge computing.

What is the difference between Fpga Deep Learning vs Machine Learning Engineer?

AspectFpga Deep LearningMachine Learning Engineer
Required CredentialsBachelor's or higher in CS, EE, or related; knowledge of FPGA programming and deep learning frameworksBachelor's or higher in CS, Data Science, or related; expertise in ML algorithms and software development
Work EnvironmentHardware-focused, embedded systems, FPGA development labsSoftware-focused, data centers, cloud platforms, or research labs
Industry UsageEmbedded AI, edge computing, specialized hardware accelerationData analysis, predictive modeling, software solutions across industries

While both roles involve AI and machine learning, Fpga Deep Learning specialists focus on hardware acceleration using FPGAs to optimize deep learning models, whereas Machine Learning Engineers develop and deploy ML algorithms primarily in software environments. The roles often overlap in AI projects but differ in technical focus and work environment.

What are the key skills and qualifications needed to thrive as an FPGA Deep Learning Engineer, and why are they important?

To thrive as an FPGA Deep Learning Engineer, you need a solid background in digital design, hardware description languages (such as VHDL or Verilog), deep learning frameworks, and a relevant degree in electrical engineering, computer engineering, or a similar field. Familiarity with FPGA development tools (like Xilinx Vivado or Intel Quartus), hardware accelerators, and experience with deploying neural networks on embedded systems are typically required. Problem-solving ability, attention to detail, and strong collaboration skills are key soft skills that make a candidate stand out. These skills and qualities are essential for efficiently bridging the gap between AI algorithms and hardware implementations, ensuring high-performance, reliable solutions.

How do professionals in FPGA Deep Learning roles typically collaborate with software and data science teams?

FPGA Deep Learning professionals often work closely with software engineers and data scientists to optimize deep learning models for hardware acceleration. This collaboration involves translating neural network architectures from high-level frameworks (like TensorFlow or PyTorch) into efficient hardware implementations, communicating constraints or opportunities for parallelization, and iteratively refining models for performance. Regular meetings and code reviews are common to ensure alignment between hardware and software development. Effective communication and understanding of both domains are essential for successfully deploying deep learning solutions on FPGA platforms.
More about Fpga Deep Learning jobs
What cities are hiring for Fpga Deep Learning jobs? Cities with the most Fpga Deep Learning job openings:
What states have the most Fpga Deep Learning jobs? States with the most job openings for Fpga Deep Learning jobs include:
What job categories do people searching Fpga Deep Learning jobs look for? The top searched job categories for Fpga Deep Learning jobs are:
Fpga Deep Learning jobs near you
Infographic showing various Fpga Deep Learning job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 89% In-person, and 11% Remote job distribution, with an average salary of $147,056 per year, or $70.7 per hour.
Compiler Engineer - Machine Learning Compiler

Compiler Engineer - Machine Learning Compiler

Mythic

Remote

Full-time

Posted 16 days ago

Job description

Job Summary:
Mythic is building the future of AI computing with breakthrough analog technology that delivers 100× the performance of traditional digital systems. The role involves developing the compiler for their novel AI accelerator, collaborating with hardware engineers and ML researchers to optimize deep learning workloads on cutting-edge dataflow hardware.
Responsibilities:
• Contribute across the full compiler stack, including operator lowering, graph/IR transformations, optimization passes, and backend code generation
• Optimize for dataflow architectures, developing pipelined schedules, memory orchestration, and resource-constrained execution strategies
• Collaborate with hardware architects to influence architectural features, ensuring the compiler and hardware evolve together
• Develop compilation strategies that unify our analog compute with digital subsystems
• Build and maintain a compiler that produces high-performance binaries with strong debugging support, clear error messages, and predictable performance models
Qualifications:
Required:
• 3+ years of experience building compilers or high-performance systems software, especially those involving complex resource management or optimization.
• Expert in modern C++ (C++14/17/20) and strong Python.
• Experience with compiler IRs (SSA-based or graph-based), transformations, and code generation
• Exposure to specialized accelerators (GPU, NPU, FPGA, or custom ASIC) or parallel architectures
Preferred:
• Experience with machine learning compiler stacks (e.g., ONNX, MLIR, TVM, XLA, IREE, PyTorch), with contributions to MLIR or LLVM projects a plus
• Experience with optimization methods (LP/MIP, CP, SAT/SMT) using solvers like Gurobi or OR-Tools for scheduling and resource allocation
• Experience compiling for specialized accelerators (GPU, NPU, FPGA, or custom ASIC) on DNN workloads; GPU/DSP experience is valuable if combined with compiler backend work beyond kernel tuning
• Familiarity with heterogeneous compilation, especially mixing custom accelerators with CPUs/GPUs/NPUs, and exposure to analog or in-memory compute is a plus
• Experience collaborating in compiler–hardware co-design (architecture + ISA) for better compiler usability and hardware efficiency
Company:
Mythic develops analog matrix processors and key cards based on analog compute-in-memory. Founded in 2012, the company is headquartered in Austin, USA, with a team of 11-50 employees. The company is currently Early Stage.

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