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Senior Software Engineer, Machine Learning Inference

Summary Description: 

Syntiant Corp., a leader in the high-growth AI software and semiconductor solutions space, has entered into an agreement to acquire a large, multi-national sensor business with global revenues in excess of $250 million per year, and is looking for an experienced and talented Senior Software Engineer of Machine Learning Inference to take on a critical role with expansive responsibilities and play a leading role in enhancing the Machine Learning function in a growing organization.

The Senior Software Engineer of Machine Learning Inference will be responsible for developing the core building blocks that power computer vision, speech, and other application-specific APIs. Our approach to machine learning emphasizes computational and numerical efficiency, achieving orders-of-magnitude improvements over the state-of-the-art. These efficiency gains stem from fundamental ML innovations, built within a generalized production framework that enables rapid deployment of real-world products.

Specific Duties and Responsibilities:

  • Develop, optimize, and maintain high-performance C/C++ software for CPUs, DSPs, NPUs, and GPUs.
  • Profile and optimize inference code for power, latency, and throughput on edge devices.
  • Develop tools to benchmark and profile AI models on resource-constrained platforms.
  • Research and implement state-of-the-art ML models with a focus on computational efficiency.
  • Debug and analyze performance bottlenecks in large-scale deployable software.
  • Collaborate with hardware architects to co-optimize software and hardware solutions.

Qualifications, Education, and Experience Required:

  • BS in Computer Science/Engineering or related field (MS or PhD preferred).
  • 5+ years of industry experience in software development and optimization.
  • Strong C/C++ programming skills with a focus on performance optimization.
  • Experience programming numerical algorithms in ML or related fields.
  • Strong understanding of linear algebra and numerical optimization.
  • Hands-on experience optimizing code for edge devices, including CPUs, DSPs, NPUs, GPUs, or other specialized processors.
  • Deep understanding of low-level computing, memory hierarchies, and parallel processing techniques.
  • Experience with assembly-level optimizations, vectorization (SIMD), and low-level debugging.
  • Familiarity with profiling tools such as perf, gprof, Valgrind, Intel VTune, NVIDIA Nsight, etc.
  • Experience with machine learning inference frameworks (TensorFlow Lite, ONNX Runtime, TVM, etc.).
  • Knowledge of AI model compression, quantization, and pruning techniques.
  • Knowledge of compiler optimizations and JIT compilation techniques.
  • Experience working in large, complex codebases with version control tools like Git.

About Syntiant:

Founded in 2017 and headquartered in Irvine, Calif., Syntiant Corp. is a leader in delivering hardware and software solutions for edge AI deployment. The company’s purpose-built silicon and hardware-agnostic models are being deployed globally to power edge AI speech, audio, sensor and vision applications across a wide range of consumer and industrial use cases, from earbuds to automobiles. Syntiant’s advanced chip solutions merge deep learning with semiconductor design to produce ultra-low-power, high performance, deep neural network processors. Syntiant also provides compute-efficient software solutions with proprietary model architectures that enable world-leading inference speed and minimized memory footprint across a broad range of processors. The company is backed by several of the world’s leading strategic and financial investors including Intel Capital, Microsoft’s M12, Applied Ventures, Bosch Ventures, the Amazon Alexa Fund, and Atlantic Bridge Capital. More information on the company can be found by visiting www.syntiant.com.

One element in our total compensation package is base pay.  The starting base pay for this role is targeted to be between $180,000 - $230,000. Individual compensation decisions are based on a number of factors, including but not limited to previous experience and skills acquired prior to joining Syntiant, cost of living in the assigned work location, assigned schedule, and salaries of similarly situated peers at the company. It is to be expected that candidates will come to us with different sets of skills and experiences and therefore will be paid at different points in the stated range. We recognize that the person(s) we select for hire may be less experienced or more experienced than the role as posted; if this is the case, any updates to available salary ranges will be communicated with candidates during the recruitment process.  

Average salary estimate

$205000 / YEARLY (est.)
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$180000K
$230000K

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What You Should Know About Senior Software Engineer, Machine Learning Inference, Syntiant

Are you ready to take a step into the future of AI? Syntiant Corp., a pioneering leader in AI software and semiconductor solutions, is on the hunt for a standout Senior Software Engineer of Machine Learning Inference. In this exciting role, you'll dive into the world of computer vision, speech, and tailored application APIs, while focusing on achieving unparalleled efficiency in machine learning. Your primary responsibilities will include developing, optimizing, and maintaining high-performance software using C/C++ for diverse platforms including CPUs, DSPs, NPUs, and GPUs. Your expertise will help in profiling inference code to enhance power efficiency, reduce latency, and boost throughput on edge devices. Imagine collaborating with innovative hardware architects to create co-optimized solutions that redefine industry standards. You'll also lead the charge on researching cutting-edge ML models, tailored for computational effectiveness, and troubleshooting performance bottlenecks in scalable software deployments. If you’re passionate about pushing the boundaries of technology and innovation, then this role at Syntiant, where we combine deep learning with advanced semiconductor design, is for you. Join us in shaping the future of edge AI applications, from smart wearables to automotive solutions, and be part of a dynamic team that is committed to excellence and continuous improvement.

Frequently Asked Questions (FAQs) for Senior Software Engineer, Machine Learning Inference Role at Syntiant
What are the responsibilities of a Senior Software Engineer of Machine Learning Inference at Syntiant?

As a Senior Software Engineer of Machine Learning Inference at Syntiant, you'll engage in developing and optimizing high-performance C/C++ software for CPUs, DSPs, NPUs, and GPUs. You'll be responsible for profiling and optimizing code for power, latency, and throughput on edge devices, as well as researching and implementing state-of-the-art ML models with a keen focus on computational efficiency. Furthermore, troubleshooting performance bottlenecks in deployable software and collaborating with hardware architects will be essential aspects of your role.

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What qualifications are required for the Senior Software Engineer position at Syntiant?

To be successful as a Senior Software Engineer of Machine Learning Inference at Syntiant, candidates are typically expected to have a BS in Computer Science or Engineering (MS or PhD preferred) and possess over 5 years of relevant industry experience. Key qualifications include strong C/C++ programming skills with a performance optimization focus, hands-on experience with edge device optimizations, and a solid understanding of numerical algorithms, linear algebra, and parallel processing techniques.

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What programming languages and tools should a Senior Software Engineer at Syntiant be familiar with?

To thrive in the Senior Software Engineer of Machine Learning Inference role at Syntiant, familiarity with C/C++ programming is essential, especially with a focus on performance optimization. Additionally, experience with machine learning inference frameworks like TensorFlow Lite and ONNX Runtime, as well as profiling tools like perf, gprof, and Intel VTune, will be crucial for streamlining efficiency in your work.

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What kind of projects will a Senior Software Engineer at Syntiant be involved in?

At Syntiant, a Senior Software Engineer of Machine Learning Inference will contribute to key projects focusing on enhancing AI applications across various domains such as speech recognition, computer vision, and sensor fusion. You'll develop tools to benchmark AI models and engage in research to implement state-of-the-art models that prioritize computational efficiency while debugging and analyzing large-scale software deployments.

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What is the expected salary range for a Senior Software Engineer of Machine Learning Inference at Syntiant?

The starting base pay for the Senior Software Engineer of Machine Learning Inference role at Syntiant is targeted to be between $180,000 - $230,000. However, individual compensation decisions are influenced by a variety of factors, including prior experience, skills, and living costs. The company recognizes the diversity of candidate experiences and will communicate any updates to salary ranges during the recruitment process.

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Common Interview Questions for Senior Software Engineer, Machine Learning Inference
Can you explain your experience with optimizing C/C++ software for edge devices?

When answering this question, it's important to highlight specific projects where you improved software performance for edge devices. Discuss the techniques you employed such as coding conventions, memory management, or utilizing profiling tools to identify bottlenecks and enhance throughput.

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How do you approach troubleshooting performance bottlenecks in a large-scale software system?

Begin by describing your systematic approach: identifying the bottleneck using profiling tools, analyzing code paths, and implementing targeted optimizations. Mention any specific tools or methodologies you've used in past roles, illustrating your ability to enhance overall system performance.

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What machine learning frameworks are you familiar with, and how have you used them?

Discuss your hands-on experience with frameworks such as TensorFlow Lite, ONNX Runtime, or TVM. Provide examples of projects where you utilized these frameworks to implement ML models, focusing on the challenges faced and how you overcame them.

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Can you describe a complex algorithm you've implemented or optimized? What challenges did you face?

Relate a specific experience with a complex algorithm, detailing the steps you took to implement or optimize it. Discuss the challenges you encountered, showcasing your problem-solving skills, and any successful outcomes from your work.

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How do you ensure your code is scalable and maintainable?

Explain your coding practices such as modular development, documentation, and version control usage with Git. Mention how you approach code reviews or collaborating with team members to maintain the codebase quality over time.

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What is your experience with numerical optimization in machine learning?

This is your chance to share specific examples of your work with numerical algorithms or optimization techniques. Discuss the impact of your efforts in improving model performance and any tools you used for validation.

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How do you stay updated with the latest machine learning advancements?

Talk about your commitment to continual learning. Mention attending conferences, taking online courses, or reading relevant literature to keep up with industry trends, emphasizing your proactive approach to personal development.

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Describe a time you worked closely with hardware architects. What was the project about?

Share a specific instance where you collaborated with hardware architects, including details about the project scope, your contributions, and the benefits derived from the partnership in optimizing performance or addressing technical challenges.

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Have you ever had to optimize a model for latency? What strategies did you use?

Here, discuss an example involving latency reduction, detailing specific strategies like model pruning, quantization, or selecting efficient algorithms. Share the results of your optimizations and how they improved the user experience or system responsiveness.

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What practices do you adhere to while debugging low-level code?

Explain your debugging methodology, including the tools you use such as Valgrind or gdb. Discuss your approach to systematically isolating issues and ensuring efficient debugging to maintain code performance and reliability.

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Founded in 2017 and headquartered in Irvine, Calif., Syntiant Corp. is a leader in moving artificial intelligence and machine learning from the cloud to edge devices.

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DATE POSTED
March 20, 2025

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