ML Software Engineer III

Crystal Equation Corporation; Redmond (WA); Presencial; USD 112.000 - 118.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

On-site role in Redmond

Descripción de la vacante

Crystal Equation Corporation is hiring an ML Software Engineer III to push state-of-the-art models from lab prototypes to real-time embedded inference. You will train, optimize, port, and run models on constrained hardware for wearables and AR/VR devices, balancing quantization and architecture trade-offs.

The role requires deep experience in C/C++, PyTorch or TensorFlow, and achieving efficient performance on DSPs, NPUs, microcontrollers, and mobile SoCs.

Formación

• Optimizing and running ML inference on embedded/resource-constrained devices.
• Experience training ML models using PyTorch or TensorFlow.

Responsabilidades

• Collaborate with research scientists to port models to real-time on embedded devices.
• Train and adapt models with latency/memory/power constraints in mind.
• Optimize models via quantization, pruning, distillation, and operator fusion.
• Port and profile models on embedded targets (DSPs, NPUs, MCUs, mobile SoCs).
• Write clean, high-performance C/C++ for on-device inference pipelines.

Conocimientos

ML inference

C/C++

PyTorch

TensorFlow

Cross-functional teamwork

Educación

BS degree in CS/CE/EE

Herramientas

ExecuTorch

TFLite/LiteRT

ONNX Runtime

Descripción del empleo

ML Software Engineer III Onsite Summary

We are seeking a ML Software Engineer to bring state-of-the-art machine learning out of the lab and onto the device. Research models only matter to our prototypes if they run in real time, within tight compute, memory, and power budgets, on the embedded hardware that powers wearables. This role sits at that boundary: you will train and adapt models alongside research scientists, then own the work of optimizing, porting, and running them efficiently on embedded targets. The ideal candidate is equally comfortable in a PyTorch training loop and in a C/C++ inference runtime on a resource-constrained device. They understand that on-device ML is a co-design problem, where model architecture, quantization, and the target hardware all shape one another, and they can make the trade-offs required to get a research model running on real hardware quickly. Familiarity with audio ML, computer vision ML, or audio DSP is a strong plus, as many of our models operate on multimodal (audio and visual) sensor data. Our team cultivates an honest and considerate environment where self-motivated individuals thrive. We encourage a strong sense of ownership and embrace the ambiguity that comes with working on the frontiers of research.

Responsibilities:

• Work side by side with research scientists to take ML models from research prototypes to efficient, real-time inference on embedded devices
• Train, fine-tune, and adapt ML models with deployment constraints (latency, memory, power, compute) in mind
• Optimize models for on-device inference using techniques such as quantization, pruning, distillation, operator fusion, and architecture adjustments
• Port, integrate, and profile ML models on embedded targets (e.g. DSPs, NPUs, microcontrollers, mobile SoCs), identifying and resolving performance bottlenecks
• Write clean, maintainable, high-performance C/C++ code for on-device inference pipelines and integrate it into real-time prototype systems
• Build tools and infrastructure that make it faster for researchers to evaluate model performance and accuracy on target hardware
• Coordinate with internal hardware, runtime, and toolchain teams across our company to evaluate and integrate with existing systems
• Work collaboratively as part of a multifunctional team where communication, documentation, and teamwork are highly valued
• Learn constantly, dive into new areas with unfamiliar technologies, and embrace the ambiguity of AR/VR problem solving

Minimum Qualifications:

• BS degree in Computer Science, Computer Engineering, Electrical Engineering, or related field, or equivalent work experience
• Industry experience optimizing and running ML inference on embedded or resource-constrained devices
• Experience training ML models using deep learning frameworks such as PyTorch or TensorFlow
• 2+ years of industry experience writing C/C++
• Experience profiling and debugging performance issues across the ML model and the target hardware
• Experience working in cross-functional teams

Preferred Qualifications:

• Experience with ML for audio (e.g. speech enhancement, source separation, keyword spotting, acoustic event detection)
• Experience with ML for computer vision (e.g. detection, tracking, segmentation, pose estimation)
• Experience with audio DSP fundamentals (e.g. filtering, STFT/filterbanks, beamforming, real-time audio processing)
• Experience with on-device inference frameworks and toolchains (e.g. ExecuTorch, TFLite/LiteRT, ONNX Runtime, vendor NPU/DSP SDKs)
• Experience with model compression techniques such as quantization-aware training, post-training quantization, pruning, or distillation
• Experience writing hardware-specific optimizations (e.g. SIMD/NEON, DSP intrinsics, fixed-point arithmetic)
• Experience working in the AR/VR or wearable device space
• Ability to gather requirements and work closely with researchers to develop novel solutions
• History of writing code to support the execution of research initiatives

Pay range and benefits:

Pay range is $81 - $86 per hour with full benefits available, including paid time off, medical/dental/vision/life insurance, 401K, parental leave, and more. Our compensation reflects the cost of labor across several US geographic markets. Pay is based on several factors including market location and may vary depending on job-related knowledge, skills, and experience.

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