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Software Engineer III
ICONMA, LLC
- 🇺🇸 United States
- On-site
- 19 hours ago
- $90.21 – $94.14 / hour
- PyTorch
- C++
- Computer Vision
- SoCs
- TensorFlow
- ONNX
19 hours ago
Responsibilities:
- 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 Client and Client 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
Requirements:
- 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
- Top 3 must-have hard skills:
- Optimizing and running ML inference on embedded / resource-constrained devices
- Training ML models (PyTorch or similar)
- C/C++ (2+ years industry experience)
- Good to have skills:
- Audio ML
- Computer vision ML
- Audio DSP
Why Should You Apply?
- Health Benefits
- Referral Program
- Excellent growth and advancement opportunities
Software Engineer III · ICONMA, LLC