ML Software Engineer

Payrate: $80.00 - $85.00/hr.
 
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 Reality Labs and Meta 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.

 
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.

 
Skills

• 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 client solutions.
• History of writing code to support the execution of research initiatives.

 
Pay Transparency: The typical base pay for this role across the U.S. is: $80.00 - $85.00 /hour. Non-exempt positions are eligible for overtime at a rate of 1.5 times the base hourly rate for all hours worked in excess of 40 in a work week, or as required by state or local law. Final offer amounts, within the base pay set forth above, are determined by factors including your relevant skills, education and experience. Full-time employees are eligible to select from different benefits packages. Packages may include medical, dental, and vision benefits, health savings accounts with qualified medical plan enrollment, 10 paid days off, 3 days paid bereavement leave, 401(k) plan participation with employer match, life and disability insurance, commuter benefits, dependent care flexible spending account, accident insurance, critical illness insurance, hospital indemnity insurance, accommodations and reimbursement for work travel, and discretionary performance or recognition bonus. Sick leave and mobile phone reimbursement provided based on state or local law. 
 
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