Machine Learning Engineer

BCforward is currently seeking a highly motivated Machine Learning Engineer for an Onsite Opportunity at Redmond, WA

Position Title: Machine Learning Engineer

Location: Redmond, WA

Anticipated Start Date: ASAP

Please note this is the target date, and it is subject to change. BCforward will send official notice ahead of a confirmed start date.

Expected Duration: 12 Months

Job Type: Onsite

Salary Range: $75/hr.- $80/hr.

Please note that actual compensation may vary within this range due to factors such as location, experience, and job responsibilities, and does not encompass additional non-standard compensation (e.g., benefits, paid time off, per diem, etc.).

Job Summary:

The team brings together researchers, developers, and engineers to create the future of virtual and augmented reality. The team develops technologies for AR glasses and wearables, including optics, displays, computer vision, audio, graphics, eye/hand/face/body tracking, and perception.

The Audio team focuses on advancing audio technologies to improve human connection and communication. The team develops and matures audio solutions for AR glasses and wearables, taking them from early research through technology transfer to product teams.

We are seeking a Machine Learning Software Engineer to bring state-of-the-art machine learning models from research prototypes to real-time execution on embedded devices. The role involves training and adapting ML models alongside research scientists, then optimizing, porting, and deploying them efficiently on resource-constrained hardware with strict compute, memory, latency, and power requirements.

The ideal candidate is experienced in both PyTorch-based model training and C/C++ inference development and understands how model architecture, quantization, and target hardware influence on-device ML performance. Experience in audio ML, computer vision, or audio digital signal processing (DSP) is a plus.

Responsibilities

• Work closely with research scientists to move ML models from research prototypes to efficient, real-time inference on embedded devices.
• Train, fine-tune, and adapt ML models while accounting for latency, memory, power, and compute constraints.
• Optimize models for on-device inference using quantization, pruning, distillation, operator fusion, and architecture adjustments.
• Port, integrate, and profile ML models on embedded targets such as DSPs, NPUs, microcontrollers, and mobile SoCs.
• Identify and resolve performance bottlenecks across ML models and target hardware.
• Develop clean, maintainable, high-performance C/C++ code for on-device inference pipelines and real-time prototype systems.
• Build tools and infrastructure that help researchers evaluate model performance and accuracy on target hardware.
• Collaborate with hardware, runtime, and toolchain teams across clients.
• Contribute to technical discussions, documentation, and cross-functional collaboration while working through evolving research requirements.

Minimum Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent work experience.
• Industry experience optimizing and running ML inference on embedded or resource-constrained devices.
• Experience training machine learning 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 ML models and target hardware.
• Experience working in cross-functional teams.

Preferred Qualifications

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

Project Overview

The immediate focus is optimizing speech extraction models for on-device inference. The team has strong research expertise in model training and is looking for an engineer who can bridge the gap between model development and efficient embedded deployment. This expertise will support both the initial project and ongoing ML deployment needs across RLR Audio.

Why This Opportunity?

• Work directly with world-class audio research scientists.
• Contribute to the next generation of AR/VR and wearable devices.
• Bring advanced research models to life on real prototype hardware.
• Work at the intersection of machine learning, embedded systems, and real-time performance optimization.

Work Arrangement: Onsite in Redmond, WA.

Benefits:

BC forward offers all eligible employees a comprehensive benefits package including, but not limited to major medical, HSA, dental, vision, employer-provided group life, voluntary life insurance, short-term disability, long-term disability, and 401k.

About BC forward :

Founded in 1998 on the idea that industry leaders needed a professional service, and workforce management expert, to fuel the development and execution of core business and technology strategies, BC forward is a Black-owned firm providing unique solutions supporting value capture and digital product delivery needs for organizations around the world. Headquartered in Indianapolis, IN with an Offshore Development Center in Hyderabad, India, BC forward’s 6,000 consultants support more than 225 clients globally.