Machine Learning Engineer

Sign-Speak Inc is building AI systems for sign language recognition and communication. The Machine Learning Engineer role supports the full model lifecycle, from training and optimization through GPU-accelerated pipelines and production deployment, including execution on local devices.

• Develop, train, evaluate, and continuously improve machine learning models for sign language recognition and communication systems.
• Implement and experiment with machine learning architectures and techniques described in research papers.
• Optimize models for accuracy
  , latency
  , throughput
  , and computational efficiency
  .
• Build and improve GPU-accelerated training and inference pipelines.
• Apply performance optimization techniques such as model quantization
  , pruning
  , and other improvements.
• Deploy and maintain machine learning models in production environments.
• Develop and optimize models for execution on local devices, including smartphones and other edge computing platforms.
• Integrate machine learning models into real-time applications
  .
• Improve machine learning infrastructure, model serving, and deployment workflows.
• Investigate performance bottlenecks across model execution, memory utilization, and hardware acceleration.
• Evaluate tradeoffs between model accuracy, inference speed, and computational resource requirements.
• Write and execute technical design documents.
• Collaborate with research and engineering teams to convert experimental models into production-ready solutions.

• Degree in Computer Science, Machine Learning, Artificial Intelligence, Computer Engineering, or equivalent professional experience.
• Experience developing, training, and evaluating machine learning models.
• Proficiency in Python and familiarity with commonly used machine learning frameworks.
• Experience implementing or adapting machine learning techniques from research papers.
• Understanding of neural networks, deep learning architectures, and model optimization.
• Experience developing or deploying machine learning models in production or other performance-constrained environments.
• Strong understanding of GPUs and hardware-accelerated machine learning.
• Familiarity with model inference optimization and performance benchmarking.
• Understanding of software engineering principles, backend systems, and machine learning pipelines.
• Willingness to learn and work in a fast-paced startup environment.

• PhD
  .
• Knowledge of the Deaf and Hard of Hearing (D/HH) community is always preferred; if unfamiliar, support for learning is available.

• Python
• Py Torch
• Tensor Flow
• CUDA
• ONNX Runtime
• TensorRT
• Core ML
• Tensor Flow Lite
• LiteRT
• MLOps

• PyTorch, Tensor Flow, or similar machine learning frameworks.
• GPU computing and optimization, including CUDA.
• Deep learning model training, fine-tuning, and evaluation.
• Model quantization, pruning, distillation, and compression.
• Model inference optimization using technologies such as ONNX Runtime or TensorRT.
• MLOps, model serving, and deployment pipelines.
• On-device machine learning deployment using Core ML, Tensor Flow Lite/LiteRT, or similar frameworks.
• Optimizing models for mobile GPUs, NPUs, or other edge accelerators.
• Real-time machine learning inference and low-latency AI applications.
• Computer vision, video understanding, human pose estimation, or related applications.
• Sequence modeling, multimodal machine learning, or transformer-based architectures.
• Distributed model training and cloud-based GPU infrastructure.
• Performance profiling, memory optimization, and hardware-aware model development.

Base salary: $120,000–$160,000 annually.

• Health insurance
• Dental insurance
• Vision insurance
• Paid time off
• Professional development support
• stoc…