Senior Associate, AI Engineer
PwC
CloudDockerKubernetesMicroservices
Machine Learning Engineer/Scientist – BCI
Precision Neuroscience
full-time
Posted on:
Location: California, New York • 🇺🇸 United States
Visit company websiteSalary
💰 $150,000 - $300,000 per year
Job Level
Mid-LevelSenior
Tech Stack
AirflowAWSEC2KubernetesNumpyPythonPyTorchScikit-LearnTensorflow
About the role
- Build real-time, robust ML systems to interpret high-dimensional neural data and enable BCI control of digital devices.
- Develop neural signal processing and decoding algorithms (spike sorting, LFP, EcoG features, denoising, filtering, spectral analysis).
- Implement low-latency inference pipelines and edge deployment/streaming architectures for neural decoding.
- Translate research into production systems that integrate with hardware, firmware, and front-end user interfaces.
- Design experimental protocols to gather training data and collaborate with software and clinical teams to execute them.
- Collaborate across disciplines (neurosurgery, AI, microfabrication, electrical engineering) and contribute to product development.
- Ensure systems meet regulatory and performance constraints for clinical deployable neurotechnology.
Requirements
- Track record developing novel algorithms with strong publication record (NeurIPS, ICML, CVPR) and ideally patents.
- Hands-on experience developing realtime machine learning algorithms leveraging high volume data (image, video, audio) in embedded real time systems.
- Experience building real-time, robust ML systems that interpret high-dimensional neural data.
- RNNs, LSTMs, CNNs, compression & optimization for real-time inference.
- Spike sorting, LFP, EcoG feature extraction; denoising, filtering, spectral analysis, ICA/PCA.
- Low-latency inference pipelines, edge deployment and streaming architecture experience.
- Proficiency in Python (PyTorch, TensorFlow, NumPy, scikit-learn).
- Experience with Kubeflow, MLFlow, Airflow.
- Containerization experience (Docker, Kubernetes).
- Experience with AI/Robotics tools (OpenCV, ROS2, Kaldi) and AWS (Sagemaker Studio, Kinesis, S3, EC2, Lambda, Cloudwatch, EMR, Elastic).
- Proven experience shipping ML-powered products in production, especially in high-stakes or real-time environments.
- Delivered end-to-end systems from signal acquisition to live neural decoding in clinical or research settings.
- Built robust, testable, and maintainable ML pipelines that integrate with hardware, firmware, and front-end interfaces.
- Contributed to cross-functional product development with software, hardware, clinical, and UX teams.
- Familiarity with FDA and HIPAA regulatory and performance constraints for deployable neurotech or medical devices.
- Willingness/ability to work onsite in Manhattan (NYC) or Santa Clara (California); company supports E3 visa sponsorship for Australian citizens.
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