Real-Time On-Device AI Audio Engineer

Bose Corporation; Framingham (MA); In loco; AUD 238.000 - 327.000; Tempo pieno

Vantaggi offerti da questo lavoro

Bonus programs

Health benefits

401(k) plan

Employee discount

Descrizione del lavoro

Bose Corporation is seeking a talented ML/AI researcher to develop real-time audio processing algorithms and agentic tools. You will orchestrate training, evaluation, and reporting across AI models, leveraging on-prem and cloud GPU resources and collaborating with audio researchers.

Strong emphasis on robust tooling, reproducible experiments, and scalable pipelines using PyTorch, CUDA, and Linux GPU environments.

Competenze

• Experience building LLM agents or agentic workflows that invoke tools and execute multi-step tasks.
• Experience with ML training pipelines and evaluation workflows.
• Experience operating on-prem Linux GPU machines, including CUDA environments and containers.

Mansioni

• Design and implement agentic workflows that plan, launch, monitor, diagnose, and summarize audio ML experiments with human oversight.
• Automate data preparation, model training, hyperparameter searches, benchmarking and result comparison across projects.
• Build reliable tooling for on-prem GPU scheduling, experiment tracking, artifact management, and reproducible execution.
• Connect LLM-powered agents to scripts, datasets, evaluation suites, and compute resources through well-defined tools and safeguards.
• Create reusable workflows and dashboards/reports for researchers.

Conoscenze

Python programming

LLM agents

ML pipelines

PyTorch

Experiment tracking

CI/CD

MLOps

System observability

Formazione

Degree in computer science / ML / electrical engineering

Strumenti

PyTorch

CUDA

Docker

Kubernetes

Descrizione del lavoro

Bose Corporation is seeking a talented ML/AI researcher to develop real-time audio processing algorithms and agentic tools. You will orchestrate training, evaluation, and reporting across AI models, leveraging on-prem and cloud GPU resources and collaborating with audio researchers.

Strong emphasis on robust tooling, reproducible experiments, and scalable pipelines using PyTorch, CUDA, and Linux GPU environments.