Research Engineer – Agentic Audio ML Workflows

This is a fully remote position, open to applicants in United States, +13 more locations.

📋 Description

• Create and execute agentic workflows that plan, launch, monitor, diagnose, and summarize audio ML experiments with suitable human oversight.

• Streamline data preparation, model training, hyperparameter optimization, benchmarking, regression testing, and result comparisons across various research projects.

• Develop tools for on-premises GPU scheduling and utilization, experiment monitoring, artifact management, and ensuring reproducible execution.

• Integrate LLM-powered agents with scripts, repositories, datasets, evaluation suites, and computing resources using clearly defined tools and safety measures.

• Collaborate with audio researchers to convert repetitive experiments into reusable workflows along with clear dashboards or reports.

⛳️ Requirements

• A degree in computer science, machine learning, electrical engineering, or a related discipline, or equivalent hands-on experience.

• Proficient software engineering skills with a strong command of Python.

• Practical experience in developing LLM agents or agentic workflows that invoke tools and execute multi-step tasks.

• Familiarity with ML training and evaluation pipelines, PyTorch, experiment tracking, and automated testing.

• Hands-on experience working with on-premises Linux GPU machines, including CUDA environments, containers, job scheduling, and resource monitoring.

• A working understanding of audio ML and DSP, including common datasets, audio metrics, and model development workflows.

• Experience with orchestration frameworks, distributed training, CI/CD, or MLOps is a plus.

• Capacity to build reliable, observable systems and communicate effectively with research scientists.

• Ability to address complex, open-ended challenges through experimentation.

🏝️ Benefits

• Bonus programs.

• Comprehensive health and welfare benefits.

• 401(k) plan.

• Exclusive wellbeing perks.

• Generous employee discount.

• Compensation tailored to skills, experience, education, and location.