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Entry Level Audio Signal Processing Machine Learning Jobs Near Me

This means designing observation spaces, action spaces, reward signals, and success criteria for ... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ...

... to accelerate the processes that our partners touch day to day and advance the speed and ... As a Machine Learning AI Engineer, you will play a key role in contributing to the designing ...

... to accelerate the processes that our partners touch day to day and advance the speed and ... As a Senior Machine Learning AI Engineer, you will play a critical role in designing, building, and ...

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Entry Level Audio Signal Processing Machine Learning information

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How much do entry level audio signal processing machine learning jobs pay per hour?

As of Jun 25, 2026, the average hourly pay for entry level audio signal processing machine learning in the United States is $17.46, according to ZipRecruiter salary data. Most workers in this role earn between $15.62 and $18.99 per hour, depending on experience, location, and employer.
What are the most commonly searched types of Audio Signal Processing Machine Learning jobs? The most popular types of Audio Signal Processing Machine Learning jobs are:
A map of the United States highlighting the number of Entry Level Audio Signal Processing Machine Learning job openings by state according to ZipRecruiter. The image is accompanied by a detailed chart listing the number of Entry Level Audio Signal Processing Machine Learning job openings in each state, with California having the most at 2 and Hawaii the least at 0.

Machine Learning Engineer

Bespoke Labs

Columbus, OH • On-site

Full-time

Posted 8 days ago


Job description

About Us

We are AI researchers and builders who understand how to curate data and RL environments that truly improve models. We curated OpenThoughts, one of the best open reasoning datasets, and have trained SOTA models such as Bespoke-MiniCheck and Bespoke-MiniChart.

We are embarked on a journey to build Environments that are entire digital worlds that can be used to push the frontier of agents.

What You'll Be Working On

You will work directly with our research team on RL environment and task creation for agent training. This means designing observation spaces, action spaces, reward signals, and success criteria for new environments — and building the infrastructure that makes world-scale RL training possible. This is a high-ownership role; you will be building novel systems, not maintaining legacy ones.

Must-Have Skills

3+ years of ML engineering experience — model training, fine-tuning, or post-training pipelines in research or production

Strong Python and deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed precision)

Hands-on experience with LLM post-training — SFT, RLHF, PPO, DPO, or reward model training — and understanding of how training data quality affects model behavior

Familiarity with RL frameworks (Gymnasium, dm_env) and the ability to design or modify reward functions for agent training objectives

Experience running experiments at scale on cloud or HPC (AWS, GCP, SLURM, or Ray)

Solid understanding of evaluation methodology — held-out sets, benchmark design, avoiding train/eval contamination