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Audio Signal Processing Machine Learning Jobs in Florida

Bespoke Labs

Machine Learning Engineer

Boca Raton, FL · On-site

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

Bespoke Labs

Machine Learning Engineer

Miami, FL · On-site

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

Bespoke Labs

Machine Learning Engineer

Hialeah, FL · On-site

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

Bespoke Labs

Machine Learning Engineer

Orlando, FL · On-site

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

Elevance Health

AI Machine Learning Scientist

Tampa, FL · On-site +1

AI Machine Learning Scientist AI Machine Learning Scientist Location: This role requires associates ... Help build reusable AI capabilities, evaluation frameworks, and governance processes that ensure AI ...

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All JobsAudio Signal Processing Machine Learning JobsAudio Signal Processing Machine Learning Jobs in Florida

Audio Signal Processing Machine Learning information

See Florida salary details

$22K

$63.1K

$128.2K

How much do audio signal processing machine learning jobs pay per year?

As of Jun 25, 2026, the average yearly pay for audio signal processing machine learning in Florida is $63,113.00, according to ZipRecruiter salary data. Most workers in this role earn between $37,400.00 and $84,400.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Audio Signal Processing Machine Learning position, and why are they important?

To thrive in Audio Signal Processing Machine Learning, you need a strong background in digital signal processing, machine learning, mathematics, and programming (typically Python, MATLAB, or C++), often supported by a relevant degree in electrical engineering, computer science, or a related field. Familiarity with machine learning frameworks (such as TensorFlow or PyTorch), audio processing libraries (like Librosa), and experience using version control systems are highly valuable. Creative problem-solving, strong analytical thinking, and effective collaboration skills will set you apart in this technical and interdisciplinary field. These skills are essential for developing innovative audio processing solutions that meet practical needs in industries like telecommunications, music, and voice recognition.

What is an Audio Signal Processing Machine Learning job?

An Audio Signal Processing Machine Learning job involves applying machine learning techniques to analyze, process, and enhance audio signals. This includes tasks like speech recognition, music classification, noise reduction, and sound synthesis. Professionals in this role work with digital signal processing (DSP), deep learning models, and frameworks like TensorFlow or PyTorch to develop audio-based AI applications. They often collaborate with researchers, engineers, and data scientists to improve audio-related technologies in industries such as telecommunications, media, and healthcare.

What are some typical projects or responsibilities for professionals in Audio Signal Processing Machine Learning roles?

Professionals in Audio Signal Processing Machine Learning roles often work on projects such as developing speech recognition systems, designing audio enhancement algorithms, or building music information retrieval solutions. Daily responsibilities may include data preprocessing, feature extraction, model design and training, and evaluating algorithm performance using large audio datasets. Collaboration with software engineers, product managers, or hardware teams is common, as solutions typically need to be integrated into larger products or platforms. These roles also require keeping up with the latest research and continuously tuning models for improved accuracy and efficiency. This variety ensures a dynamic work environment where innovation and technical growth are encouraged.

What are the most commonly searched types of Audio Signal Processing Machine Learning jobs in Florida? The most popular types of Audio Signal Processing Machine Learning jobs in Florida are:
What are popular job titles related to Audio Signal Processing Machine Learning jobs in Florida? For Audio Signal Processing Machine Learning jobs in Florida, the most frequently searched job titles are:
What job categories do people searching Audio Signal Processing Machine Learning jobs in Florida look for? The top searched job categories for Audio Signal Processing Machine Learning jobs in Florida are:
Audio Signal Processing Machine Learning jobs near you

Machine Learning Engineer

Bespoke Labs

Jacksonville, FL • 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

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