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Full Time Machine Learning Ops Engineer Jobs in Florida

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 ...

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 ...

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 ...

Sr Machine Learning Engineer

Jacksonville, FL ยท On-site +1

$113K - $149K/yr

Senior Machine Learning Engineer What you will do Let's do this. Let's change the world. In this vital role you will play a pivotal role in building and scaling our machine learning models from ...

They are seeking a Senior Machine Learning Engineer to design and implement core pricing services, collaborate with various teams, and enhance the pricing platform's performance and reliability.

They are seeking a Machine Learning Engineer to own the design and implementation of core pricing services, collaborating with cross-functional teams to enhance their pricing platform and drive ...

As a software engineer on the team, you'll collaborate with data scientists, machine learning engineers, product managers, and partner engineering and operations teams to turn ideas into resilient ...

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Full Time Machine Learning Ops Engineer information

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and maintain AI systems, and while AI automation can handle certain tasks, MLEs are essential for creating, optimizing, and troubleshooting complex models. AI tools may augment their work, but the role requires expertise in data science, programming, and system integration that cannot be fully replaced by AI itself.

How much do MLOps engineers make?

MLOps engineers typically earn between $100,000 and $150,000 annually, with salaries increasing based on experience, location, and expertise in tools like Kubernetes, Docker, and cloud platforms. Senior roles or those with specialized skills can exceed $180,000 per year.

What is the difference between Full Time Machine Learning Ops Engineer vs Data Scientist?

AspectFull Time Machine Learning Ops EngineerData Scientist
Primary focusDeploying, maintaining, and optimizing ML models in production environmentsAnalyzing data, building models, and deriving insights
Required skillsMachine learning deployment, cloud platforms, scripting, DevOps practicesStatistical analysis, data visualization, programming (Python/R)
Work environmentProduction systems, cloud infrastructure, cross-functional teamsResearch, data analysis, model development in labs or offices
Common certificationsCloud certifications (AWS, GCP), ML Ops certificationsData science certifications, statistical courses

While both roles involve machine learning, the Full Time Machine Learning Ops Engineer focuses on deploying and maintaining models in production, requiring DevOps and cloud skills. Data Scientists primarily analyze data and develop models, often working in research settings. Understanding these differences helps in choosing the right career path or job focus.

What engineer makes $500,000 a year?

A senior or lead machine learning operations (MLOps) engineer with extensive experience, specialized skills in deploying and maintaining machine learning systems, and working at large tech companies or in high-demand industries can earn $500,000 or more annually. Compensation often includes base salary, bonuses, and stock options, especially in competitive markets or executive-level roles.

Are MLOps engineers in demand?

MLOps engineers are in high demand due to the increasing adoption of machine learning models in various industries. Companies seek professionals skilled in deploying, monitoring, and maintaining ML systems using tools like Docker, Kubernetes, and cloud platforms, making this a growing and competitive field for job seekers.
What are the most commonly searched types of Machine Learning Ops Engineer jobs in Florida? The most popular types of Machine Learning Ops Engineer jobs in Florida are:
What cities in Florida are hiring for Full Time Machine Learning Ops Engineer jobs? Cities in Florida with the most Full Time Machine Learning Ops Engineer job openings:

Machine Learning Engineer

Bespoke Labs

Lake Worth, FL โ€ข On-site

Full-time

Posted 23 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 โ€” 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 โ€” 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 โ€” SFT, RLHF, PPO, DPO, or reward model training โ€” 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 โ€” held-out sets, benchmark design, avoiding train/eval contamination