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Artificial Intelligence Machine Learning Engineer Jobs in Boca Raton, FL

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

As an AI Engineering team member, you will be instrumental in advancing new features and/or solutions from the Proof of Concept stage to full production readiness. Your role involves refining and ...

AI Solutions Engineer

Fort Lauderdale, FL · On-site

$125 - $150/hr

Position Summary The AI Solutions Engineer will lead the development and implementation of artificial intelligence, machine learning, and automation solutions across Sound Income Group. As a new add ...

Senior ML Engineer

Hollywood, FL · On-site

$95K - $131K/yr

Client seeking a Senior Machine Learning Engineer in Fort Lauderdale, FL to design, develop, and deploy scalable machine learning solutions that power customer intelligence, personalization ...

Showing results 21-40

Artificial Intelligence Machine Learning Engineer information

See Boca Raton, FL salary details

$29.8K

$121.8K

$183.1K

How much do artificial intelligence machine learning engineer jobs pay per year?

As of Sep 9, 2026, the average yearly pay for artificial intelligence machine learning engineer in Boca Raton, FL is $121,830.00, according to ZipRecruiter salary data. Most workers in this role earn between $96,000.00 and $146,600.00 per year, depending on experience, location, and employer.

What is an artificial intelligence machine learning engineer?

An Artificial Intelligence (AI) Machine Learning Engineer is a professional who designs, builds, and implements machine learning models and AI systems. They work with large datasets, develop algorithms, and use programming languages like Python or R to enable computers to learn from data and make predictions or decisions. Their work is essential in fields such as natural language processing, computer vision, and robotics. These engineers collaborate with data scientists, software developers, and business stakeholders to deploy AI solutions in real-world applications.

What are some common challenges faced by artificial intelligence machine learning engineers when deploying models to production?

One of the main challenges AI/ML engineers encounter is ensuring that models trained in a controlled environment perform reliably in real-world production settings. This often involves handling issues like data drift, scaling models to handle large volumes of requests, and integrating with existing infrastructure. Collaboration with data engineers and software developers is crucial to streamline deployment, monitor model performance, and address any unexpected behavior quickly. Keeping up with evolving tools and best practices is also important for long-term model maintenance and success.

What are the key skills and qualifications needed to thrive as an artificial intelligence machine learning engineer, and why are they important?

To thrive as an Artificial Intelligence Machine Learning Engineer, you need strong programming skills (typically in Python or R), a background in mathematics or statistics, and a degree in computer science or a related field. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch, or scikit-learn), cloud platforms, and relevant certifications are highly valuable. Problem-solving ability, creativity, and effective communication are important soft skills that distinguish top performers in this role. These competencies are crucial for designing robust AI solutions, collaborating with cross-functional teams, and driving innovation in rapidly evolving technological environments.

What is the difference between Artificial Intelligence Machine Learning Engineer vs Data Scientist?

AspectArtificial Intelligence Machine Learning EngineerData Scientist
Required CredentialsBachelor's or higher in CS, AI, ML, or related; certifications like TensorFlow, AWSBachelor's or higher in CS, Statistics, or related; certifications in data analysis or visualization
Work EnvironmentDevelops AI/ML models, coding, deploying algorithms in software environmentsAnalyzes data, builds models, interprets data insights for business decisions
Employer & Industry UsageTech companies, AI startups, R&D departmentsFinance, healthcare, marketing, consulting firms

While both roles involve working with data and algorithms, Artificial Intelligence Machine Learning Engineers focus on designing, building, and deploying AI/ML models in software systems. Data Scientists primarily analyze data to extract insights and support decision-making. The roles often overlap but differ in their core focus and daily tasks.

What are popular job titles related to Artificial Intelligence Machine Learning Engineer jobs in Boca Raton, FL?

For Artificial Intelligence Machine Learning Engineer jobs in Boca Raton, FL, the most frequently searched job titles are:

What job categories do people searching Artificial Intelligence Machine Learning Engineer jobs in Boca Raton, FL look for?

The top searched job categories for Artificial Intelligence Machine Learning Engineer jobs in Boca Raton, FL are:

What cities near Boca Raton, FL are hiring for Artificial Intelligence Machine Learning Engineer jobs?

Cities near Boca Raton, FL with the most Artificial Intelligence Machine Learning Engineer job openings:

Infographic showing various Artificial Intelligence Machine Learning Engineer job openings in Boca Raton, FL as of August 2026, with employment types broken down into 100% Full Time. Highlights an 86% In-person, and 14% Remote job distribution, with an average salary of $121,830 per year, or $58.6 per hour.

Machine Learning Engineer

Lake Worth Beach, FL

Full-time

Re-posted 10 days ago


Job description

  • Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch

  • Build and maintain the infrastructure around RL training: rollout collection, data curation, reward model serving, and experiment orchestration

  • Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues

  • Build evaluation harnesses and benchmark infrastructure, with held-out sets and contamination controls, so results are trustworthy

  • Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams

  • Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics

  • Implement methods from recent ML papers quickly and turn them into production-grade systems