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Principal Machine Learning 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 ...

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 ...

Junior Machine Learning Engineer

Melbourne, FL ยท On-site

$65K - $106K/yr

Position Description ENSCO, Inc. is seeking a Junior Machine Learning Engineer with direct experience and applications with using Machine Learning (ML) and Deep Learning (DL) models, frameworks ...

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 ...

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 ...

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Principal Machine Learning Engineer information

See Florida salary details

$55.3K

$110K

$158.8K

How much do principal machine learning engineer jobs pay per year?

As of Jul 11, 2026, the average yearly pay for principal machine learning engineer in Florida is $110,016.00, according to ZipRecruiter salary data. Most workers in this role earn between $88,600.00 and $129,300.00 per year, depending on experience, location, and employer.

What types of projects and responsibilities can a Principal Machine Learning Engineer typically expect in this role?

Principal Machine Learning Engineers are often tasked with leading the design, development, and deployment of large-scale machine learning models and systems that address key business challenges. In this role, you will collaborate closely with data scientists, engineers, and product managers to define project requirements, architect solutions, and ensure high-quality delivery. You may also guide research initiatives, oversee code and model reviews, and mentor junior engineers, helping to shape the technical direction of the team. Typical responsibilities can range from prototyping and optimizing algorithms to ensuring models are scalable, reliable, and aligned with organizational goals.

What are the key skills and qualifications needed to thrive in the Principal Machine Learning Engineer position, and why are they important?

To thrive as a Principal Machine Learning Engineer, you need advanced expertise in machine learning algorithms, statistical analysis, software engineering, and a strong background in computer science or related fields, often supported by a master's or PhD degree. Familiarity with tools such as Python, TensorFlow, PyTorch, cloud platforms (AWS, GCP, Azure), and relevant certifications strengthens technical capability. Leadership, strategic thinking, effective communication, and mentorship are vital soft skills for guiding teams and collaborating across departments. These competencies are essential for driving innovation, ensuring technical excellence, and influencing organizational AI initiatives.

Will MLE be replaced by AI?

Principal Machine Learning Engineers design, develop, and oversee AI and machine learning systems, and their roles involve understanding complex algorithms, data management, and model deployment. While AI automates certain tasks, MLE roles focus on building and maintaining AI infrastructure, which requires human expertise, critical thinking, and ongoing innovation that AI cannot fully replace. The role is expected to evolve alongside advancements in AI technology but remains essential for guiding AI development and ensuring ethical, effective implementation.

What does a Principal Machine Learning Engineer do?

A Principal Machine Learning Engineer leads the design, development, and deployment of machine learning models and systems. They set technical strategy, mentor engineers, and collaborate with cross-functional teams to solve complex AI challenges. Their role often includes researching new algorithms, optimizing model performance, and ensuring scalability in production environments. Additionally, they work closely with data scientists, software engineers, and product managers to align ML initiatives with business objectives.

How much do principal AI engineers make?

Principal AI engineers typically earn between $130,000 and $200,000 annually, with salaries varying based on experience, location, and industry. They often have advanced skills in machine learning, deep learning, and data science, and may receive bonuses or stock options as part of compensation packages.

What engineers make $300,000 a year?

Principal Machine Learning Engineers and senior data scientists in the tech industry often earn $300,000 or more annually, especially with extensive experience, advanced skills in deep learning and AI, and working at large technology companies or startups with competitive compensation packages. High salaries may also include bonuses, stock options, and other benefits.

What engineer makes $500,000 a year?

A Principal Machine Learning Engineer can earn $500,000 or more annually, especially with extensive experience, advanced skills in deep learning and data science, and working at large tech companies or in high-demand industries. Compensation often includes base salary, bonuses, and stock options, reflecting their seniority and expertise.
Infographic showing various Principal Machine Learning Engineer job openings in Florida as of July 2026, with employment types broken down into 90% Full Time, 6% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $110,016 per year, or $52.9 per hour.

Machine Learning Engineer

Bespoke Labs

Boca Raton, 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