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Junior Aws Machine Learning Jobs in Florida (NOW HIRING)

Bespoke Labs

Machine Learning Engineer

Orlando, FL · On-site

... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ... HPC (AWS, GCP, SLURM, or Ray) Solid understanding of evaluation methodology -- held-out sets ...

Bespoke Labs

Machine Learning Engineer

Miami, FL · On-site

... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ... HPC (AWS, GCP, SLURM, or Ray) Solid understanding of evaluation methodology -- held-out sets ...

Bespoke Labs

Machine Learning Engineer

Hialeah, FL · On-site

... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ... HPC (AWS, GCP, SLURM, or Ray) Solid understanding of evaluation methodology -- held-out sets ...

Bespoke Labs

Machine Learning Engineer

Pensacola, FL · On-site

... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ... HPC (AWS, GCP, SLURM, or Ray) Solid understanding of evaluation methodology -- held-out sets ...

Bespoke Labs

Machine Learning Engineer

Lake Worth, FL · On-site

... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ... HPC (AWS, GCP, SLURM, or Ray) Solid understanding of evaluation methodology -- held-out sets ...

Bespoke Labs

Machine Learning Engineer

Melbourne, FL · On-site

... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ... HPC (AWS, GCP, SLURM, or Ray) Solid understanding of evaluation methodology -- held-out sets ...

OneBlood

AI & Machine Learning Engineer

Saint Petersburg, FL · On-site

$105K - $127K/yr

Strong working knowledge of machine learning methodologies, including supervised learning (e.g ... Skilled in cloud-based ML development and deployment on platforms such as AWS, Azure, or Google ...

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Junior Aws Machine Learning information

What is a junior AWS machine learning engineer?

Junior AWS Machine Learning engineers are entry-level professionals who work with Amazon Web Services (AWS) to develop, deploy, and maintain machine learning models. They assist in data preparation, model training, and integration of AI solutions using AWS tools such as SageMaker, Lambda, and S3. These engineers often collaborate with data scientists and software teams to implement predictive analytics and automation solutions on the AWS cloud platform. Their role typically involves learning best practices for cloud security, data handling, and scalable machine learning deployment.

What are the key skills and qualifications needed to thrive as a junior AWS machine learning engineer?

To thrive as a Junior AWS Machine Learning Engineer, you need a solid grasp of programming (especially Python), foundational knowledge of machine learning concepts, and a relevant degree in computer science or a related field. Familiarity with AWS services like SageMaker, Lambda, and S3, as well as certifications such as AWS Certified Machine Learning – Specialty, are highly valuable. Strong problem-solving skills, attention to detail, and the ability to communicate technical ideas clearly help you stand out in this role. These skills and qualities are crucial for efficiently developing, deploying, and maintaining machine learning solutions on AWS in collaborative, fast-paced environments.

What are some common challenges faced by junior AWS machine learning engineers when deploying models to production environments?

Junior AWS Machine Learning Engineers often encounter challenges such as managing the scalability of their models, ensuring data security and compliance in the cloud, and integrating machine learning pipelines with existing AWS services. Since production environments require high reliability, newcomers may also need to learn how to monitor model performance and troubleshoot issues using AWS tools like SageMaker and CloudWatch. Collaborating closely with data engineers and DevOps teams is essential to streamline deployment and maintain model accuracy over time.

What is the difference between Junior Aws Machine Learning vs Data Scientist?

AspectJunior Aws Machine LearningData Scientist
Required CredentialsBasic AWS certifications, entry-level ML knowledgeAdvanced degrees, certifications like AWS, data analysis skills
Work EnvironmentCloud platforms, machine learning projects, collaborative teamsData analysis, modeling, research, cross-functional teams
Employer & Industry UsageTech companies, startups, cloud service providersFinance, healthcare, tech, research institutions

Junior AWS Machine Learning roles focus on implementing ML models using AWS tools with foundational knowledge, while Data Scientists typically handle broader data analysis, modeling, and research tasks. The roles overlap in cloud-based ML work but differ in scope and experience level.

What are the most commonly searched types of Aws Machine Learning jobs in Florida?

The most popular types of Aws Machine Learning jobs in Florida are:

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Junior Aws Machine Learning jobs near you

Machine Learning Engineer

Bespoke Labs

Orlando, FL • On-site

Full-time

This job post has expired today. Applications are no longer accepted.

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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