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Work Based Learning Program Aws Jobs in Nebraska

Bespoke Labs

Machine Learning Engineer

Omaha, NE · On-site

What You'll Be Working On You will work directly with our research team on RL environment and task ... HPC (AWS, GCP, SLURM, or Ray) Solid understanding of evaluation methodology -- held-out sets ...

EPITEC

Business Analyst III

Omaha, NE · Hybrid

Drives value-based prioritization , partnering with stakeholders to sequence work based on business ... Able to ramp quickly in complex environments , building relationships, learning systems and ...

New

Epitec

Business Analyst III-

Omaha, NE · On-site

Drives value-based prioritization , partnering with stakeholders to sequence work based on business ... Able to ramp quickly in complex environments , building relationships, learning systems and ...

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Work Based Learning Program Aws information

See Nebraska salary details

$44.8K

$77.6K

$175K

How much do work based learning program aws jobs pay per year?

As of Jun 23, 2026, the average yearly pay for work based learning program aws in Nebraska is $77,580.00, according to ZipRecruiter salary data. Most workers in this role earn between $54,300.00 and $84,900.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in a Work-Based Learning Program focused on AWS, and why are they important?

To thrive in a Work-Based Learning Program focused on AWS, you need foundational knowledge of cloud computing concepts, basic programming skills, and familiarity with networking, supported by relevant coursework or entry-level certifications like AWS Cloud Practitioner. Hands-on experience with AWS tools such as EC2, S3, Lambda, and the AWS Management Console is typically required, along with understanding of version control systems like Git. Strong problem-solving abilities, willingness to learn, and effective communication are important soft skills for adapting to real-world technical environments. These skills and qualities are crucial for successfully applying cloud concepts in practical settings and collaborating with teams to solve business challenges.

What is the difference between Work Based Learning Program Aws vs Cloud Support Associate?

AspectWork Based Learning Program AwsCloud Support Associate
CredentialsTypically no formal certifications required; focus on trainingOften requires AWS certifications or related cloud credentials
Work EnvironmentEducational or training setting, often part-time or internshipProfessional cloud support environment, full-time role
Employer & Industry UsageEducational institutions, training providers, AWS programsCloud service providers, IT companies, AWS partners

The Work Based Learning Program Aws is primarily a training or internship opportunity designed to develop skills in AWS cloud services, often without requiring prior certifications. In contrast, a Cloud Support Associate is a full-time professional role that typically requires AWS certifications and involves supporting cloud customers in a real-world environment. While the learning program focuses on education and skill development, the associate role emphasizes practical support and troubleshooting in the industry.

What is a Work Based Learning Program with AWS?

A Work Based Learning Program with AWS is an educational initiative that combines classroom instruction with real-world work experience using Amazon Web Services (AWS) technologies. These programs are designed to help students and professionals gain hands-on cloud computing skills by working on projects, internships, or apprenticeships in partnership with employers. Participants learn about cloud infrastructure, deployment, and AWS services, making them more competitive in the job market. Such programs often include mentorship, industry certifications, and exposure to real business challenges.

How does participating in an AWS Work-Based Learning Program help prepare candidates for a cloud-focused career?

Participating in an AWS Work-Based Learning Program offers hands-on experience with key AWS cloud services, allowing candidates to apply classroom concepts to real-world projects. You'll typically collaborate with mentors and teammates in a structured environment, working on tasks such as cloud migration, automation, and security configuration. This immersion helps build both technical and professional skills, making you more competitive for roles such as cloud support associate or solutions architect. Additionally, exposure to industry best practices and networking opportunities within the program can significantly accelerate your career growth in cloud computing.
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Work Based Learning Program Aws jobs near you

Machine Learning Engineer

Bespoke Labs

Omaha, NE • On-site

Full-time

Posted 5 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

Apply