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Weekday Machine Learning Research Scientist Jobs in Nebraska

About Us We are AI researchers and builders who understand how to curate data and RL environments ... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ...

About Us We are AI researchers and builders who understand how to curate data and RL environments ... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ...

About Us We are AI researchers and builders who understand how to curate data and RL environments ... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ...

About Us We are AI researchers and builders who understand how to curate data and RL environments ... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ...

About Us We are AI researchers and builders who understand how to curate data and RL environments ... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ...

About Us We are AI researchers and builders who understand how to curate data and RL environments ... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ...

About Us We are AI researchers and builders who understand how to curate data and RL environments ... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ...

About Us We are AI researchers and builders who understand how to curate data and RL environments ... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ...

A Journeyman Data Scientist is typically a mid-level professional who works independently on data ... Develop, test, and deploy machine learning and statistical models. * Create predictive analytics ...

Showing results 41-60

Weekday Machine Learning Research Scientist information

What are the key skills and qualifications needed to thrive as a Weekday Machine Learning Research Scientist, and why are they important?

To thrive as a Weekday Machine Learning Research Scientist, you need a solid background in mathematics, statistics, programming (Python, R), and a relevant advanced degree such as a Master's or Ph.D. in computer science or a related field. Expertise with machine learning frameworks (like TensorFlow, PyTorch), data processing tools, and familiarity with cloud computing platforms are typically required. Strong analytical thinking, problem-solving abilities, and clear communication skills help you collaborate with teams and present complex findings effectively. These skills are crucial for developing innovative models, delivering impactful research, and ensuring successful implementation in real-world applications.

What does a Weekday Machine Learning Research Scientist do?

A Weekday Machine Learning Research Scientist conducts research and develops new algorithms or models in the field of machine learning, typically during standard business days (Monday to Friday). Their work involves designing experiments, analyzing data, publishing findings, and collaborating with other scientists or engineers. They may focus on improving existing machine learning techniques or creating innovative solutions for real-world problems. This role often requires a strong background in mathematics, computer science, and statistics, as well as proficiency in programming languages like Python or R.

What are some common challenges faced by a Weekday Machine Learning Research Scientist, and how are they typically addressed within the team?

Weekday Machine Learning Research Scientists often encounter challenges such as managing large datasets, tuning complex models, and keeping up with rapidly evolving research. Collaboration is key—team members regularly hold meetings to share findings, brainstorm solutions, and review code. Access to robust computational resources and mentorship from senior researchers helps address technical obstacles, while a structured, weekday schedule allows for focused research and effective work-life balance.

What is the difference between Weekday Machine Learning Research Scientist vs Weekend Machine Learning Research Scientist?

AspectWeekday Machine Learning Research ScientistWeekend Machine Learning Research Scientist
CredentialsMaster's or PhD in Computer Science, Data Science, or related fieldsSame as weekday role
Work EnvironmentTypically in office or research labs during standard hoursFlexible hours, often part-time or project-based
Employer & Industry UsageTech companies, research institutions, startupsFreelance projects, consulting firms, academic collaborations

The main difference between a Weekday Machine Learning Research Scientist and a Weekend Machine Learning Research Scientist lies in their work schedule and environment. Weekday roles usually involve full-time employment with structured hours, while weekend roles are often part-time or freelance, offering more flexibility. Both roles require similar credentials and are used across tech and research industries.

What are the most commonly searched types of Machine Learning Research Scientist jobs in Nebraska? The most popular types of Machine Learning Research Scientist jobs in Nebraska are:

Machine Learning Engineer

Bespoke Labs

Grand Island, NE • On-site

Full-time

Re-posted 20 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