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Ml Model Fine Tuning Jobs in Nebraska (NOW HIRING)

penlink

AI Engineer

Lincoln, NE · On-site

Experience fine-tuning and evaluating machine learning or large language models * Knowledge of cloud platforms and deploying AI/ML solutions within scalable environments * Strong analytical and ...

penlink

AI Engineer

Lincoln, NE · On-site +1

Experience fine-tuning and evaluating machine learning or large language models * Knowledge of cloud platforms and deploying AI/ML solutions within scalable environments * Strong analytical and ...

McCain Foods Limited

Field IT Engineer

Grand Island, NE · On-site

$81K - $108K/yr

... of model selection, fine-tuning, prompt engineering, APIs, security, and enterprise-scale ... Proven experience in data and tag analysis within manufacturing environments, leveraging AI/ML ...

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Field IT Engineer

Grand Island, NE · On-site

... of model selection, fine-tuning, prompt engineering, APIs, security, and enterprise-scale ... Proven experience in data and tag analysis within manufacturing environments, leveraging AI/ML ...

GE HealthCare

AI Engineer

Bellevue, NE · On-site

$108K - $130K/yr

Optimize foundation models through prompt engineering, fine-tuning, distillation, quantization, and inference optimization techniques. * Implement responsible AI practices, including model evaluation ...

D.A. Davidson

AI & Automation Engineer

Omaha, NE · On-site

$90K - $120K/yr

Advanced AI & ML Skills: Familiarity with large language models beyond basic API usage - e.g ... Implement prompt engineering techniques and fine-tune AI models or prompts to improve the relevance ...

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Ml Model Fine Tuning information

What is ML model fine-tuning?

ML model fine-tuning is the process of taking a pre-trained machine learning model and making small adjustments to its parameters using new data relevant to your specific task. This approach allows you to leverage the general knowledge the model has already learned, while adapting it to perform better on your particular dataset or problem. Fine-tuning is common in fields like natural language processing and computer vision, as it saves time and resources compared to training a model from scratch. The process typically involves retraining the last few layers of the model or using a lower learning rate for the entire model.

What are some common challenges faced when fine-tuning machine learning models in a production environment?

One common challenge when fine-tuning ML models in production is ensuring that the updated models generalize well to new, unseen data without overfitting to recent trends or noise. Additionally, coordinating with data engineers and software developers is crucial to maintain data pipelines and model deployment workflows. Managing computational resources and keeping track of model versions for reproducibility can also be complex, especially in fast-paced or large-scale environments. Regular communication with stakeholders is important to align model updates with business objectives and to ensure the smooth integration of improvements.

What are the key skills and qualifications needed to thrive as an ML model fine tuning specialist, and why are they important?

To thrive as an ML Model Fine Tuning Specialist, you need a solid background in machine learning, statistics, programming (often Python), and experience with model training and evaluation. Familiarity with frameworks such as TensorFlow, PyTorch, and tools like Hugging Face Transformers, along with experience in managing GPUs and cloud platforms, is typically required. Strong problem-solving skills, attention to detail, and effective communication help you understand project requirements and collaborate with data scientists and engineers. These skills are crucial for optimizing model performance, ensuring accurate results, and delivering robust AI solutions tailored to specific business needs.

What is the difference between Ml Model Fine Tuning vs Data Scientist?

AspectMl Model Fine TuningData Scientist
CredentialsKnowledge of machine learning frameworks, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentFocus on model optimization, coding, and experimentationData analysis, modeling, and interpretation
Industry UsageAI/ML development teams, tech companiesResearch, analytics, business intelligence

While Ml Model Fine Tuning involves adjusting pre-trained models to improve performance, Data Scientists analyze data, develop models, and interpret results. Fine tuning is a specialized task within the broader scope of a Data Scientist's role, often requiring similar technical skills but focusing more on model optimization.

What job categories do people searching Ml Model Fine Tuning jobs in Nebraska look for?

The top searched job categories for Ml Model Fine Tuning jobs in Nebraska are:

What cities in Nebraska are hiring for Ml Model Fine Tuning jobs?

Cities in Nebraska with the most Ml Model Fine Tuning job openings:

Ml Model Fine Tuning jobs near you

AI Engineer

penlink

Lincoln, NE • On-site

Full-time

Re-posted 20 days ago

Job description

Penlink is a technology company bringing clarity to complex data for people who need it now. We partner with law enforcement agencies across the United States, offering a software solution to manage data and aid investigators solving crimes. It sounds like a lot of data and analytics, but really, it’s about improving the world and keeping safe the places we call home. 

We focus on creating products that positively impact our communities and being "in the mission" and less about the laidback culture and amazing benefits – even though we offer those too. With our get it done attitude and focused mission we are growing at an unprecedented rate and are therefore seeking an AI Engineer to design, build, and ship AI-powered features in production. You’ll work across the stack from prompt design and model integration to evaluation, deployment, and optimization.  You’ll partner closely with product, design, and engineering to turn business needs into reliable AI systems. 

This role is a great fit if you enjoy working at the intersection of applied machine learning and product engineering, and you’re comfortable iterating in a space where the tools and best practices are still evolving quickly. 

YOUR RESPONSIBILITIES 

  • Design, develop, and implement AI-powered features and applications 
  • Research, fine-tune, and deploy machine learning and large language models to support business and product initiatives 
  • Integrate AI models into existing applications and backend systems through APIs and scalable architectures 
  • Build and maintain data pipelines, including collecting, cleaning, preparing, and validating datasets for model training and evaluation 
  • Monitor, evaluate, and maintain model performance through testing, benchmarking, and performance metrics 
  • Optimize AI systems for scalability, latency, reliability, and cost efficiency 
  • Collaborate cross-functionally with Product, Design, and Engineering teams to translate business needs into practical AI-driven solutions 
  • Contribute to continuous improvement efforts surrounding AI development processes, tooling, and best practices 
  • Communicate AI capabilities, limitations, and recommendations clearly to both technical and non-technical stakeholders 
  • Promote responsible AI practices by considering model bias, ethical implications, and system transparency throughout development 

Requirements:

YOUR COMPETENCIES & EXPERIENCE 

  • 5+ years of professional software engineering experience, including hands-on experience building and deploying production AI or machine learning systems 
  • Demonstrated experience delivering at least one AI-powered product or feature to end users in a production environment 
  • Strong programming skills, particularly in Python 
  • Experience with machine learning and deep learning frameworks such as PyTorch, TensorFlow, or JAX 
  • Familiarity with large language model ecosystems and tools including prompt engineering, embeddings, vector databases, and Retrieval-Augmented Generation (RAG) pipelines 
  • Experience fine-tuning and evaluating machine learning or large language models 
  • Knowledge of cloud platforms and deploying AI/ML solutions within scalable environments 
  • Strong analytical and problem-solving skills with the ability to navigate ambiguity and rapidly evolving technologies 
  • Excellent written and verbal communication skills, including the ability to explain AI concepts and model behavior to non-technical audiences 
  • Understanding of AI limitations, bias mitigation, and ethical considerations related to machine learning systems 
  • Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent practical experience 

This position currently follows a hybrid schedule requiring two days per week in our Lincoln, Nebraska office. Onsite requirements may be adjusted based on business needs and company or departmental policy.