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Ml Model Fine Tuning Jobs in Ohio (NOW HIRING)

Synapticon GmbH

$12.75 - $17/hr

WHAT WE ARE LOOKING FOR We're looking for a motivated AI/ML Engineering Intern (ideally 6 months ... Experience with foundation model fine-tuning is a strong plus -- this work involves adapting large ...

Synapticon GmbH

WHAT WE ARE LOOKING FOR We're looking for a motivated AI/ML Engineering Intern (ideally 6 months ... Experience with foundation model fine-tuning is a strong plus -- this work involves adapting large ...

Hudson Manpower

Data Scientist

Cincinnati, OH · On-site

$55 - $60/hr

Uplift Modeling * Heterogeneous Treatment Effect Modeling * Define treatments, control groups ... fine-tuning, and agentic AI where applicable. * Evaluate emerging AI/ML technologies for production ...

Lorven Technologies

AI Engineer

Columbus, OH · On-site

The ideal candidate will have hands-on experience building and deploying AI/ML solutions ... Strong knowledge of prompt engineering, embeddings, model evaluation, and fine-tuning. * Experience ...

Lorven Technologies, Inc.

AI Engineer

Columbus, OH · On-site

The ideal candidate will have hands-on experience building and deploying AI/ML solutions ... Strong knowledge of prompt engineering, embeddings, model evaluation, and fine-tuning. * Experience ...

neeyo Switzerland AG

$80 - $110/hr

Sehr gute Kenntnisse in Python (AI/ML) * Erfahrung mit Modelltraining, Fine-Tuning und Datenanalyse * Gute Kenntnisse in TypeScript / JavaScript und Go * Erfahrung mit Datenbanken (SQL/MySQL) von ...

Eisneramper Llp

AI Solution Architect

Columbus, OH · On-site +1

$60.75 - $80.25/hr

GPT models, prompt engineering, and fine-tuning * Azure Cognitive Services: Document Intelligence ... Data engineering and ML model development * Programming Languages: Python, C#, JavaScript ...

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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.

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Ml Model Fine Tuning jobs near you

AI/ML Engineering Intern - Behavioral Safety (m/f/d)

On-site

Synapticon GmbH
See more jobs at Synapticon GmbHexternal

$12.75 - $17/hr

Other

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

Job description

WHAT WE ARE LOOKING FOR

We're looking for a motivated AI/ML Engineering Intern (ideally 6 months, mandatory internship) to join our Safety AI team in Schönaich (close to Stuttgart), with a flexible start date. If you're excited about applying machine learning to genuinely open research questions in humanoid robotics — from semantic intent recognition to AI-based fall strategies — this is a rare opportunity to do research with a direct path to product.


HERE'S WHERE YOU MAKE AN IMPACT

  • Prototype multimodal foundation model integration for robot safety context — exploring how large-scale models can give robots semantic awareness of their environment and the people in it

  • Develop and test AI-based fall strategy controllers, working on one of the more genuinely open research questions in humanoid deployment

  • Implement ML model inference in the robot control loop — bridging the gap between research models and the timing constraints of real-time robot software

  • Benchmark behavioral safety classifiers for semantic intent recognition in simulation, building the evidence base for what these models can and can't reliably distinguish in deployment

  • Build evaluation frameworks for AI safety behaviour aligned with ISO/IEC TR 5469 — contributing to a principled, documented approach to assessing AI in safety-relevant contexts


WHO YOU ARE

  • Enrolled MSc student in Machine Learning, Computer Science, or Robotics — with a research mindset and the discipline to support it with rigorous, reproducible evaluation

  • Strong PyTorch skills; hands-on RL experience using Stable Baselines3, Isaac Lab, or similar frameworks — you've trained policies and analysed why they fail, not just run examples

  • You've integrated ML inference pipelines with ROS2 — or are confident you can make it work cleanly in a real-time control context

  • Experience with foundation model fine-tuning is a strong plus — this work involves adapting large models for a specific purpose, not just deploying them off the shelf

  • You're interested in safety-critical AI — not as a compliance checkbox, but as a genuinely hard technical and methodological problem

  • Strong mathematical foundation across linear algebra, optimisation, and probability — you're comfortable with the theory behind what you implement


WHAT'S IN IT FOR YOU

  • Flexible working hours when life takes unexpected turns

  • Company support for gym memberships through Wellpass

  • Birthday vouchers

  • Drinks and coffee (free of charge, of course)

  • Snacks and fruit for all employees, as well as breakfast every Monday

  • Regular global team-building events such as boot camps, skiing, and summer BBQs

  • An international team with long-term prospects

  • Highly competent colleagues who are experts in their field and happy to support you in your learning and growth

  • Modern office space

  • Our company's WG (upon availability)

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