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Ml Model Fine Tuning Jobs (NOW HIRING)

Jobtailor

Senior Lead AI Engineer

Foster City, CA · On-site

$140 - $190/hr

Build and own the end-to-end model fine-tuning pipeline: data preprocessing, training, evaluation ... Mentor ML engineers across the team. Requirements * 10+ years of software engineering experience ...

Photon

GenAI Developer / Python

Manhattan, NY · On-site

$55.50 - $76.25/hr

ML/DL: PyTorch, TensorFlow, and Model Fine-tuning. Deployment: Docker, Production API management, and LLM monitoring. Tools: Prompt Engineering, Workflow Design, and GenAI Optimization. Key ...

Socket.dev

Robotics AI Engineer

Cupertino, CA · On-site

$180 - $240/hr

Experience with model fine-tuning and distillation for efficient, on-device deployment * Hands-on experience with inference engines such as llama.cpp, vLLM, MLX, or Core ML * Experience building ...

Apple

Sr. Robotics AI Engineer

Cupertino, CA · On-site

$128K - $177K/yr

... model fine-tuning and distillation for efficient, on-device deployment Hands-on experience with inference engines such as llama.cpp, vLLM, MLX, or Core ML Experience building agent harnesses for ...

Kapitus

MLOps Architect

Arlington, VA · On-site

$117K - $189K/yr

Data ingestion & preprocessing o Feature engineering / embedding generation o Model training & fine-tuning (traditional ML + foundation models) * Model evaluation & validation * Deployment (real-time ...

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How much do ml model fine tuning jobs pay per hour?

As of Aug 23, 2026, the average hourly pay for ml model fine tuning in the United States is $69.33, according to ZipRecruiter salary data. Most workers in this role earn between $56.73 and $76.92 per hour, depending on experience, location, and employer.

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
Infographic showing various Ml Model Fine Tuning job openings in the United States as of August 2026, with employment types broken down into 78% Full Time, and 22% Contract. Highlights an 67% In-person, and 33% Remote job distribution, with an average salary of $144,212 per year, or $69.3 per hour.

Sr Data Scientist - Gen AI ML - Tampa/Irving/ Mississauga

Photon

Irving, TX • On-site

Full-time, Contractor

Medical, Dental, Vision, Retirement, PTO

Re-posted 29 days ago

Job description


Role Summary:
We are seeking a Generative AI Engineer to build, optimize, and scale production-ready AI applications. You will design complex multi-agent systems, implement advanced RAG pipelines, and manage the deployment of both frontier and local LLMs. The ideal candidate blends deep machine learning expertise with modern software engineering practices.
Technical Stack:
LLMs: Gemini, OpenAI, Claude, Llama, and Local Model deployment.
Frameworks: LangChain, LlamaIndex, and Hugging Face.
Orchestration: LangGraph and Multi-Agent Systems (MAS).
Development: Python, FastAPI, and Asynchronous Programming.
RAG & Data: PostgreSQL, Vector Databases, and Advanced Retrieval strategies.
ML/DL: PyTorch, TensorFlow, and Model Fine-tuning.
Deployment: Docker, Production API management, and LLM monitoring.
Tools: Prompt Engineering, Workflow Design, and GenAI Optimization.
Key Responsibilities:
Develop and orchestrate sophisticated AI workflows using LangGraph and multi-agent architectures.
Build and maintain Advanced RAG systems utilizing LlamaIndex and vector databases for high-accuracy retrieval.
Integrate and swap diverse LLMs (commercial and open-source) based on performance and cost requirements.
Design and deploy high-performance, scalable backend services using FastAPI and Async Python.
Fine-tune large language models (LLMs) using PyTorch/TensorFlow to improve domain-specific performance.
Optimize GenAI workflows for latency, cost, and reliability using advanced prompt engineering and monitoring tools.
Containerize and deploy AI services via Docker to production environments.
Required Qualifications:
7+ years of experience ; Hands-on experience building and deploying GenAI applications in a production setting.
Strong proficiency in Python and the modern AI library ecosystem (LangChain, LlamaIndex, etc.).
Experience with vector search, embedding models, and advanced data retrieval patterns.
Knowledge of model fine-tuning techniques and local LLM quantization/hosting.
Familiarity with production-grade monitoring, API security, and CI/CD for ML.
Compensation, Benefits and Duration
Minimum Compensation: USD 62,000
Maximum Compensation: USD 217,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is not available for independent contractors
No applications will be considered if received more than 120 days after the date of this post

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