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Llm Fine Tune Model Jobs (NOW HIRING)

Senior AI/LLM Engineer

$179K - $201K/yr

Develop and fine-tune LLMs and RAG pipelines to reason over Internet-scale data, summarize findings ... Continuously evaluate and improve model performance through RAGAS, LangSmith, and automated ...

Solutions Architect, LLM Model Builder

Santa Clara, CA · On-site

$74 - $97.50/hr

... fine-tune, optimize, and deploy foundation model solutions for customer workloads. The Partner ... Familiarity with Nemotron, NeMo, Dynamo, TensorRT-LLM, Triton, vLLM, and similar inference and ...

Senior AI/LLM Engineer

$179K - $201K/yr

Develop and fine-tune LLMs and RAG pipelines to reason over Internet-scale data, summarize findings ... Continuously evaluate and improve model performance through RAGAS, LangSmith, and automated ...

We are looking for a GenAI Ops Engineer to train, fine-tune, and deploy Generative AI models (LLMs ... Requirements: * Experience with LLM training, fine-tuning, and inference optimization.

... Fine-tune and evaluate LLMs using both open-source and proprietary data • Collaborate with ... improve model quality, latency, explainability, and safety • Stay ahead of the curve in LLM ...

Python Developer

Fremont, CA · On-site

$55.25 - $76/hr

Responsibilities : • Design, develop, and optimize Python applications with a focus on LLM integration and deployment. • Build and fine-tune deep learning models using PyTorch, ensuring ...

Python Developer

Fremont, CA · On-site

$55.25 - $76/hr

Responsibilities : • Design, develop, and optimize Python applications with a focus on LLM integration and deployment. • Build and fine-tune deep learning models using PyTorch, ensuring ...

Senior AI Engineer

$107K - $146K/yr

Fine-tune, adapt, and evaluate LLMs for domain-specific use cases using prompt engineering ... LLM fine-tuning, prompt engineering, embeddings, vector databases, semantic search, and model ...

Fine-tune models on targeted datasets to improve baseline performance, preventing issues like poor ... Deep hands-on experience with prompt engineering, LLM experimentation, and systematic evaluation of ...

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Llm Fine Tune Model information

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$166.2K

$193.5K

How much do llm fine tune model jobs pay per year?

As of Aug 23, 2026, the average yearly pay for llm fine tune model in the United States is $166,249.00, according to ZipRecruiter salary data. Most workers in this role earn between $158,000.00 and $170,000.00 per year, depending on experience, location, and employer.

What is an LLM fine-tune model?

An LLM fine-tune model refers to a large language model (LLM) that has been further trained on a specific dataset to specialize in certain tasks or domains. Fine-tuning allows organizations or individuals to adapt a general-purpose LLM, such as GPT or BERT, to better understand and respond to domain-specific language, requirements, or user needs. This process improves the model's accuracy, relevance, and usefulness for specialized applications. Fine-tuning typically involves using transfer learning techniques and requires a curated dataset for the desired task.

What are the key skills and qualifications needed to thrive as an LLM fine-tune model engineer?

To thrive as an LLM Fine-Tune Model Engineer, you need a strong background in machine learning, natural language processing, and programming (typically Python), often supported by a degree in computer science or related fields. Experience with deep learning frameworks (such as PyTorch or TensorFlow), model evaluation tools, and familiarity with cloud platforms or MLOps tools is essential. Analytical thinking, attention to detail, and effective communication help in troubleshooting, interpreting results, and collaborating with cross-functional teams. These skills ensure the development of robust, accurate, and scalable language models tailored to specific business needs.

What are some common challenges faced when fine-tuning large language models (LLMs) in a professional setting?

Fine-tuning large language models often involves handling vast datasets, ensuring data privacy, and balancing computational resource constraints. Professionals in this role must troubleshoot issues related to overfitting, bias in training data, and model drift. Collaboration with data engineers, domain experts, and MLOps teams is crucial to ensure the model meets specific business needs while maintaining ethical and performance standards.

What is the difference between Llm Fine Tune Model vs Data Scientist?

AspectLlm Fine Tune ModelData Scientist
Required CredentialsKnowledge of machine learning, NLP, programming skillsStatistics, programming, domain expertise
Work EnvironmentAI development teams, research labsBusiness, research, analytics teams
Industry UsageAI, tech companies, startupsFinance, healthcare, marketing, tech

The main difference is that an Llm Fine Tune Model focuses on customizing large language models for specific tasks, while a Data Scientist analyzes data to generate insights and build models. Both roles require programming and analytical skills, but their applications and focus areas differ significantly.

Infographic showing various Llm Fine Tune Model job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 13% Part Time, and 2% Contract. Highlights an 89% Physical, 4% Hybrid, and 7% Remote job distribution, with an average salary of $166,249 per year, or $79.9 per hour.

Senior Machine Learning Engineer, Apple Search & Knowledge Platforms

Apple

Santa Clara, CA • On-site

$143K - $189K/yr

Full-time

Re-posted 19 days ago


Apple rating

8.0

Company rating: 8.0 out of 10

Based on 677 frontline employees who took The Breakroom Quiz

7th of 30 rated technology retailers


Job description

The AI, Search & Knowledge Platforms team builds amazing products and services for Apple's customers while serving as a foundational partner to teams across Apple. The team delivers world-class AI, search, and knowledge systems powering Siri, Apple Intelligence, Safari, and iMessage, and operates the foundational platforms and infrastructure that keep these intelligent experiences running at hyperscale.
As part of this group, you will be doing large scale machine learning and deep learning research and development to improve Open Domain Question Answering (using both structured knowledge graph data and unstructured web data) and Summarization as well as developing fundamental building blocks needed for Artificial Intelligence. This involves developing sophisticated machine learning and large language models (LLMs) to understand user queries, retrieve and rank relevant documents across multiple sources and synthesize information across documents to provide user with a direct answer that best satisfies their intent and information seeking needs. Additionally, you will research and develop the state-of-the-art LLMs for summarizing personal data such as emails, messages, and notifications.
You will also work with researchers and data scientists to develop, fine-tune, and evaluate domain specific Large Language Models for various tasks and applications in Apple's AI powered products and conduct applied research to transfer the cutting edge research in generative AI to production ready technologies.
Description
As a member of our fast-paced group, you'll have the unique and rewarding opportunity to shape upcoming products from Apple. We are looking for highly motivated machine learning engineers and researchers having strong machine learning and deep learning fundamentals with hands-on experience in fine-tuning deep learning and large language models.
Minimum Qualifications
2+ years of experience working with Deep learning or LLM model development for various NLP tasks and RAG applications including prompt engineering, training data collection and generation, model fine-tuning and model evaluation.
Experience working with Python and at least one of the deep learning frameworks such as TensorFlow, PyTorch, or JAX.
Master's in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
Preferred Qualifications
PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
4+ years of experience with large-scale model training, optimization, and deployment
One or more scientific publications in various conferences and journals
Outstanding communication and interpersonal skills with ability to work with cross-functional teams.
1+ year of experience in various state-of-the-art techniques related to LLM fine-tuning in 1 or more of the following areas:
Supervised Fine-tuning (SFT) with Rejection Sampling
Preference-based fine-tuning techniques (e.g RLHF, Reward model, DPO, PPO, GRPO etc.)
Parameter efficient fine-tuning techniques (e.g LoRA)
Hallucination reduction and factual accuracy improvements
Designing and implementing safety guardrails

What Apple employees say

Pay

Benefits

Hours and flexibility

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976