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Ml Model Fine Tuning Jobs (NOW HIRING)

Design and implement AI/ML models to support business objectives and innovation initiatives ... Familiarity with vector databases, prompt engineering, and model fine-tuning. * Exposure to AI ...

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 ...

... and fine-tuning ML models to optimize for performance • Communicating results to stakeholders Qualifications : Required : • Designing scalable machine learning infrastructure, including ...

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 ...

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 ...

$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 ...

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Ml Model Fine Tuning information

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$10

$69

$143

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

As of Sep 13, 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.

More about Ml Model Fine Tuning jobs

What cities are hiring for Ml Model Fine Tuning jobs?

Cities with the most Ml Model Fine Tuning job openings:

What states have the most Ml Model Fine Tuning jobs?

States with the most job openings for Ml Model Fine Tuning jobs include:

Infographic showing various Ml Model Fine Tuning job openings in the United States as of September 2026, with employment types broken down into 62% Full Time, and 38% Contract. Highlights an 62% In-person, and 38% Remote job distribution, with an average salary of $144,212 per year, or $69.3 per hour.

Staff ML Engineer, Fine Tuning - Slack

San Francisco, CA • On-site

Other

Medical, Dental, Vision, Life, Retirement

Posted 26 days ago


Key responsibilities

  • Design and execute finetuning strategies for large language models and other deep learning architectures tailored to Slack's NLP tasks.

  • Own the model training lifecycle end-to-end, including data curation, training infrastructure, hyperparameter optimization, evaluation, deployment, and monitoring.

  • Build and maintain scalable finetuning training pipelines on GPU infrastructure.


Job description

## Staff ML Engineer, Fine Tuning - SlackApplyremote type: Office Tech-Flexiblelocations: Washington - Seattle: Georgia - Atlanta: California - San Franciscotime type: Full timeposted on: Posted 2 Days Agotime left to apply: End Date: August 10, 2026 (30+ days left to apply)job requisition id: JR344115*To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.*Job CategorySoftware EngineeringJob Details****About Salesforce****Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.**\*IN SCHOOL OR GRADUATED WITHIN THE LAST 12 MONTHS? PLEASE VISIT FUTURE FORCE FOR OPPORTUNITIES\*** Slack is looking for a Staff Machine Learning Engineer with deep expertise in model training and finetuning to join our ML team. You'll design, train, and ship NLP models that power core product experiences — from summarization and search ranking to generative AI features used by millions daily. This role is hands-on: you'll work at a low level with training frameworks, optimize model architectures, build finetuning pipelines, and own the full lifecycle from experiment to production.## ## **At Slack, that impact can be huge:*** We have over 10 million daily active users relying on our product.* At peak usage, a million messages a minute pass through Slack.* During the week, our users spend over a billion minutes a day active in our product.Machine learning engineers at Slack ship models that serve millions of users daily. This role owns that end-to-end: finetuning models for Slack's NLP tasks and putting them into production with the rigor and reliability our users expect. We're not looking for someone who hands off a checkpoint — we want someone who sees it through to serving traffic. Broader ML skills — data pipelines, experimentation, feature engineering — are valuable here too, but deep training and productionization expertise is the core of this role. This is a practical machine learning team, not a research team. Our goal is to deliver business value with machine learning and data in whatever form that takes. Sometimes that means bootstrapping something simple like a logistic regression and moving on. Other times that means developing sophisticated, finely tuned models and novel solutions to Slack’s unique problem space. We are looking for engineers who are driven by driving impact for our business, building great products for our customers, and delivering robust, reliable services with machine learning.## ## **What you will be doing:*** Design and execute finetuning strategies for large language models and other deep learning architectures tailored to Slack's NLP tasks (summarization, ranking, classification, generation).* Own the model training lifecycle end-to-end: data curation, training infrastructure, hyperparameter optimization, evaluation, deployment and monitoring.* Build and maintain scalable finetuning training pipelines on GPU infrastructure.* Brainstorm with Product Managers, Designers and Frontend Engineers to conceptualize and build new features for our large (and growing!) user base.* Produce high-quality results by leading or contributing heavily to large multi-functional projects that have a significant impact on the business.* Mentor other engineers and deeply review code.* Improve engineering standards, tooling, and processes.## What you should have:* 5+ years of hands-on experience training and fine-tuning deep learning models in NLP (or a closely related domain like speech, IR, or multimodal).* 5+ years of experience with common deep learning frameworks like PyTorch, TensorFlow, JAX, etc* Track record of shipping fine-tuned models to production that serve real users at scale — not just research prototypes.* Experience with functional or imperative programming languages: PHP, Python, Ruby, Go, C, Scala or Java.* An analytical and data driven mindset, and know how to measure success with complicated ML/AI products.* Led technical architecture discussions and helped drive technical decisions within the team.* The ability to write understandable, testable code with an eye towards maintainability.* Strong communication skills and you are capable of explaining complex technical concepts to designers, support, and other specialists.## ## Nice to have:* Expertise with recommendation systems or search.* Familiarity with model optimization for inference (quantization, pruning, speculative decoding, compilation via TorchScript/TensorRT/ONNX).* Experience with retrieval-augmented generation and hybrid retrieval/generation systems.* Broad experience across NLP, ML, and Generative AI capabilities.* Knowledge of using multiple data types in RAG solutions including structured, unstructured, and knowledge graphs.* Broad experience across NLP, ML, and Generative AI capabilities.Unleash Your PotentialWhen you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and *be your best*, and our AI agents accelerate your impact so you can *do your best*. Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world.AccommodationsIf you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form.Please note that Salesforce uses artificial intelligence (AI) tools to help our recruiters assess and evaluate candidates’ resumes and qualifications throughout the recruiting process. Humans will always make any candidate selection and hiring decisions. Please see our Candidate Privacy Statement for more information about how we use your personal data and your rights, including with regard to use of AI tools and opt out options.Posting StatementSalesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at the following link: https://www.salesforcebenefits.com.Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.### ### ### ### At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions.The typical base salary range for this position is $197,300 - $313,700 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $237,700 - $344,700 annually.The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.### ### ### ###### About Us****Slack is a messaging app for business which lets you organize conversations into channels so everyone moves faster and stays in sync. It’s a platform that connects everyone in your business—employees, customers, and partners— securely with each other and integrates easily with apps you use every day to get work done. And everything happens, using any device, within a digital workspace that’s super easy to use.********Ensuring a diverse and inclusive workplace where we learn from each other is core to Slack’s values. We welcome people of different backgrounds, experiences, abilities and perspectives. We are an equal opportunity employer and a pleasant and supportive place to work.********Come do the best work of your life here at Slack.**** #J-18808-Ljbffr