Machine Learning Engineer 5
San Jose, CA · On-site
They are seeking a Machine Learning Engineer to join the Brand Intelligence Predict team ... MLOps practices and pipelines. • Familiarity with cloud ML services (AWS, GCP, Azure). • ...
Machine Learning Engineer 5
San Jose, CA · On-site
They are seeking a Machine Learning Engineer to join the Brand Intelligence Predict team ... MLOps practices and pipelines. • Familiarity with cloud ML services (AWS, GCP, Azure). • ...
Machine Learning Engineer
Sunnyvale, CA · On-site
Description Apple's Video Computer Vision (VCV) Face and Body technologies team is looking for a skilled Machine Learning Engineer with experience developing ML models for computer vision and ...
Machine Learning Engineer
Sunnyvale, CA · On-site
Description Apple's Video Computer Vision (VCV) Face and Body technologies team is looking for a skilled Machine Learning Engineer with experience developing ML models for computer vision and ...
Machine Learning Engineer
Sunnyvale, CA · On-site
Description Apple's Video Computer Vision (VCV) Face and Body technologies team is looking for a skilled Machine Learning Engineer with experience developing ML models for computer vision and ...
Machine Learning Engineer
Sunnyvale, CA · On-site
Description Apple's Video Computer Vision (VCV) Face and Body technologies team is looking for a skilled Machine Learning Engineer with experience developing ML models for computer vision and ...
As a Staff Machine Learning Engineer, you will be responsible for driving the design, development ... Experience with CI/CD and MLOps pipelines for automated model deployment and monitoring * Track ...
As a Staff Machine Learning Engineer, you will be responsible for driving the design, development ... Experience with CI/CD and MLOps pipelines for automated model deployment and monitoring * Track ...
Machine Learning Operations Engineer (MLOps)
Fremont, CA · On-site
$50 - $100/hr
... Machine Learning Operations (MLOps) Engineer to help develop, deploy, and scale AI/ML solutions within advanced manufacturing operations. This is a highly visible, cross-functional role focused on ...
Quick apply
Machine Learning Operations Engineer (MLOps)
Fremont, CA · On-site
$50 - $100/hr
... Machine Learning Operations (MLOps) Engineer to help develop, deploy, and scale AI/ML solutions within advanced manufacturing operations. This is a highly visible, cross-functional role focused on ...
As a Staff Machine Learning Engineer, you will be responsible for driving the design, development ... Experience with CI/CD and MLOps pipelines for automated model deployment and monitoring * Track ...
As a Staff Machine Learning Engineer, you will be responsible for driving the design, development ... Experience with CI/CD and MLOps pipelines for automated model deployment and monitoring * Track ...
The Machine Learning Engineer will design and operate scalable ML infrastructure, working closely ... Required : • 3+ years of experience in MLOps, ML infrastructure, or related backend/platform ...
The Machine Learning Engineer will design and operate scalable ML infrastructure, working closely ... Required : • 3+ years of experience in MLOps, ML infrastructure, or related backend/platform ...
Machine Learning Engineer
Fremont, CA · On-site
RIT Solutions, Inc. is seeking a highly skilled Machine Learning Engineer to join their Software Machine Learning and Computer Vision team. The role involves designing, developing, and deploying ...
Machine Learning Engineer
Fremont, CA · On-site
RIT Solutions, Inc. is seeking a highly skilled Machine Learning Engineer to join their Software Machine Learning and Computer Vision team. The role involves designing, developing, and deploying ...
Machine Learning Engineer
$150K - $450K/yr
The Role As a Machine Learning Engineer at the Institute of Foundation Models, your primary responsibility is to develop and implement innovative machine learning models that address real-world ...
Quick apply
Machine Learning Engineer
$150K - $450K/yr
The Role As a Machine Learning Engineer at the Institute of Foundation Models, your primary responsibility is to develop and implement innovative machine learning models that address real-world ...
As a Staff Machine Learning Engineer, you will be responsible for driving the design, development ... Experience with CI/CD and MLOps pipelines for automated model deployment and monitoring * Track ...
As a Staff Machine Learning Engineer, you will be responsible for driving the design, development ... Experience with CI/CD and MLOps pipelines for automated model deployment and monitoring * Track ...
Staff Machine Learning Engineer
Palo Alto, CA · On-site
$230 - $345/hr
As an Machine Learning Engineer (MLE), you're expected to: * Research Execution & Technical ... Bridge the gap between research and production by designing architectures that respect MLOps ...
Staff Machine Learning Engineer
Palo Alto, CA · On-site
$230 - $345/hr
As an Machine Learning Engineer (MLE), you're expected to: * Research Execution & Technical ... Bridge the gap between research and production by designing architectures that respect MLOps ...
Machine Learning Engineer
Sunnyvale, CA · On-site
The Machine Learning Engineer will be responsible for developing and implementing innovative machine learning models, collaborating with cross-functional teams to deploy scalable solutions that drive ...
Machine Learning Engineer
Sunnyvale, CA · On-site
The Machine Learning Engineer will be responsible for developing and implementing innovative machine learning models, collaborating with cross-functional teams to deploy scalable solutions that drive ...
BeeGenius is building the future of work, and they are seeking an AI/Machine Learning Engineer to join their team. In this role, you will be responsible for developing and implementing machine ...
BeeGenius is building the future of work, and they are seeking an AI/Machine Learning Engineer to join their team. In this role, you will be responsible for developing and implementing machine ...
Machine Learning Engineer
Sunnyvale, CA · On-site
As a Machine Learning Engineer, you will develop and implement innovative machine learning models, collaborate with cross-functional teams, and contribute to MBZUAI's mission of driving impactful AI ...
Machine Learning Engineer
Sunnyvale, CA · On-site
As a Machine Learning Engineer, you will develop and implement innovative machine learning models, collaborate with cross-functional teams, and contribute to MBZUAI's mission of driving impactful AI ...
Machine Learning Engineer 5
San Jose, CA · On-site
Machine Learning Engineer - Brand Intelligence Predict The Opportunity Join us at Adobe as a ... Hands-on knowledge of MLOps practices and pipelines. * Familiarity with cloud ML services (AWS, GCP ...
Machine Learning Engineer 5
San Jose, CA · On-site
Machine Learning Engineer - Brand Intelligence Predict The Opportunity Join us at Adobe as a ... Hands-on knowledge of MLOps practices and pipelines. * Familiarity with cloud ML services (AWS, GCP ...
Machine Learning Engineer - Brand Intelligence Predict The Opportunity Join us at Adobe as a ... Hands-on knowledge of MLOps practices and pipelines. * Familiarity with cloud ML services (AWS, GCP ...
Machine Learning Engineer - Brand Intelligence Predict The Opportunity Join us at Adobe as a ... Hands-on knowledge of MLOps practices and pipelines. * Familiarity with cloud ML services (AWS, GCP ...
They are seeking a Machine Learning Engineer to design and develop scalable training pipelines for multimodal AI systems, collaborating with data engineering and research teams to drive the technical ...
They are seeking a Machine Learning Engineer to design and develop scalable training pipelines for multimodal AI systems, collaborating with data engineering and research teams to drive the technical ...
We are looking for a Machine Learning Engineer to join and play a big part in the next revolution of Maps; to enable users to find more things in innovative ways. On our team, you will have plenty of ...
We are looking for a Machine Learning Engineer to join and play a big part in the next revolution of Maps; to enable users to find more things in innovative ways. On our team, you will have plenty of ...
The Machine Learning Engineer will design and develop scalable training pipelines for multimodal AI systems, collaborate with data engineering and research teams, and influence core decisions around ...
The Machine Learning Engineer will design and develop scalable training pipelines for multimodal AI systems, collaborate with data engineering and research teams, and influence core decisions around ...
Machine Learning Engineer
Sunnyvale, CA · On-site
$150.40 - $277.60/hr
Description Apple's Video Computer Vision (VCV) Face and Body technologies team is looking for a skilled Machine Learning Engineer with experience developing ML models for computer vision and ...
Machine Learning Engineer
Sunnyvale, CA · On-site
$150.40 - $277.60/hr
Description Apple's Video Computer Vision (VCV) Face and Body technologies team is looking for a skilled Machine Learning Engineer with experience developing ML models for computer vision and ...
Mlops Machine Learning Engineer information
See Milpitas, CA salary details
$36.7K - $53.9K
1% of jobs
$53.9K - $71K
1% of jobs
$71K - $88.2K
5% of jobs
$88.2K - $105.4K
6% of jobs
$119.6K is the 25th percentile. Wages below this are outliers.
$105.4K - $122.5K
14% of jobs
$122.5K - $139.7K
14% of jobs
The median wage is $148.3K / yr.
$139.7K - $156.8K
18% of jobs
$156.8K - $174K
14% of jobs
$177.5K is the 75th percentile. Wages above this are outliers.
$174K - $191.2K
12% of jobs
$191.2K - $208.3K
11% of jobs
$208.3K - $225.5K
5% of jobs
$36.7K
$150.1K
$225.5K
How much do mlops machine learning engineer jobs pay per year?
As of Aug 13, 2026, the average yearly pay for mlops machine learning engineer in Milpitas, CA is $150,064.00, according to ZipRecruiter salary data. Most workers in this role earn between $118,300.00 and $180,600.00 per year, depending on experience, location, and employer.
What does an MLOps machine learning engineer do?
An MLOps Machine Learning Engineer bridges the gap between data science and IT operations by developing, deploying, and maintaining machine learning models in production environments. They are responsible for automating workflows, managing model versioning, monitoring performance, and ensuring scalability and reliability of ML systems. Their work enables organizations to deploy machine learning solutions efficiently and consistently, making it easier to update and manage models as business needs evolve.
How does an MLOps machine learning engineer typically collaborate with data scientists and software engineers during the deployment of machine learning models?
An MLOps Machine Learning Engineer acts as a bridge between data scientists and software engineers, ensuring machine learning models transition smoothly from development to production. They often work closely with data scientists to understand model requirements, data pipelines, and performance metrics, while also collaborating with software engineers to integrate models into scalable systems. Regular communication, shared documentation, and joint troubleshooting sessions are common, as the role requires aligning model performance with system reliability and maintainability. This collaborative environment helps ensure that models are robust, scalable, and impactful in real-world applications.
What is the difference between Mlops Machine Learning Engineer vs Data Scientist?
| Aspect | Mlops Machine Learning Engineer | Data Scientist |
|---|---|---|
| Required Credentials | Bachelor's or master's in CS, data science, or related fields; certifications in cloud platforms or MLOps tools | Bachelor's or master's in statistics, data science, or related fields; certifications in data analysis or machine learning |
| Work Environment | Focus on deploying, maintaining, and scaling ML models in production environments | Focus on data analysis, model development, and insights generation |
| Employer & Industry Usage | Tech companies, startups, enterprises implementing ML solutions | Research institutions, analytics firms, tech companies for data insights |
While both roles involve machine learning, Mlops Machine Learning Engineers specialize in deploying and maintaining models in production, ensuring scalability and reliability. Data Scientists primarily focus on developing models and analyzing data to generate insights. The roles often overlap but differ in their core responsibilities and work environments.
What are the key skills and qualifications needed to thrive as an MLOps machine learning engineer?
To thrive as an MLOps Machine Learning Engineer, you need a strong background in machine learning concepts, software engineering, and cloud infrastructure, typically supported by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, GCP, Azure), and certifications such as Google Professional Machine Learning Engineer are highly beneficial. Strong problem-solving abilities, collaboration, and communication skills help you work effectively across data science and engineering teams. These skills are essential for reliably deploying, monitoring, and maintaining scalable machine learning solutions in production environments.
What are popular job titles related to Mlops Machine Learning Engineer jobs in Milpitas, CA? For Mlops Machine Learning Engineer jobs in Milpitas, CA, the most frequently searched job titles are:
What job categories do people searching Mlops Machine Learning Engineer jobs in Milpitas, CA look for? The top searched job categories for Mlops Machine Learning Engineer jobs in Milpitas, CA are:
What cities near Milpitas, CA are hiring for Mlops Machine Learning Engineer jobs? Cities near Milpitas, CA with the most Mlops Machine Learning Engineer job openings:
Adobe rating
8.9
Based on 10 frontline employees who took The Breakroom Quiz
39th of 244 rated software companies
Job description
Job Summary:
Adobe is a leading company that empowers creativity through innovative platforms and tools. They are seeking a Machine Learning Engineer to join the Brand Intelligence Predict team, responsible for building LLM-powered systems to simulate consumer audiences and enhance marketing decision-making.
Responsibilities:
• Design, build, and ship LLM-powered systems that simulate consumer audiences end-to-end, from proof-of-concept to production.
• Develop complex inference and reasoning harnesses on top of frontier LLMs, agentic flows, persona conditioning, retrieval, and sampling strategies tuned for distributional fidelity.
• Fine-tune LLMs on survey, panel, and behavioral data to improve alignment with real-world audience distributions; own the full loop from data curation through eval.
• Build the evaluation datasets, benchmarks, and harnesses that define what 'good' means for synthetic audience quality - distributional fidelity, behavioral validity, subgroup calibration.
• Partner with product management, applied science, and engineering to translate a fast-moving research literature into shipping product features.
Qualifications:
Required:
• Substantial hands-on experience building LLM-based applications in production.
• Demonstrated experience designing and shipping complex inference harnesses on top of large language models (agentic systems, structured reasoning, sampling/decoding strategies, RAG).
• Hands-on experience fine-tuning LLMs with techniques including SFT, preference optimization (DPO/GRPO) and modern post-training tradeoffs.
• Experience with RLHF, RLAIF, or RL-based state alignment of LLMs.
• Proven track record of building evaluation datasets and harnesses — you have opinions about what makes an eval load-bearing versus theater.
• Proficiency in Python and strong grounding in data structures, algorithms, and modern ML tooling (PyTorch, Hugging Face, vLLM, W&B or equivalents).
• Hands-on knowledge of MLOps practices and pipelines.
• Familiarity with cloud ML services (AWS, GCP, Azure).
• Shipped a customer-facing Gen AI feature from proof-of-concept to production end-to-end.
• MS or PhD or equivalent experience in Computer Science, Machine Learning, or a related technical field, or equivalent experience.
Preferred:
• Prior work on synthetic audiences, persona simulation, or LLM-based human behavior modeling.
• Familiarity with the synthetic audiences research literature (e.g., silicon samples, generative agents, SubPOP, HumanLM, DeepBind).
• Experience with public opinion or survey data (GSS, ANES, WVS, MIDUS) or panel-based consumer research data.
Company:
Adobe is a software company that provides its users with digital marketing and media solutions. Founded in 1982, the company is headquartered in San Jose, USA, with a team of 10001+ employees. The company is currently Late Stage.
Adobe is a leading company that empowers creativity through innovative platforms and tools. They are seeking a Machine Learning Engineer to join the Brand Intelligence Predict team, responsible for building LLM-powered systems to simulate consumer audiences and enhance marketing decision-making.
Responsibilities:
• Design, build, and ship LLM-powered systems that simulate consumer audiences end-to-end, from proof-of-concept to production.
• Develop complex inference and reasoning harnesses on top of frontier LLMs, agentic flows, persona conditioning, retrieval, and sampling strategies tuned for distributional fidelity.
• Fine-tune LLMs on survey, panel, and behavioral data to improve alignment with real-world audience distributions; own the full loop from data curation through eval.
• Build the evaluation datasets, benchmarks, and harnesses that define what 'good' means for synthetic audience quality - distributional fidelity, behavioral validity, subgroup calibration.
• Partner with product management, applied science, and engineering to translate a fast-moving research literature into shipping product features.
Qualifications:
Required:
• Substantial hands-on experience building LLM-based applications in production.
• Demonstrated experience designing and shipping complex inference harnesses on top of large language models (agentic systems, structured reasoning, sampling/decoding strategies, RAG).
• Hands-on experience fine-tuning LLMs with techniques including SFT, preference optimization (DPO/GRPO) and modern post-training tradeoffs.
• Experience with RLHF, RLAIF, or RL-based state alignment of LLMs.
• Proven track record of building evaluation datasets and harnesses — you have opinions about what makes an eval load-bearing versus theater.
• Proficiency in Python and strong grounding in data structures, algorithms, and modern ML tooling (PyTorch, Hugging Face, vLLM, W&B or equivalents).
• Hands-on knowledge of MLOps practices and pipelines.
• Familiarity with cloud ML services (AWS, GCP, Azure).
• Shipped a customer-facing Gen AI feature from proof-of-concept to production end-to-end.
• MS or PhD or equivalent experience in Computer Science, Machine Learning, or a related technical field, or equivalent experience.
Preferred:
• Prior work on synthetic audiences, persona simulation, or LLM-based human behavior modeling.
• Familiarity with the synthetic audiences research literature (e.g., silicon samples, generative agents, SubPOP, HumanLM, DeepBind).
• Experience with public opinion or survey data (GSS, ANES, WVS, MIDUS) or panel-based consumer research data.
Company:
Adobe is a software company that provides its users with digital marketing and media solutions. Founded in 1982, the company is headquartered in San Jose, USA, with a team of 10001+ employees. The company is currently Late Stage.
About Adobe
Sourced by ZipRecruiter
Adobe for All is our vision to advance diversity, equity, and inclusion (DEI) across our company and in our communities. We’re focused on creating a more diverse and inclusive workforce; unleashing the full potential of every employee; and driving meaningful impact for Adobe, our industry, and society at large. Creativity has the power to unite us and inspire us to change the world. Through a vision we call Creativity for All, we’re empowering millions of people of all ages and backgrounds to express themselves, reach their full potential, and share their diverse perspectives with the world. We’re committed to advancing the responsible use of technology and driving a positive environmental impact through sustainability and climate action. Our innovations are making a significant impact across AI ethics, security, privacy, trust and safety, accessibility, and sustainability.
Industry
Computer and computer peripheral equipment and software wholesalers
Company size
10,000+ Employees
Headquarters location
San Jose, CA, US
Year founded
1982