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Machine Learning Engineer Opt Jobs in Saratoga, CA

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 ...

Nace AI is a company focused on machine learning solutions, and they are seeking a Machine Learning Engineer to translate cutting-edge research into scalable, production-ready solutions. The role ...

Role Summary We are seeking a highly motivated Machine Learning Engineer with a strong background in model architecture design and algorithm development, ideally with experience in scientific domains ...

Bee Genius is building the future of work and is seeking an AI/Machine Learning Engineer to join their team. The role involves developing and implementing machine learning models and algorithms to ...

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 ...

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 ...

We are seeking a Machine Learning Engineer to join our team developing machine learning solutions for quality assurance and process monitoring in additive manufacturing. Working closely with process ...

Machine Learning Engineer

Fremont, CA ยท On-site

$150K - $220K/yr

We are seeking a Machine Learning Engineer to join our team developing machine learning solutions for quality assurance and process monitoring in additive manufacturing. Working closely with process ...

Machine Learning Engineer

Cupertino, CA ยท On-site

$150K - $225K/yr

As a Machine Learning Engineer on our core AI/ML team, you will design and build GenAI-powered features and workflows leveraging LLMs and modern AI techniques. You will collaborate closely with ...

Machine Learning Engineer

Fremont, CA ยท On-site

$150K - $220K/yr

We are seeking a Machine Learning Engineer to join our team developing machine learning solutions for quality assurance and process monitoring in additive manufacturing. Working closely with process ...

As a Machine Learning Engineer, you will play a central role in translating cutting-edge machine learning research into scalable, production-ready solutions. You will collaborate closely with cross ...

Machine Learning Engineer

Mountain View, CA ยท On-site +1

$117K - $152K/yr

We're looking for a Machine Learning Engineer to join our Offline Infrastructure team. This is an ideal role for a recent university graduate who is excited to work on large-scale systems and apply ...

They are seeking a Machine Learning Engineer to translate research into scalable solutions, collaborating with teams to architect robust systems and integrate AI-driven features into applications.

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 ...

Showing results 21-40

Machine Learning Engineer Opt information

See Saratoga, CA salary details

$34.7K

$142K

$213.4K

How much do machine learning engineer opt jobs pay per year?

As of Aug 8, 2026, the average yearly pay for machine learning engineer opt in Saratoga, CA is $141,990.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,900.00 and $170,900.00 per year, depending on experience, location, and employer.

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models into production environments. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, reliable systems that organizations can use to make predictions or automate tasks. Their responsibilities include data preprocessing, choosing appropriate algorithms, model training, and ensuring the model's performance in real-world applications. Machine Learning Engineers often collaborate with data scientists, data engineers, and product teams to deliver intelligent solutions.

What is the difference between Machine Learning Engineer Opt vs Data Scientist?

AspectMachine Learning Engineer OptData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; certifications in ML toolsBachelor's or Master's in CS, Statistics, or related fields; data analysis certifications
Work EnvironmentDevelops, tests, and deploys ML models in production systemsAnalyzes data, builds models, and provides insights for decision-making
Employer & Industry UsageTech companies, AI startups, e-commerce, financeResearch institutions, tech firms, consulting, finance
Common Search & ComparisonOften compared for technical skills and deployment focusCompared for data analysis and business insights

Machine Learning Engineers Opt focus on deploying scalable ML models in production environments, while Data Scientists primarily analyze data and develop models for insights. Both roles require strong technical skills, but their core responsibilities differ in application and deployment.

What are the key skills and qualifications needed to thrive as a machine learning engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need a solid background in mathematics, statistics, and programming (especially Python), typically supported by a degree in computer science, engineering, or a related field. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch), data processing tools, and cloud platforms, along with relevant certifications, is highly valuable. Strong problem-solving ability, collaboration, and effective communication are standout soft skills in this role. These skills and qualities ensure the successful development, deployment, and integration of machine learning solutions that drive business value.

What are some common challenges machine learning engineers face when deploying models to production environments?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, handling data drift, and integrating models seamlessly with existing systems when deploying to production. Monitoring model performance in real time and retraining models as new data becomes available are also critical tasks. Collaboration with data engineers and DevOps teams is essential to address infrastructure and deployment hurdles while maintaining model accuracy and reliability.
What are popular job titles related to Machine Learning Engineer Opt jobs in Saratoga, CA? For Machine Learning Engineer Opt jobs in Saratoga, CA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Opt jobs in Saratoga, CA look for? The top searched job categories for Machine Learning Engineer Opt jobs in Saratoga, CA are:
What cities near Saratoga, CA are hiring for Machine Learning Engineer Opt jobs? Cities near Saratoga, CA with the most Machine Learning Engineer Opt job openings:
Infographic showing various Machine Learning Engineer Opt job openings in Saratoga, CA as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $141,990 per year, or $68.3 per hour.

Machine Learning Engineer

Abaka AI

Mountain View, CA โ€ข On-site

Full-time

Re-posted 27 days ago


Job description

Job Summary:
Abaka AI is built on the mission to be the worldโ€™s most trusted data partner for AI companies, supporting global partners with reliable and scalable data solutions. 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 model training strategy and infrastructure.
Responsibilities:
โ€ข Design, build, and optimize scalable machine learning pipelines for multimodal model training, fine-tuning, and evaluation across text, image, audio, video, and 3D data.
โ€ข Work closely with data engineering and research teams to develop efficient data workflows, including collection, preprocessing, annotation, versioning, and model integration.
โ€ข Implement and refine training strategies for large-scale AI systems, including vision, video, and diffusion models, ensuring reproducibility, efficiency, and strong model performance.
โ€ข Develop tools and automation frameworks that accelerate model experimentation, hyperparameter tuning, and deployment.
โ€ข Identify and address performance bottlenecks in data or training pipelines to improve throughput, stability, and resource utilization.
โ€ข Collaborate with product and infrastructure teams to ensure smooth integration of model outputs into both internal and client-facing applications.
โ€ข Support internal best practices for model governance, experiment tracking, and documentation to maintain high engineering standards and reproducibility.
Qualifications:
Required:
โ€ข Strong academic background in computer science, artificial intelligence, machine learning, or related fields.
โ€ข 3+ years of experience in applied machine learning or ML engineering, with a demonstrated ability to deliver production-ready models or pipelines.
โ€ข Proficient in Python and ML frameworks such as PyTorch, TensorFlow, or JAX, with hands-on experience in large-scale distributed training and inference systems.
โ€ข Familiarity with multimodal data processing (e.g., text-image pairing, video understanding, speech-audio modeling) and dataset optimization for model training.
โ€ข Solid understanding of ML system design, including feature pipelines, data loaders, model serving, and evaluation frameworks.
โ€ข Experience with modern infrastructure tools such as Kubernetes, Ray, Airflow, or MLflow, along with cloud-based training environments (AWS, GCP, Azure).
โ€ข Excellent communication and collaboration skills, capable of working effectively across engineering, research, and product teams to accomplish shared goals.
โ€ข Self-driven and adaptable, comfortable operating in a fast-paced startup environment, and able to demonstrate strong ownership and urgency in execution.
Preferred:
โ€ข Masterโ€™s degree or Ph.D. is preferred.
Company:
Abaka AI provides accurate and efficient AI data services, including data collection, data cleaning, data annotation, and OTS datasets. Founded in 2021, the company is headquartered in Palo Alto, USA, with a team of 51-200 employees. The company is currently Growth Stage.