Tranzeal Incorporated is seeking talented Machine Learning Engineers to join Prescient Design, a division focused on molecular design methods. The successful candidate will manage projects deploying ...
Tranzeal Incorporated is seeking talented Machine Learning Engineers to join Prescient Design, a division focused on molecular design methods. The successful candidate will manage projects deploying ...
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Quick apply
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Machine Learning Engineer Opt information
See San Ramon, CA salary details
$35.2K - $51.7K
1% of jobs
$51.7K - $68.1K
1% of jobs
$68.1K - $84.6K
5% of jobs
$84.6K - $101K
6% of jobs
$114.6K is the 25th percentile. Wages below this are outliers.
$101K - $117.5K
14% of jobs
$117.5K - $133.9K
14% of jobs
The median wage is $142.2K / yr.
$133.9K - $150.4K
18% of jobs
$150.4K - $166.9K
14% of jobs
$170.2K is the 75th percentile. Wages above this are outliers.
$166.9K - $183.3K
12% of jobs
$183.3K - $199.8K
11% of jobs
$199.8K - $216.2K
5% of jobs
$35.2K
$143.9K
$216.2K
How much do machine learning engineer opt jobs pay per year?
As of Sep 9, 2026, the average yearly pay for machine learning engineer opt in San Ramon, CA is $143,902.00, according to ZipRecruiter salary data. Most workers in this role earn between $113,400.00 and $173,200.00 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 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 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 is the difference between Machine Learning Engineer Opt vs Data Scientist?
| Aspect | Machine Learning Engineer Opt | Data Scientist |
|---|---|---|
| Required Credentials | Bachelor's or Master's in CS, AI, or related fields; certifications in ML tools | Bachelor's or Master's in CS, Statistics, or related fields; data analysis certifications |
| Work Environment | Develops, tests, and deploys ML models in production systems | Analyzes data, builds models, and provides insights for decision-making |
| Employer & Industry Usage | Tech companies, AI startups, e-commerce, finance | Research institutions, tech firms, consulting, finance |
| Common Search & Comparison | Often compared for technical skills and deployment focus | Compared 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 popular job titles related to Machine Learning Engineer Opt jobs in San Ramon, CA?
For Machine Learning Engineer Opt jobs in San Ramon, CA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Opt jobs in San Ramon, CA look for?
The top searched job categories for Machine Learning Engineer Opt jobs in San Ramon, CA are:
What cities near San Ramon, CA are hiring for Machine Learning Engineer Opt jobs?
Cities near San Ramon, CA with the most Machine Learning Engineer Opt job openings:
Machine Learning Engineer
South San Francisco, CA • On-site
Tranzeal Incorporated •
Business Management Consulting • 51 - 200 employees51 - 200 employees
Full-time
Re-posted 2 days ago
Job description
Job Summary:
Tranzeal Incorporated is seeking talented Machine Learning Engineers to join Prescient Design, a division focused on molecular design methods. The successful candidate will manage projects deploying machine learning techniques for drug design and optimization.
Responsibilities:
• You will join Prescient Design within the Computational Sciences organization in gRED.
• Your peers will be machine learning scientists, engineers, computational chemists, and computational biologists.
• You will closely collaborate with scientists within Prescient and across gRED.
• You will develop machine learning and Bayesian optimization workflows to analyze existing, and design new, small and large molecules.
• You will be expected to form close working relationships with small molecule and protein therapeutic development efforts across the gRED organization.
• You will be expected to work on existing projects and generate new project ideas.
Qualifications:
Required:
• PhD degree in a quantitative field (e.g., Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics), or MS degree and 3+ years of industry experience.
• Demonstrated experience with machine learning libraries in production-ready workflows (e.g., PyTorch + Lightning + Weights and Biases)
• Record of achievement, including at least one high-impact first author publication or equivalent.
• Excellent written, visual, and oral communication and collaboration skills.
Preferred:
• Experience with physical modeling methods (e.g., molecular dynamics) and cheminformatics toolkits (e.g., rdkit)
• Previous focus on one or more of these areas: molecular property prediction, computational chemistry, de novo drug design, medicinal chemistry, small molecule design, self-supervised learning, geometric deep learning, Bayesian optimization, probabilistic modeling, statistical methods.
• Public portfolio of computational projects (available on e.g. GitHub)
Company:
Tranzeal is an industry leading global Business Transformation Service Provider. Founded in , the company is headquartered in San Jose, USA, with a team of 201-500 employees. The company is currently Growth Stage.
Tranzeal Incorporated is seeking talented Machine Learning Engineers to join Prescient Design, a division focused on molecular design methods. The successful candidate will manage projects deploying machine learning techniques for drug design and optimization.
Responsibilities:
• You will join Prescient Design within the Computational Sciences organization in gRED.
• Your peers will be machine learning scientists, engineers, computational chemists, and computational biologists.
• You will closely collaborate with scientists within Prescient and across gRED.
• You will develop machine learning and Bayesian optimization workflows to analyze existing, and design new, small and large molecules.
• You will be expected to form close working relationships with small molecule and protein therapeutic development efforts across the gRED organization.
• You will be expected to work on existing projects and generate new project ideas.
Qualifications:
Required:
• PhD degree in a quantitative field (e.g., Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics), or MS degree and 3+ years of industry experience.
• Demonstrated experience with machine learning libraries in production-ready workflows (e.g., PyTorch + Lightning + Weights and Biases)
• Record of achievement, including at least one high-impact first author publication or equivalent.
• Excellent written, visual, and oral communication and collaboration skills.
Preferred:
• Experience with physical modeling methods (e.g., molecular dynamics) and cheminformatics toolkits (e.g., rdkit)
• Previous focus on one or more of these areas: molecular property prediction, computational chemistry, de novo drug design, medicinal chemistry, small molecule design, self-supervised learning, geometric deep learning, Bayesian optimization, probabilistic modeling, statistical methods.
• Public portfolio of computational projects (available on e.g. GitHub)
Company:
Tranzeal is an industry leading global Business Transformation Service Provider. Founded in , the company is headquartered in San Jose, USA, with a team of 201-500 employees. The company is currently Growth Stage.
About Tranzeal
Sourced by ZipRecruiter
Tranzeal is an industry leading global Business Transformation Service Provider. We offer specific consulting services as well as pre-packaged, industry specific solutions and services to companies around the world. Since our foundation, Tranzeal has evolved from a small start up company to a mid market player dedicated to providing solutions and services to SMB and large enterprise customers. Our Consulting Services are dedicated to helping our Clients maximize their investments in IT and the overall effectiveness and efficiency of the business. Tranzeal has built its center of competency in Enterprise Resource Planning, Business Intelligence, Supply Chain Management, Customer Resource Management and Information Integration solutions, as well as specific Service orientated offerings such as Test, Quality Assurance and Data Management.
Industry
Business management consulting
Company size
51 - 200 Employees
Headquarters location
San Jose, CA, US