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Machine Learning Engineer Jobs in Berkeley, CA (NOW HIRING)

S27a

Machine Learning Engineer

San Francisco, CA · On-site

$140 - $210/hr

Maintain, monitor, and enhance deployed machine learning systems to ensure continuous improvement. * Collaborate with software engineers, data scientists, and product teams to integrate AI solutions.

New

Blank Bio

Machine Learning Engineer

San Francisco, CA · On-site

The Machine Learning Engineer will be responsible for scaling models, building training infrastructure, and ensuring reproducibility across large-scale biological datasets while collaborating with ...

Jobzhr

Machine Learning Engineer

Pleasanton, CA · On-site

$110 - $150/hr

... with machine learning frameworks such as TensorFlow, Keras, and PyTorch. Knowledge of cloud platforms and technologies, specifically Microsoft Azure, is crucial. Experience in DevOps and MLOps ...

Gotion, Inc.

Machine Learning Engineer

Fremont, CA · On-site

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 ...

OpenReq

Machine Learning Engineer

San Francisco, CA · On-site

As a Machine Learning Engineer, you will shape the technical direction of the company by automating the ML life-cycle and engaging directly with customers while contributing to the architectural ...

Reducto

Machine Learning Engineer

San Francisco, CA · On-site

They are seeking a Machine Learning Engineer to train and deploy critical models for their core product, focusing on interpreting unstructured data and improving model performance. Responsibilities ...

Menlo Ventures

Machine Learning Engineer

San Mateo, CA · On-site

$195 - $350/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

We're hiring a Machine Learning Engineer as the volume and complexity of legal AI workflows in our system scale rapidly. As more firms rely on Eve to automate high‑stakes legal work -- from intake ...

Superhuman

Machine Learning Engineer

San Francisco, CA · On-site

$180 - $260/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

The Opportunity We are building a platform for AI Agents to come together and solve arbitrarily complex tasks, leveraging Superhuman ubiquitous UI. As a Machine Learning Engineer on this team, you ...

Jobzhr

Machine Learning Engineer, Drive

San Francisco, CA · On-site

$204 - $299/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

About The Role As a Machine Learning Engineer on the Drive team, you'll own machine learning systems end-to-end--from feature engineering and model development to experimentation, deployment ...

Canals

Machine Learning Engineer

San Francisco, CA · Remote

We're looking for a senior-level Machine Learning Engineer who can move quickly while maintaining high quality, owning end-to-end ML pipelines while shaping product features that deliver real-world ...

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Showing results 41-60

All JobsMachine Learning Engineer JobsMachine Learning Engineer Jobs in Berkeley, CA

Machine Learning Engineer information

See Berkeley, CA salary details

$38.6K

$157.7K

$236.9K

How much do machine learning engineer jobs pay per year?

As of Aug 16, 2026, the average yearly pay for machine learning engineer in Berkeley, CA is $157,670.00, according to ZipRecruiter salary data. Most workers in this role earn between $124,300.00 and $189,800.00 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 and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

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 strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What does a machine learning engineer do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are some common challenges faced by machine learning engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

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

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

More about Machine Learning Engineer jobs

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For Machine Learning Engineer jobs in Berkeley, CA, the most frequently searched job titles are:

What job categories do people searching Machine Learning Engineer jobs in Berkeley, CA look for?

The top searched job categories for Machine Learning Engineer jobs in Berkeley, CA are:

What cities near Berkeley, CA are hiring for Machine Learning Engineer jobs?

Cities near Berkeley, CA with the most Machine Learning Engineer job openings:

Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in Berkeley, CA as of August 2026, with employment types broken down into 78% Full Time, and 22% Contract. Highlights an 100% In-person job distribution, with an average salary of $157,670 per year, or $75.8 per hour.

Machine Learning Engineer

S27a

San Francisco, CA • On-site

$140 - $210/hr

Other

Posted yesterday

New

Job description

Responsible for developing next-generation AI systems designed to simplify task automation for users. This role involves designing, evaluating, deploying, and maintaining AI solutions, utilizing both Large Language Models (LLMs) and Bardeen's custom models in areas such as semantic parsing, dialog systems, agents, and text generation. The position collaborates with engineers to integrate AI features into Bardeen's products, ensuring a high-quality user experience.

Specific duties include:

  • Research, design, and implement machine learning algorithms to optimize workflow automation.

  • Develop, test, and modify computer programs to apply machine learning models to real-world applications.

  • Research, design, and implement machine learning and AI algorithms to model real world processes, including process discovery, process conformance, and opportunity identification for automation and AI agents.

  • Develop, test, and modify computer programs that apply machine learning models to operational data sources such as event logs, clickstreams, tickets, documents, and call transcripts.

  • Design and improve methods for process and entity extraction from unstructured and semi structured data, including tasks, systems, stakeholders, and key business objects.

  • Stay familiar with and evaluate state of the art research in process mining, workflow intelligence, representation learning for events and processes, and LLM based planning and tool use, and translate it into practical enterprise solutions.

  • Perform statistical analysis and apply data mining techniques to diagnose bottlenecks, measure impact, and improve model performance and robustness in production settings.

  • Deploy machine learning models into production systems, ensuring scalability and efficiency.

  • Maintain, monitor, and enhance deployed machine learning systems to ensure continuous improvement.

  • Collaborate with software engineers, data scientists, and product teams to integrate AI solutions.

  • Prepare technical documentation and reports detailing methodologies and outcomes.

  • Utilize cloud computing platforms such as AWS and GCP to manage large-scale data processing and storage.

  • Ensure compliance with industry standards, data governance, and security protocols for machine learning applications.

Job Requirements:

Requires a Master's degree in Computational Science and Engineering, or a closely related field that focuses on Machine Learning, and 1 year of experience.

Experience must include:

  • Experience with modern deep learning models, particularly large language models (LLMs) and multimodal architectures used for understanding text, structured data, and behavioral traces.

  • Familiarity with OpenAI, Anthropic, or Hugging Face Transformers (GPT, Mistral, LLaMA, etc.).

  • Experience with Python, Hugging Face, and OpenAI, Gemini and Anthropic SDKs.

  • Experience with designing evaluation frameworks, benchmarking model variants, and measuring before/after impact.

  • Experience with production-grade data and inference infrastructure, including AWS and GCP.

  • Experience with monitoring, optimization, and scaling of LLM inference workloads across distributed systems.

  • Experience with ML and AI algorithms to model real world business processes and identification of high impact automation and AI agent opportunities.

  • Experience with using LLMs for performing statistical analysis.

Remote work is permitted. Travel is required to unanticipated locations nationwide. Travel is less than 5% of time.

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