ZipRecruiter

Log In

1

Machine Learning Engineer Biotech Jobs in Berkeley, CA

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.

Capital One

Lead Machine Learning Engineer

San Francisco, CA · On-site +1

$120K - $159K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

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 ...

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 ...

Zoomcar

Machine Learning Engineer

San Francisco, CA · On-site

$150 - $190/hr

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 ...

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 ...

Superhuman

Machine Learning Engineer

San Francisco, CA · On-site

$180 - $260/hr

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 ...

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 ...

previous page

Showing results 41-60

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

Machine Learning Engineer Biotech information

See Berkeley, CA salary details

$38.6K

$157.7K

$236.9K

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

As of Sep 6, 2026, the average yearly pay for machine learning engineer biotech 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 does a machine learning engineer do in biotech?

A Machine Learning Engineer in biotech applies advanced algorithms and data analysis techniques to solve biological and medical problems. They work with large datasets such as genomic sequences, medical images, or clinical records to develop predictive models, automate data analysis, and uncover insights that can accelerate drug discovery, diagnostics, and personalized medicine. Their work often involves close collaboration with biologists, data scientists, and software engineers to create tools and solutions that improve healthcare outcomes. Machine Learning Engineers in this field need a strong background in both computational methods and biological sciences.

How do machine learning engineers in biotech typically collaborate with research scientists and domain experts?

Machine Learning Engineers in biotech often work closely with research scientists and domain experts to translate complex biological problems into data-driven solutions. This collaboration involves regular meetings to understand experimental data, refine project goals, and iterate on model development based on domain feedback. Engineers are expected to communicate technical concepts clearly, adapt models to fit scientific needs, and help validate results alongside laboratory teams. This interdisciplinary environment fosters innovation but also requires flexibility and strong communication skills.

What are the key skills and qualifications needed to thrive as a machine learning engineer in biotech?

To thrive as a Machine Learning Engineer in Biotech, you need a solid background in computer science, statistics, and biology, often with an advanced degree in a related field. Experience with programming languages such as Python or R, machine learning frameworks like TensorFlow or PyTorch, and familiarity with bioinformatics tools are typically required. Strong problem-solving, communication, and interdisciplinary collaboration skills set standout candidates apart. These capabilities are crucial for developing effective models that drive scientific innovation and advance biotechnological research.

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

AspectMachine Learning Engineer BiotechData Scientist Biotech
Required CredentialsBachelor's or Master's in Computer Science, Data Science, or related; knowledge of ML frameworksBachelor's or Master's in Data Science, Statistics, or related; strong analytical skills
Work EnvironmentDevelops ML models, coding, deploying algorithms in biotech R&DAnalyzes biological data, interprets results, creates reports
Employer & Industry UsageBiotech firms, pharma companies, research labsBiotech companies, healthcare, research institutions

While both roles work with biological data, Machine Learning Engineers focus on developing and deploying ML algorithms, whereas Data Scientists analyze and interpret biological datasets to inform research and decision-making in biotech settings.

What are popular job titles related to Machine Learning Engineer Biotech jobs in Berkeley, CA?

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

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

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

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

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

Machine Learning Engineer Biotech jobs near you
Infographic showing various Machine Learning Engineer Biotech job openings in Berkeley, CA as of August 2026, with employment types broken down into 1% As Needed, 70% Full Time, 28% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote 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 21 days ago

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.

#J-18808-Ljbffr

Apply