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Applied Math Jobs in Berkeley, CA (NOW HIRING)

Apex Informatics

Data Engineer

Pleasanton, CA · On-site

$127K - $152K/yr

Bachelor's degree or equivalent experience in computer science, applied math, physics, engineering, statistics, economics or related field. 3+ years of industry experience in Data Engineering 3+ ...

Hop

Applied Data Scientist

San Francisco, CA · On-site

Required : • BS/MS in Statistics, Computer Science, Applied Mathematics, or a quantitative field. • 3-5 years of applied data science; minimum 2 years working with NLP or large-scale text data in ...

Hop

Applied Data Scientist

San Francisco, CA · On-site

Required : • BS/MS in Statistics, Computer Science, Applied Mathematics, or a quantitative field. • 3-5 years of applied data science; minimum 2 years working with NLP or large-scale text data in ...

Capital One

Applied Researcher II

San Francisco, CA

S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications * PhD in Computer ...

Capital One

Applied Researcher II

San Francisco, CA · On-site

S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...

Capital One

Applied Researcher II

San Francisco, CA

S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...

Capital One

Applied Researcher II

San Francisco, CA · On-site

S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...

Capital One

Applied Researcher I

San Francisco, CA · On-site

S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...

Capital One

Applied Researcher I

San Francisco, CA · On-site

S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research Preferred Qualifications [choose correct set ...

Hop

Applied Data Scientist

San Francisco, CA

What we require BS/MS in Statistics, Computer Science, Applied Mathematics, or a quantitative field. 3-5 years of applied data science; minimum 2 years working with NLP or large-scale text data in ...

Capital One

Applied Researcher I

San Francisco, CA · On-site

PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields * LLM * PhD focus on NLP or Masters with 5 years of industrial NLP ...

Capital One

Applied Researcher I

San Francisco, CA · On-site

S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...

Hop

Applied Data Scientist

San Francisco, CA

What we require * BS/MS in Statistics, Computer Science, Applied Mathematics, or a quantitative field. * 3-5 years of applied data science; minimum 2 years working with NLP or large-scale text data ...

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Showing results 1-20

All JobsApplied Math JobsApplied Math Jobs in Berkeley, CA

Applied Math information

See Berkeley, CA salary details

$27.6K

$72K

$115.7K

How much do applied math jobs pay per year?

As of Aug 4, 2026, the average yearly pay for applied math in Berkeley, CA is $72,043.00, according to ZipRecruiter salary data. Most workers in this role earn between $55,100.00 and $85,700.00 per year, depending on experience, location, and employer.

Is applied math a useful degree?

Applied math is a useful degree for careers in data analysis, finance, engineering, and research, as it develops skills in problem-solving, modeling, and quantitative analysis. Graduates often find employment in industries that rely on mathematical and computational tools, and the degree can lead to roles requiring programming and statistical knowledge.

What jobs can you get with applied math?

Applied math graduates can pursue careers such as data analyst, operations researcher, financial analyst, actuary, or software developer. These roles often require strong analytical skills, proficiency in programming languages like Python or R, and knowledge of statistical and mathematical modeling. Many positions are found in finance, technology, healthcare, and government sectors.

What is an applied mathematician?

Applied mathematicians are professionals who use mathematical theories, techniques, and computational methods to solve practical problems in fields such as engineering, science, business, and industry. They often develop models to analyze real-world phenomena, optimize processes, and predict outcomes. Applied mathematicians may work in diverse areas like data analysis, operations research, finance, and computer science, collaborating with experts from other disciplines to address complex challenges.

Is applied math in demand?

Applied math professionals are in high demand across industries such as finance, data analysis, engineering, and technology due to their skills in modeling, problem-solving, and quantitative analysis. Employers seek candidates with strong analytical abilities and proficiency in tools like MATLAB, Python, or R, making applied math a valuable and often well-compensated field.

What is the difference between Applied Math vs Data Analyst?

AspectApplied MathData Analyst
Required CredentialsBachelor's or higher in Mathematics, Applied Math, or related fieldsBachelor's or higher in Statistics, Data Science, or related fields
Work EnvironmentResearch labs, academia, finance, engineeringBusiness, finance, healthcare, marketing
Industry UsageModeling, simulations, algorithm developmentData interpretation, reporting, visualization
Common Search/ComparisonApplied Math vs Data Analyst

Applied Math and Data Analysts often share skills in statistical analysis and problem-solving. However, Applied Math focuses more on developing mathematical models and algorithms, while Data Analysts primarily interpret and visualize data to inform business decisions. Both roles are vital across industries, but their daily tasks and focus areas differ significantly.

What careers use applied math?

Applied math is used in careers such as data analyst, financial analyst, operations researcher, actuary, engineer, and computer scientist. These roles involve using mathematical models, statistical techniques, and computational tools to solve real-world problems across industries like finance, technology, healthcare, and engineering.

What are some typical projects or problems an applied mathematician may work on within a multidisciplinary team?

Applied mathematicians often collaborate with experts from fields such as engineering, computer science, and finance to tackle real-world challenges. For example, they might develop algorithms for optimizing logistics and supply chains, create mathematical models to predict disease spread in healthcare, or analyze large data sets to inform business strategies. This collaboration typically involves regular meetings, data sharing, and iterative problem solving, making strong communication skills and adaptability essential for success in the role.

What are the key skills and qualifications needed to thrive as an applied mathematician, and why are they important?

To thrive as an Applied Mathematician, you need strong mathematical modeling, analytical, and problem-solving skills, usually supported by a degree in mathematics, applied mathematics, or a related field. Familiarity with programming languages (such as Python, MATLAB, or R), statistical software, and computational tools is typically required. Excellent communication, teamwork, and critical thinking abilities help translate complex mathematical concepts for diverse audiences and collaborative projects. These skills are vital for developing solutions to real-world problems across industries, ensuring accuracy, innovation, and practical impact.
What are popular job titles related to Applied Math jobs in Berkeley, CA? For Applied Math jobs in Berkeley, CA, the most frequently searched job titles are:
What job categories do people searching Applied Math jobs in Berkeley, CA look for? The top searched job categories for Applied Math jobs in Berkeley, CA are:
What cities near Berkeley, CA are hiring for Applied Math jobs? Cities near Berkeley, CA with the most Applied Math job openings:
Applied Math jobs near you
Infographic showing various Applied Math job openings in Berkeley, CA as of July 2026, with employment types broken down into 75% Full Time, 24% Part Time, and 1% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $72,043 per year, or $34.6 per hour.

Senior/Principal Forward Deployed Engineer - Applied AI/ML

Luminary Cloud

San Mateo, CA • On-site

$142K - $197K/yr

Full-time

Posted 28 days ago

Job description

Full-time position | San Mateo, CA (Onsite)
JOIN THE REVOLUTION IN ENGINEERING INNOVATION
Luminary helps engineering companies be more competitive by getting to market faster, creating better products, and reducing development risk. We do this through our Physics AI platform - the fastest and easiest way to build and deploy models that understand and instantly predict physical reality with precision. Our customers span industries from automotive and aerospace to defense, industrial, semiconductors, and energy - ranging from hyper-growth startups to Fortune 100 enterprises. Luminary is a Series B company headquartered in San Mateo, California.
YOUR IMPACT
As a Senior/Principal Applied AI/ML Scientist on Luminary's Applied AI/ML team, you build the Physics AI models that power customer outcomes. You work in a matrix structure inside customer value delivery teams alongside a Lead Delivery Engineer, Applications Engineers, and Data & Platform engineers. You take real customer engineering problems, design and train the right model architectures, and partner with the team to deploy those models into production engineering workflows. You operate at the boundary of cutting-edge research and applied delivery - staying connected to the frontier of physics-informed ML while making sure your work ships and gets used.
WHAT YOU'LL DO
  • Own model development for Physics AI engagements: architecture selection, training pipeline design, hyperparameter tuning, evaluation, and validation against ground-truth simulation.
  • Work with Applications Engineers to ensure training data is physically meaningful and adequate for the target use case.
  • Partner with Data & Platform engineers to operationalize training pipelines, model registries, and inference serving.
  • Collaborate with Luminary Research to apply state-of-the-art techniques - neural operators, diffusion models, geometric deep learning, latent representations - to real customer problems.
  • Embrace co-engineering: work side-by-side with customer data scientists and engineers, sharing methodology and building model literacy on the customer side.
  • Bring back signal from delivery into Research and Product, helping shape the next generation of Luminary's Physics AI methods and platform.
  • Mentor junior team members and contribute to internal best practices for applied physics-informed ML.

WHAT YOU BRING
  • 5-10 years of experience in applied machine learning, with significant exposure to scientific computing, engineering simulation, or physics-informed ML. Principal-level candidates trend toward the upper end of the range.
  • Strong proficiency in Python and PyTorch (or equivalent deep learning framework). You write production-quality ML code, not just research notebooks.
  • Hands-on experience training and deploying models on engineering or scientific data - surrogate models, neural operators, graph neural networks, diffusion models, or related architectures.
  • Working knowledge of engineering simulation: CFD, FEA, EM, thermal, or related - enough to collaborate effectively with domain experts and understand what the model needs to learn.
  • Experience with distributed training, GPU workloads, and modern ML infrastructure (experiment tracking, model registries, inference serving).
  • Strong scientific mindset: rigorous experimentation, careful evaluation, honest reporting of what works and what does not.
  • Customer-facing presence; comfortable explaining model architectures and limitations to engineering audiences.
  • Self-starter mentality, persistent through iteration, willing to travel occasionally to customer sites.

PREFERRED QUALIFICATIONS
  • Advanced degree (MS or PhD) in Computer Science, Applied Math, Physics, Engineering, or related quantitative discipline.
  • Published work in physics-informed ML, neural operators, scientific machine learning, or related fields.
  • Experience with physics-informed AI/ML frameworks (e.g. PhysicsNeMo, JAX-based scientific ML stacks) or foundation model fine-tuning pipelines for scientific data.
  • Prior experience in applied research roles at engineering, simulation, or scientific computing companies.
  • Track record of shipping models into production engineering workflows.

Apply