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Computational Finance Jobs in California (NOW HIRING)

Marlabs

Computational Biology MLOps Engineer

San Diego, CA · On-site

$118K - $139K/yr

Since 2000, we have partnered with some of the largest healthcare, life sciences, financial ... Computational Biology MLOps Engineer | About You As a Computational Biology MLOps Engineer, you are ...

NVIDIA Gruppe

Senior Memory Planner

Santa Clara, CA · On-site

$100 - $155.25/hr

Our technology impacts the visual experience in video game development, film production, deep learning, space exploration, medicine, computational finance and automotive design. And we've only ...

Nvidia

Senior Memory Planner

Santa Clara, CA · On-site

Our technology impacts the visual experience in video game development, film production, deep learning, space exploration, medicine, computational finance and automotive design. And we've only ...

Nvidia

Senior Memory Planner

Santa Clara, CA · On-site

Our technology impacts the visual experience in video game development, film production, deep learning, space exploration, medicine, computational finance and automotive design. And we've only ...

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All JobsComputational Finance JobsComputational Finance Jobs in California

Computational Finance information

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$40

$54

$73

How much do computational finance jobs pay per hour?

As of Sep 5, 2026, the average hourly pay for computational finance in California is $54.21, according to ZipRecruiter salary data. Most workers in this role earn between $46.25 and $72.60 per hour, depending on experience, location, and employer.

What is a computational finance?

A Computational Finance job involves using mathematical models, statistical techniques, and computer algorithms to analyze financial markets, assess risks, and develop trading strategies. Professionals in this field work with large datasets, implement quantitative models, and may develop software for pricing derivatives, portfolio optimization, or algorithmic trading. These roles are common in investment banks, hedge funds, asset management firms, and financial technology companies. Strong programming skills (e.g., Python, C++, R) and knowledge of finance and mathematics are essential for success in this career.

What does a computational finance do?

Professionals in Computational Finance often work on developing and implementing quantitative models for trading strategies, risk management, and pricing of complex financial instruments. They may spend their day analyzing market data, running simulations, optimizing algorithms, and collaborating with traders, risk managers, and software developers. Many roles also involve creating tools to automate workflows and improve the accuracy of forecasts. This work environment is highly collaborative and fast-paced, providing opportunities to contribute directly to financial decision-making and innovation within the company. Over time, successful professionals can progress to lead analysis teams or specialize further in areas such as algorithmic trading or financial engineering.

What are the key skills and qualifications needed to thrive in computational finance?

To thrive in Computational Finance, you need strong quantitative and analytical skills, expertise in financial theory, and typically a background in mathematics, statistics, computer science, or finance. Familiarity with programming languages such as Python, R, or MATLAB, experience with statistical modeling software, and knowledge of financial databases or certifications like CFA are highly valuable. Attention to detail, problem-solving ability, and effective communication make individuals stand out in this field. These skills are crucial for designing complex financial models, interpreting large data sets, and delivering actionable insights to support investment decisions.

What can you do with a computational finance degree?

A computational finance degree prepares individuals for roles such as quantitative analyst, risk manager, or financial engineer, involving tasks like developing models, analyzing financial data, and implementing algorithms. Graduates often work with programming languages like Python, C++, or R and may pursue certifications such as CFA or FRM. The degree provides skills applicable in investment banks, hedge funds, asset management, and financial technology firms.

What are popular job titles related to Computational Finance jobs in California?

For Computational Finance jobs in California, the most frequently searched job titles are:

What job categories do people searching Computational Finance jobs in California look for?

The top searched job categories for Computational Finance jobs in California are:

Computational Finance jobs near you
Infographic showing various Computational Finance job openings in California as of August 2026, with employment types broken down into 91% Full Time, 7% Part Time, and 2% Contract. Highlights an 83% Physical, 6% Hybrid, and 11% Remote job distribution, with an average salary of $112,753 per year, or $54.2 per hour.

Computational Biologist MLOps Engineer

Marlabs

San Diego, CA • On-site

Other

Re-posted 13 days ago

Job description

Marlabs, a global AI and Digital Solutions Consulting firm, delivers intelligent solutions across AI, data, analytics, and product engineering. Since 2000, we have partnered with some of the largest healthcare, life sciences, financial services, and government organizations worldwide. As we continue to expand our global footprint, we have an exciting opportunity for a highly skilled Computational Biology MLOps Engineer to join our innovative and dynamic team.


This role requires onsite work three (3) days per week in Indianapolis, IN or San Diego, CA.


Computational Biology MLOps Engineer | About You

As a Computational Biology MLOps Engineer, you are responsible for building and scaling the ML infrastructure that supports next generation in silico protein design and engineering. You bridge cutting edge AI research and production systems at the intersection of machine learning, computational biology, and high performance computing. You thrive in cross functional environments and partner closely with computational scientists and platform engineers to accelerate research velocity. You bring strong software, DevOps, and data engineering fundamentals with hands on experience across CI/CD, orchestration, and distributed training. Experience working with scientific or multimodal data and interest in protein language and generative models is a plus.


Computational Biology MLOps Engineer | Day-to-Day

  • Build and maintain ML infrastructure, including CI/CD pipelines (GitHub Actions) for model training, evaluation, and deployment.
  • Orchestrate compute across Kubernetes clusters and SLURM and HPC environments to optimize utilization for large scale training.
  • Develop robust and scalable data pipelines that deliver ML ready datasets from biological sources such as PDB and mmCIF files, sequence databases, and assay readouts.
  • Create tools and frameworks that enable rapid iteration on protein language models, diffusion models, and other generative approaches.
  • Architect systems that scale across distributed environments and support multimodal datasets for large foundational models.
  • Implement monitoring, logging, and alerting to ensure reliability, performance, and cost efficiency of production ML systems.


Computational Biology MLOps Engineer | Skills & Experience

  • 5+ years of overall industry experience in software engineering, DevOps, data engineering, or ML engineering roles, including 3+ years of focused MLOps experience building and maintaining production grade ML infrastructure.
  • Proven CI/CD expertise with GitHub Actions and strong DevOps practices including infrastructure as code, version control, and collaborative workflows.
  • Hands on Kubernetes experience in deploying and managing containerized ML workloads with familiarity using container registries.
  • Proficiency with SLURM or similar job schedulers in HPC environments and experience with distributed training optimization including mixed precision and checkpointing.
  • Strong Python skills and experience with major ML frameworks including PyTorch, TensorFlow, or JAX.
  • Experience building ETL processes and scalable data and feature pipelines and experience with cloud platforms such as AWS, GCP, or Azure.
  • Preferred experience with scientific data, protein structure formats such as PDB and mmCIF, protein AI models including ESM, and agentic systems such as MCP, LangGraph, and LangChain.

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