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Algorithmic Trader Jobs in California (NOW HIRING)

Databento

Software Engineer (C++/Java)

San Francisco, CA · On-site +1

An interest in financial data or algorithmic trading. Notice about phishing scams Be cautious of phishing scams impersonating Databento that offer fake job interviews and request purchases. Official ...

Databento

Software Engineer (C++/C#)

San Francisco, CA · On-site +1

An interest in financial data or algorithmic trading. Notice about phishing scams Be cautious of phishing scams impersonating Databento that offer fake job interviews and request purchases. Official ...

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All JobsAlgorithmic Trader JobsAlgorithmic Trader Jobs in California

Algorithmic Trader information

See California salary details

$39K

$95.5K

$266K

How much do algorithmic trader jobs pay per year?

As of Aug 14, 2026, the average yearly pay for algorithmic trader in California is $95,507.00, according to ZipRecruiter salary data. Most workers in this role earn between $55,800.00 and $104,100.00 per year, depending on experience, location, and employer.

How do you become an algorithmic trader?

To become an algorithmic trader, you typically need a strong background in mathematics, programming, and finance, often holding a degree in a related field such as computer science, engineering, or finance. Developing skills in programming languages like Python or C++, understanding trading strategies, and gaining experience with trading platforms and data analysis are essential. Many algorithmic traders also pursue certifications or advanced degrees to enhance their knowledge and credibility.

What are the key skills and qualifications needed to thrive as an algorithmic trader?

To thrive as an Algorithmic Trader, you need a strong background in mathematics, statistics, programming (often Python, C++, or R), and a solid understanding of financial markets, usually supported by a relevant degree. Familiarity with trading platforms, backtesting frameworks, and data analysis tools, plus certifications like CFA or FRM, are commonly required. Strong analytical thinking, attention to detail, and the ability to work under pressure are vital soft skills for success in this fast-paced environment. These skills enable traders to develop effective, automated strategies that capitalize on market opportunities while managing risk efficiently.

What are some typical challenges faced by algorithmic traders in their day-to-day work?

Algorithmic traders often encounter challenges such as maintaining and updating trading algorithms to adapt to rapidly changing market conditions, ensuring low-latency execution, and managing the risks associated with automated trading. Collaborating closely with quantitative analysts, software engineers, and risk managers is essential to refine strategies and troubleshoot technical issues. Staying compliant with regulatory requirements and monitoring for unexpected market behaviors or system errors are also important aspects of the role.

What is an algorithmic trader?

An algorithmic trader is a financial professional who uses computer algorithms to automate trading strategies in financial markets. These traders develop, test, and implement mathematical models that analyze market data and execute trades at speeds and frequencies impossible for humans. Algorithmic traders work in environments such as investment banks, hedge funds, and proprietary trading firms, and their goal is often to maximize returns while managing risk. They typically need strong skills in programming, quantitative analysis, and finance.

What is the difference between Algorithmic Trader vs Quantitative Analyst?

AspectAlgorithmic TraderQuantitative Analyst
Required CredentialsDegree in finance, computer science, or related field; programming skillsDegree in mathematics, statistics, or finance; strong analytical skills
Work EnvironmentTrading firms, hedge funds, financial institutions; fast-pacedFinancial institutions, research firms; analytical and research-focused
Employer & Industry UsageUsed primarily in trading and investment firmsUsed in asset management, hedge funds, and investment banks

While both roles require strong quantitative skills and programming knowledge, Algorithmic Traders focus on developing and executing trading algorithms in real-time markets, whereas Quantitative Analysts primarily develop models and strategies for investment decision-making. The roles often overlap but differ mainly in their focus on trading execution versus model development.

What are the most commonly searched types of Algorithmic Trader jobs in California?

The most popular types of Algorithmic Trader jobs in California are:

What are popular job titles related to Algorithmic Trader jobs in California?

For Algorithmic Trader jobs in California, the most frequently searched job titles are:

What job categories do people searching Algorithmic Trader jobs in California look for?

The top searched job categories for Algorithmic Trader jobs in California are:

What cities in California are hiring for Algorithmic Trader jobs?

Cities in California with the most Algorithmic Trader job openings:

Algorithmic Trader jobs near you
Infographic showing various Algorithmic Trader job openings in California as of August 2026, with employment types broken down into 90% Full Time, and 10% Contract. Highlights an 90% In-person, and 10% Remote job distribution, with an average salary of $95,507 per year, or $45.9 per hour.

Machine Learning Engineer

Framework Ventures

San Francisco, CA • On-site

$120 - $160/hr

Other

Posted 8 days ago

Job description

Andalusia Labs is building foundational economic infrastructure for programmable global markets, connecting capital, computation, and coordination across the internet. Our work sits at the intersection of distributed systems, finance, and machine intelligence, with the goal of growing the world’s programmable GDP.

Our team has shipped massively scalable systems and products at Coinbase, Google, AWS, Microsoft, X, TikTok, Goldman Sachs, and High-Frequency Trading firms. We are backed by Coinbase, Mubadala, Lightspeed, Bain Capital, Pantera, Framework, Digital Currency Group, Proof Group, Nima Capital, Naval Ravikant, Arthur Hayes, and founders, GPs, and executives from organizations like Founders Fund, Google, and Coinbase.

Role

We are looking for a talented and driven Machine Learning Engineer who is passionate about building innovative products from 0 to Production. As a Machine Learning Engineer, you will work on a variety of projects related to applied machine learning. You will work closely with the founders, engineers, and other cross‑functional partners and bring new products and business lines to market. This is an amazing opportunity offering you the ability to learn new technical skills in blockchain and work with an amazing team of engineers.

Responsibilities
  • Work closely with the founders, engineers, and other cross‑functional partners to rapidly iterate, experiment, and launch products
  • Improve and optimize LLMs for use in production systems
  • Design and implement scalable data and machine learning pipelines
  • Build best‑in‑class AI chatbots that guide users through their Karak journey by translating research papers, blogs, and technical documentation into more accessible content
  • Participate in discussions from the initial product ideas to launch
  • Understand, build, and help optimize financial algorithms
Requirements
  • BA/BS in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field, or equivalent practical experience
  • 5+ years of systems programming experience working with at least one of these languages (Python, Scala, Java)
  • Experience building machine learning models with ML frameworks such as Tensorflow, PyTorch, and other open‑source frameworks
  • Experience manipulating and optimizing large amounts of structured and unstructured data through pipeline development tools
  • Familiarity with modern software development practices, including version control (Git), continuous integration, and automated testing as applied to Rust, Go, and/or Solidity stacks
  • Highly autonomous, ability to design and develop software with minimal guidance
  • Ability to work in a fast‑paced environment and across the product engineering stack
  • Clear written and verbal communication
Bonus
  • Experience building or working on open‑source ML projects
  • Experience building on EVM, Solana, or Cosmos
  • Experience in algorithmic trading or understanding of traditional finance primitives
  • Founded a company
  • Experience working with startups
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