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Algorithmic Trading Jobs in California (NOW HIRING)

Amat

AI Algorithm Developer

Santa Clara, CA

$161K - $221K/yr

Document design decisions, trade-offs, and algorithmic approaches clearly * * Build surrogate models and active learning frameworks for sparse, noisy manufacturing data * Create novel algorithms that ...

Applied Materials

AI Algorithm Developer

Santa Clara, CA · On-site

$161K - $221K/yr

Document design decisions, trade-offs, and algorithmic approaches clearly * Build surrogate models and active learning frameworks for sparse, noisy manufacturing data * Create novel algorithms that ...

Unconventional AI

AI Systems, Training

Palo Alto, CA · On-site

$123K - $168K/yr

Design rigorous benchmarking suites to track Model Flops Utilization (MFU), memory bandwidth, and convergence stability. • Act as a translator, discussing algorithmic trade-offs with theorists and ...

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Algorithmic Trading information

See California salary details

$73.5K

$84.6K

$92.8K

How much do algorithmic trading jobs pay per year?

As of Jun 9, 2026, the average yearly pay for algorithmic trading in California is $84,627.00, according to ZipRecruiter salary data. Most workers in this role earn between $79,900.00 and $89,800.00 per year, depending on experience, location, and employer.

What Is Algorithmic Trading?

Algorithmic trading involves trading in equities, currencies, or other financial instruments using computer programs. A trading program uses an algorithm to calculate current market conditions. This trading method is automated, so the program buys or sells the financial instrument when the algorithm says that the market meets all the requirements for a profitable trade. To create an algorithm, you perform mathematical and statistical analysis, also known as quantitative analysis, on an exchange or equity. After creating an algorithm with defined trading rules, you test it using historical market data. While this is primarily a technical field, you also need an understanding of the market.

What is algorithmic trading?

Algorithmic trading refers to the use of computer programs and algorithms to automatically execute trading orders in financial markets. These algorithms follow predefined rules based on factors like price, timing, and volume to optimize trading strategies and reduce human intervention. Algorithmic trading is widely used by institutional investors, hedge funds, and individual traders to increase efficiency, minimize costs, and capitalize on market opportunities. It can range from simple rule-based systems to complex strategies involving machine learning and artificial intelligence.

What is the difference between Algorithmic Trading vs Quantitative Analyst?

AspectAlgorithmic TradingQuantitative 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-pacedInvestment banks, asset management firms; research-focused
Employer & Industry UsageUsed to automate trading strategiesDevelops models to inform trading decisions

While both roles involve quantitative skills and finance knowledge, Algorithmic Traders focus on implementing automated trading systems, whereas Quantitative Analysts develop models and strategies that may be used by traders or firms. The roles often overlap but differ mainly in their primary focus: execution versus modeling.

What are the main challenges faced by professionals in algorithmic trading, and how can they be addressed?

Professionals in algorithmic trading often encounter challenges such as developing strategies that remain effective in rapidly changing markets, minimizing latency for faster execution, and managing the risks associated with automated trading systems. To address these challenges, it's essential to stay updated with the latest market trends and technological advancements, conduct rigorous backtesting of algorithms, and implement robust risk management protocols. Collaboration with quantitative analysts, software engineers, and risk managers is also key to ensuring strategies are both innovative and resilient.

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

To thrive as an Algorithmic Trader, you need a strong background in quantitative analysis, programming (often Python, C++, or Java), and a solid understanding of financial markets, typically supported by a degree in mathematics, engineering, finance, or computer science. Familiarity with statistical modeling tools, trading platforms, and backtesting systems is essential, and certifications such as CFA or FRM can be advantageous. Superior problem-solving skills, attention to detail, and the ability to work under pressure set standout professionals apart in this field. These skills are crucial to developing, implementing, and refining trading strategies that can operate profitably and reliably in fast-moving financial environments.
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Algorithmic Trading jobs near you

Quantitative Researcher/Developer, Algorithmic Trading Analytics

Quanta Search

Los Angeles, CA

Other

Posted 12 days ago

Job description

Our client, a leading Asset Management firm, is hiring for a Quantitative Researcher/Developer in Algorithmic Trading Analytics
Job Responsibilities (include, but not limited to the following):
  • Build backend services for capture, storage and analysis of various datasets required to measure transaction costs
  • Develop web front end for Trade Cost Analysis (TCA)
  • Develop statistical models and machine learning frameworks for evaluation of execution methods and algorithms
  • Research market impact and information transfer phenomena for various asset classes
  • Data modelling and development of systematic trading strategies

Candidate profile:
  • B.S., B.A., M.S., M.F.E or Ph.D. degree in technical field
  • Market Data Specialist, Market Microstructure
  • Familiarity with equity fundamental databases, Compustat, FactSet, Reuters WorldScope, RavenPack
  • Data Science, Data Analysis, Machine Learning, Neural Networks, Deep Learning
  • Ability to assess buy vs build tradeoffs and performance tradeoffs at all levels of the technology stack
  • Programming skills:
- Experience with kdb+/Q (required)
- Proficiency in at least one compiled language like Go/Rust/Scala/C++/Java (required)
- Proficiency in at least one scripting language like R/Python/Ruby/V8 (required)
- Experience with modern development stack (GitLab/Docker/Kubernetes/Rancher) (desirable)

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