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Algorithmic Trading Jobs in California (NOW HIRING)

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 ...

ALSO

Senior Software Engineer, Algorithms + ML

Palo Alto, CA · On-site

$144K - $189K/yr

You should be comfortable balancing algorithmic quality with real-world product constraints such as ... Manage technical priorities, project timelines, and trade-offs in a fast-moving product development ...

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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 Jul 13, 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.

How to become an algorithmic trader?

To become an algorithmic trader, you should develop strong programming skills in languages like Python or C++, gain knowledge of financial markets and trading strategies, and learn to use trading platforms and data analysis tools. A background in mathematics, statistics, or computer science is often essential, and many traders pursue certifications such as the Chartered Market Technician (CMT) or Financial Risk Manager (FRM). Experience with backtesting and risk management is also important for success in this field.

Is algo trading a good career?

Algorithmic trading is a specialized career that involves developing and implementing automated trading strategies using programming skills, data analysis, and financial knowledge. It can be lucrative and in demand in financial firms, but it requires strong technical expertise, continuous learning, and understanding of market regulations. Success in this field depends on technical proficiency, risk management, and staying updated with market trends.

Who is the richest Algo trader in the world?

Algorithmic trading is a specialized finance role involving the use of algorithms and programming skills to execute trades. While individual net worth is often private, some of the most successful algorithmic traders and quant hedge fund managers, such as Jim Simons, have accumulated significant wealth through quantitative strategies and advanced data analysis. These professionals typically work in high-frequency trading firms or hedge funds and require strong mathematical, programming, and financial skills.

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.

Can I make money with algorithmic trading?

Algorithmic trading professionals develop and implement automated trading strategies that can generate profits if the algorithms are well-designed and market conditions are favorable. Success depends on skills in programming, data analysis, and risk management, and consistent profitability is not guaranteed. Many traders experience both gains and losses, and ongoing testing and optimization are essential for potential profitability.

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.
More about Algorithmic Trading jobs
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Algorithmic Trading jobs near you
Infographic showing various Algorithmic Trading job openings in California as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $84,627 per year, or $40.7 per hour.

AI Systems, Training

Unconventional AI

Palo Alto, CA • On-site

$123K - $168K/yr

Full-time

Re-posted 9 days ago

Job description

Job Summary:
Unconventional AI is a company focused on redefining computing to solve the energy limitations of AI. They are seeking a key contributor to build a next-generation ML model training platform and co-design training systems alongside novel AI models and hardware.
Responsibilities:
• Build and maintain highly optimized, model-specific training stacks specifically tuned for state-of-the-art generative vision, language, and world models.
• Design and scale multi-node distributed training systems, implementing elastic sharding and robust data streaming pipelines for fast, large-scale iteration. Implement and robust model checkpointing and recovery mechanisms.
• Develop and optimize kernels using low-level programming models like CUDA and Triton. 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 converting model requirements into concrete specifications for infrastructure and hardware engineering teams.
Qualifications:
Required:
• Education: An MS/PhD or equivalent research/project experience in a quantitative field such as AI/Machine Learning, Computer Science, Physics, Electrical Engineering, or Applied Math.
• Experience: Veteran of the modern ML software stack. Demonstrated ability to map state-of-the-art AI model architectures (e.g., transformers, Mixture of Experts, diffusion models) to system performance implication. Deep expertise in how models are partitioned across a cluster, with a mastery of communication primitives, and parallelism strategies.
• Software Development: Proven track record of implementing, debugging, and maintaining production-grade training frameworks—such as Megatron-LM, DeepSpeed, Ray, PyTorch Lightning—turning raw compute into a reliable model-building factory.
Preferred:
• Unconventional Co-Design: A forward-looking perspective on co-designing algorithms for unconventional computing paradigms that map closely to the physics of underlying systems.
Company:
Unconventional AI rethinks computer foundations to optimize energy efficiency for AI. Founded in 2025, the company is headquartered in San Francisco, USA, with a team of 11-50 employees. The company is currently Early Stage.

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