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Quant Developer Jobs in California (NOW HIRING)

Poesis LLC

Machine Learning Engineer

Menlo Park, CA

Poesis Machine Learning Engineer At Poesis, machine learning and artificial intelligence open the ... Familiarity with quantitative investing, portfolio construction, or risk management * Experience ...

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How much do quant developer jobs pay per year?

As of Jun 13, 2026, the average yearly pay for quant developer in California is $167,506.00, according to ZipRecruiter salary data. Most workers in this role earn between $132,700.00 and $196,400.00 per year, depending on experience, location, and employer.

How much do quant devs make?

Quant developers typically earn between $100,000 and $200,000 annually, with experienced professionals and those at top firms earning over $300,000 including bonuses. Compensation often depends on experience, location, and the complexity of the models they develop, with many roles requiring strong programming skills in languages like Python or C++ and knowledge of financial markets.

What is a Quant Developer job?

A Quant Developer (Quantitative Developer) is a software engineer who builds and maintains financial models, trading systems, and analytical tools for quantitative analysts and traders. They use programming languages like Python, C++, or Java to develop algorithms that automate trading strategies, risk analysis, and data processing. Quant Developers typically work in hedge funds, investment banks, or proprietary trading firms, collaborating with quants and portfolio managers to optimize trading performance. Strong mathematical skills, proficiency in financial markets, and expertise in software development are essential for this role.

Does JP Morgan hire quants?

JP Morgan actively hires quantitative analysts and developers for roles in trading, risk management, and technology. These positions typically require strong programming skills, knowledge of financial models, and often a background in mathematics or engineering. The firm offers opportunities for quants across various teams and locations, with competitive hiring standards and onboarding processes.

What are some typical challenges quant developers face in their daily work?

Quant developers often work with large, complex datasets and real-time data streams, which can present technical challenges related to performance, accuracy, and scalability. They may need to continuously adapt to changing market requirements or new financial regulations, requiring staying up to date and learning new tools or methods. Collaboration with quants, traders, and other stakeholders is common, so balancing technical problem-solving with effective communication is also important. These challenges make the role both demanding and intellectually rewarding for those passionate about technology and finance.

Is quant developer a good career?

A quant developer is a highly specialized role that involves developing algorithms and models for financial trading and risk management. It typically requires strong programming skills in languages like Python or C++, along with a background in mathematics or finance. The role offers high earning potential and demand in financial firms but often involves long hours and high pressure.

What are the key skills and qualifications needed to thrive in the Quant Developer position, and why are they important?

To thrive as a Quant Developer, you need advanced programming skills (often in Python, C++, or Java), a strong foundation in mathematics or statistics, and a relevant degree such as in computer science, engineering, or quantitative finance. Expertise in numerical libraries, version control systems like Git, and familiarity with financial modeling tools or industry data feeds is highly valuable. Collaboration, strong analytical thinking, and the ability to communicate complex concepts clearly are critical soft skills for this role. These capabilities are essential for designing robust quantitative models and working effectively with cross-functional teams in fast-paced financial environments.

Is 30 too late to become a quant?

Quantitative analyst roles typically require strong backgrounds in mathematics, programming, and finance, often gained through advanced degrees or relevant experience. While starting a career at 30 is possible, it may require additional training or certifications such as a master's in financial engineering or programming skills in Python or C++. Age is less important than skills, experience, and the ability to adapt to a fast-paced, technical environment.
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Quant Developer jobs near you
Infographic showing various Quant Developer job openings in California as of June 2026, with employment types broken down into 89% Full Time, 3% Part Time, 3% Contract, and 5% Nights. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $167,506 per year, or $80.5 per hour.
Experienced Quantitative Developer

Experienced Quantitative Developer

Swish Analytics

San Francisco, CA • On-site, Remote

Full-time

Posted 26 days ago

Job description

Company Description
Swish Analytics is a sports analytics, betting, and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition. We're looking for team-oriented individuals with an authentic passion for accurate and predictive real-time data who can execute in a fast-paced, creative, and continually-evolving environment without sacrificing technical excellence. Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building systems to support products across a variety of industries and consumer/enterprise clients.
Role Overview
You'll architect and build the core trading systems that execute our fair value models across sports betting exchanges at scale. This is a systems engineering role focused on real-time decision-making, multi-venue orchestration, and low-latency execution under production constraints.
Core Responsibilities
Real-Time Trading Engine Architecture
  • Design event-driven trading systems that consume fair value models and market data to make sub-second execution decisions
  • Build the core logic for comparing fair values against live market prices and determining when/where to trade
  • Implement asynchronous order generation, submission, and cancellation workflows across multiple venues with different latency profiles
  • Design state machines for order lifecycle management (pending, accepted, filled, cancelled, rejected) with proper event ordering and idempotency

Multi-Venue Execution & Routing
  • Build venue-specific integrations (WebSocket connections to Matchbook, Kalshi; REST API adapters for Betfair; FIX protocol handlers)
  • Implement intelligent order routing that selects optimal venues based on liquidity, fees, latency, and position constraints
  • Design coordination logic for managing orders across multiple venues when a single bet spans several platforms
  • Handle venue-specific quirks (rate limiting, connection drops, partial fills, odds movement during submission)

Position & Risk Management Systems
  • Build real-time position tracking systems that aggregate exposure across all venues, markets, and event types
  • Implement global liability management that enforces risk limits while maximizing capital utilization
  • Design systems that detect and respond to position drift (when actual fills deviate from intended exposure)
  • Create reconciliation engines that validate positions against venue reports and detect/resolve discrepancies

Data & Execution Infrastructure
  • Design data pipelines that ingest real-time market data from multiple feeds (WebSocket streams, REST polling, custom adapters) into low-latency in-memory stores
  • Build efficient order book representation and query systems optimized for fast fair value lookups
  • Implement message ordering and deduplication logic for ensuring consistent state across async operations
  • Design persistent logging and event sourcing for order/trade auditing and post-incident analysis
Required Qualifications
Domain Experience
  • 3+ years building production trading/market-making systems for betting syndicates, sharp groups, or exchanges
  • Deep understanding of exchange vs. bookmaker dynamics and practical experience executing against both
  • Hands-on experience integrating with real-time sports betting data feeds and exchange APIs

Technical Fundamentals
  • 3+ years of production Python with expert-level async/await, event loop, and concurrent execution skills
  • Strong system design for distributed, real-time, event-driven systems
  • Deep understanding of database transactions, consistency models, and state management under high throughput
  • Experience with message streaming platforms (Kafka or equivalent) for order/execution event handling
  • Proficiency with containerization (Docker), orchestration (Kubernetes), and cloud infrastructure (AWS, GCP)

Core Competencies
  • Ability to architect systems that make correct decisions under tight latency constraints
  • Strong debugging skills for timing issues, race conditions, and event ordering problems
  • Systematic problem-solving for production incidents in trading systems
  • Pragmatic engineering decisions (when to accept latency vs. consistency tradeoffs)
Strongly Preferred
  • Experience building order management systems (OMS) or execution management systems (EMS)
  • Background in low-latency or high-frequency trading system design
  • Hands-on work with WebSocket real-time connections and connection resilience patterns
  • Experience with FIX protocol or similar financial messaging standards
  • Knowledge of multi-leg execution and cross-product coordination challenges
  • Familiarity with market microstructure (order book dynamics, market impact, slippage models)
  • Experience designing systems that respond to real-time market feedback (volatile prices, volume spikes)
Nice to Have
  • Contributions to trading infrastructure or market-making open-source projects
  • Experience with Protobuf for efficient data serialization in latency-sensitive systems
  • Exposure to blockchain/DeFi trading systems and AMM-style execution
  • Knowledge of database CDC (Debezium) or event streaming architectures for audit/replay
  • Background building resilience patterns (circuit breakers, backpressure, graceful degradation) in trading systems
  • Experience working with Rust or C++

Base salary: starting at $170,000
Swish Analytics is an Equal Opportunity Employer. All candidates who meet the qualifications will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, pregnancy status, genetic, military, veteran status, marital status, or any other characteristic protected by law. The position responsibilities are not limited to the responsibilities outlined above and are subject to change. At the employer's discretion, this position may require successful completion of background and reference checks.
Department Trading Analytics Role Trading Data Engineering Locations San Francisco, CA - Remote Remote status Fully Remote

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