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Assistant Machine Learning Quant Jobs in Hercules, CA

Poesis

Machine Learning Engineer

San Francisco, CA · On-site

$200K - $280K/yr

About the Role At Poesis, machine learning and artificial intelligence open the door to improved ... Familiarity with quantitative investing, portfolio construction, or risk management * Experience ...

MSCI

Machine Learning Researcher

San Francisco, CA · On-site

$144K - $187K/yr

Strong interest in quantitative finance * Expert programming skills * Publication record in machine learning or quantitative finance (preferred) * Ability to communicate complex ideas clearly to ...

MSCI

Machine Learning Researcher

San Francisco, CA · On-site

$144K - $187K/yr

Strong interest in quantitative finance * Expert programming skills * Publication record in machine learning or quantitative finance (preferred) * Ability to communicate complex ideas clearly to ...

MSCI

Machine Learning Researcher

San Francisco, CA · On-site

$144K - $187K/yr

Strong interest in quantitative finance * Expert programming skills * Publication record in machine learning or quantitative finance (preferred) * Ability to communicate complex ideas clearly to ...

Reducto

Machine Learning Engineer

San Francisco, CA · On-site

They are seeking a Machine Learning Engineer to train and deploy critical models for their core ... quantitative approach to building products. Ability to debug, experiment, and iterate fast. You ...

Hive

Machine Learning Manager

San Francisco, CA · On-site

$180K - $250K/yr

Machine Learning Manager In order to execute our vision, we're constantly growing our machine ... These tools assist our recruitment team but do not replace human judgment. Final hiring decisions ...

Hive

Machine Learning Manager

San Francisco, CA · On-site

$180K - $250K/yr

Machine Learning Manager In order to execute our vision, we're constantly growing our machine ... These tools assist our recruitment team but do not replace human judgment. Final hiring decisions ...

Jobzhr

Machine Learning Engineer

San Francisco, CA · On-site

$130 - $200/hr

About the Role We are seeking a Machine Learning Engineer to design, build, and evaluate advanced ... Strong understanding of experimental design, model evaluation, and quantitative analysis.

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Assistant Machine Learning Quant information

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How much do assistant machine learning quant jobs pay per hour?

As of Aug 28, 2026, the average hourly pay for assistant machine learning quant in Hercules, CA is $19.93, according to ZipRecruiter salary data. Most workers in this role earn between $18.03 and $21.25 per hour, depending on experience, location, and employer.

What is an assistant machine learning quant?

Assistant Machine Learning Quants are entry-level professionals in quantitative finance who support senior quants by applying machine learning techniques to analyze financial data, build predictive models, and develop trading strategies. Their responsibilities often include data cleaning, feature engineering, model selection, and performance evaluation. They work closely with quantitative researchers and traders to improve algorithmic trading systems and risk management processes. This role typically requires strong programming skills, a solid understanding of machine learning concepts, and familiarity with financial markets.

How does an assistant machine learning quant typically collaborate with senior quants and data scientists on projects?

As an Assistant Machine Learning Quant, you will often work closely with senior quantitative researchers and data scientists by supporting model development, data preprocessing, and feature engineering tasks. You may contribute to brainstorming sessions, implement prototypes, and assist in backtesting trading strategies or risk models. This collaborative environment provides valuable mentorship opportunities and exposure to best practices in quantitative analysis and machine learning within the finance industry. Effective communication and a willingness to learn from senior team members are key to success in this role.

What are the key skills and qualifications needed to thrive as an assistant machine learning quant, and why are they important?

To thrive as an Assistant Machine Learning Quant, you need strong quantitative skills, a background in statistics or mathematics, and typically a degree in a STEM field. Familiarity with programming languages such as Python or R, experience with machine learning frameworks, and knowledge of financial modeling tools are essential. Strong problem-solving abilities, attention to detail, and effective communication are standout soft skills in this role. These competencies enable accurate model development, efficient data analysis, and clear collaboration with team members in high-stakes financial environments.
Assistant Machine Learning Quant jobs near you
Infographic showing various Assistant Machine Learning Quant job openings in Hercules, CA as of August 2026, with employment types broken down into 1% As Needed, 70% Full Time, 26% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $41,459 per year, or $19.9 per hour.

Staff Machine Learning Engineer, Rewards Economy

Mistplay

Oakland, CA • Remote

$190K/yr

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Mistplay is the #1 loyalty app for mobile gamers. Our community of millions of engaged mobile gamers come to Mistplay to discover new games to play and earn rewards. Gamers are rewarded for their time and money spent within the games and can redeem those rewards for gift cards. Mistplay is on a mission to be the best way to play mobile games for everyone everywhere! Download Mistplay on the Google Play Store here and follow us on Instagram, Twitter and Facebook.

Overview: 
 
Mistplay is seeking an innovative Staff Machine Learning Engineer to lead the design and optimization of our rewards economy. In this critical role, you will develop and balance reward systems to ensure they are enticing and add value for our users while maintaining cost-effectiveness for Mistplay. Collaborating with the Data and AI organization, you will lead initiatives in real-time predictive modeling and optimization within our machine learning-driven operations.
 
What You'll Do at Mistplay:
• You’ll be part of the cross functional group responsible for the overall rewarding and the loyalty economy strategy for Mistplay.
• Create and implement machine learning driven rewards economy systems and core loops.
• Continuously analyze and optimize the economy strategies and implementations to enhance engagement and drive business outcomes.
• Drive the strategy on how machine learning and AI can further advance the optimization for better user experience and business performance.
• Distill complex model outputs into actionable strategic insights for executive stakeholders, advocating for AI-first approaches to loyalty and retention.
• Design and productionize contextual bandits and RL frameworks to personalize reward distribution in real-time, ensuring the right user gets the right incentive at the right moment.
• Serve as the primary architect for Mistplay’s rewards economy, balancing complex variables to ensure a "win-win" scenario: maximum value for players and sustainable unit economics for the business.
• Partner with Product, Analytics, Engineering, and Finance to translate high-level business goals into algorithmic constraints and incentive structures.
 
What You'll Bring to Mistplay:
• A combination of 8+ years in Data Science, Machine Learning, Quant, Econometrics, and/or Master’s or Ph.D. in Data Science, Machine Learning, or a related quantitative field.
• Understanding of reinforcement learning (DeepQ, contextual bandits)
• Expertise in implementing machine learning models using Python and PyTorch, paired with strong data analysis skills using SQL and Python.
• Fast and agile approach to exploring and implementing solutions with a strategic focus on the core loop and economy design.
• Experience and background in Quant, Data Science, or related fields, with a foundational understanding of economy and core loop designs in gaming or similar contexts.
• A passion for innovative AI solutions, with demonstrated knowledge in real-time predictive modeling.
• Prior experience in the ad-tech or mobile gaming industry with a focus on the economics of player behavior and engagement strategies is a strong nice-to-have.
 

The salary listed is based on the National Average across the US, & based on base salary only, there is a bonus component & many additional benefits, including equity.

For candidates who demonstrate full readiness for the defined scope of the role, the typical starting salary is $190,000 USD. Offers below this point reflect candidates we believe can grow into the full scope of the role with support and development. Offers above this point reflect impact that meaningfully exceeds the role’s defined expectations or an expanded scope from day one. 

At Mistplay, we are very passionate about internal equity, & ensuring all staff members are paid fairly & equally for their position. The final salary offered will vary based on candidate experience & location.

We are also open to up levelling the position to Principal for candidates that demonstrate this level of expertise - We encourage all to apply! 

Why Mistplay?
We strive to make our work environment as inviting and fun as possible! Working at Mistplay is coupled with a whole array of perks that we've adopted virtually and in-person: Team Lunches, game nights, company-wide events, and so much more. Our culture is deeply rooted in growth and upheld by a team of smart, dynamic, and enthusiastic people. We utilize data to constantly learn, improve, and adapt. We foster an environment where everyone is encouraged to share their ideas, push boundaries, take calculated risks, and witness their visions come to life.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.