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Machine Learning Assistant Jobs in New York (NOW HIRING)

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Machine Learning Assistant information

What is a machine learning assistant?

A Machine Learning Assistant is a professional who supports the development, implementation, and maintenance of machine learning models and systems. They assist data scientists and engineers by preparing datasets, conducting preliminary data analysis, running experiments, and helping to optimize algorithms. This role often involves coding, testing models, and ensuring the quality and reliability of machine learning solutions. Machine Learning Assistants play a key role in streamlining workflows and enabling faster progress in AI projects.

What are the key skills and qualifications needed to thrive as a machine learning assistant?

To thrive as a Machine Learning Assistant, a solid background in mathematics, statistics, programming (often Python), and foundational knowledge of machine learning algorithms is essential, typically supported by a relevant degree or coursework. Familiarity with tools like TensorFlow, scikit-learn, Jupyter Notebooks, and version control systems such as Git is commonly required. Strong problem-solving abilities, attention to detail, and the capability to communicate findings effectively are standout soft skills in this role. These skills ensure accurate data analysis, effective model building, and successful collaboration within multidisciplinary teams.

What are some common challenges a machine learning assistant may face when supporting data preparation and model training?

Machine Learning Assistants often encounter challenges such as cleaning large, unstructured datasets, identifying and handling missing or inconsistent data, and ensuring data privacy compliance. They also need to communicate effectively with data scientists and engineers to understand project requirements and adapt to evolving priorities. Staying organized and managing multiple tasks simultaneously—such as data preprocessing, feature engineering, and running model experiments—is crucial for success in this role.

What are the most commonly searched types of Machine Learning jobs in New York?

The most popular types of Machine Learning jobs in New York are:

What are popular job titles related to Machine Learning Assistant jobs in New York?

For Machine Learning Assistant jobs in New York, the most frequently searched job titles are:

What job categories do people searching Machine Learning Assistant jobs in New York look for?

The top searched job categories for Machine Learning Assistant jobs in New York are:

What cities in New York are hiring for Machine Learning Assistant jobs?

Cities in New York with the most Machine Learning Assistant job openings:

Machine Learning Assistant jobs near you
Infographic showing various Machine Learning Assistant job openings in New York as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 23% Part Time, 2% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Staff Machine Learning Engineer, Rewards Economy

Mistplay

New York, NY • Remote

$180K - $220K/yr

Full-time

Posted 4 days ago

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 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.