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Simplebet is hiring a
Artificial Intelligence & Machine Learning Architect

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Read by 36 job seekers.

New York, NY

Our mission is to power the future of fan engagement. 

Simplebet is a B2B sports technology company that uses machine learning and real-time solutions to make every moment of every sporting event a betting opportunity. We’re reimagining how people enjoy sports with products that are simple, intuitive, and entertaining.  Our technology powers micro-betting capabilities of the leading operators around the globe.



Responsibilities:
  • Design and build AI and ML products.
  • Research, prototype, validate, and iterate on AI/ML algorithms, patterns, processes, and tooling to identify new opportunities and maintain AI/ML competitive differentiation.
  • Act as AI/ML tooling and modeling SME to evangelize, implement, configure, and maintain best-fit architectural solutions.
  • Participate in commercial pursuits and ensure that AI/ML solutions exceed market expectations.
  • Function as individual contributor on any part of cloud ML pipeline including preparation and cleansing of data, developing features, training and evaluating models, and deploying models to production.
  • Collaborate daily with Engineering and Product Teams to help steer ongoing AI/ML efforts.
  • Train/advise/enable engineers on efficient use of available AI/ML tooling, best practices, and techniques.
  • Provide architectural assessments, strategies, and roadmaps for competitive AI/ML solutions.
  • Actively mentor Junior Architects and ML Engineers.
  • Occasionally directly manage Engineers to complete a specific initiative.
  • Effectively represent Simplebet in the greater ML/AI community by contributing to open-source projects, technical writing and speaking, and evangelizing company solutions in public forums.
  • Apply statistical skills to evaluate and interpret experimental data.
  • Ensure adherence to security and proper data availability.
  • Communicate important technical information to Leadership Team to guide organizational direction and demonstrate understanding of technology tradeoffs.
  • Optimize model development life cycles.
  • Maximize quality of models, services, and tooling with unit testing, integration testing, automation, observability, and fault tolerance.

  • Requirements:
  • Master’s degree in Statistics, Mathematics or related field and three years experience in the job offered or three years probabilistic classification modeling.
  • Experience must include three years each of the following: Python programming; AWS/GCP/Azure; Docker/Kubernetes; Scikit-Learn framework; and ML/AI process and orchestration tools.
  • Experience must include two years each of the following: relational and non-relational DBs; ML statistical evaluation techniques including temporal, non-linear, Bayesian, latent-space and local regression techniques; applying AI/ML models to play-by-play sports data; and mentoring engineers and delivering solutions within defined timeframes.
  • Experience may be gained concurrently.

  • Simplebet is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based upon race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.

    We are committed to providing access, equal opportunity and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. Please let us know if you require reasonable accommodation for the interview process, and we will make every effort to provide it.

    Estimated Salary range:

    Our salary intelligence, powered by our AI algorithms, has calculated an estimated range for this position between $120,000 - $160,000 . Please note that the actual salary for this position may vary, as it is dependent on various factors including but not limited to experience, location, and market dynamics.

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