ZipRecruiter

Log In

1

Probabilistic Programming Bayesian Jobs in New York

MassMutual

AI Engineer

Manhattan, NY · Hybrid

$141K - $185K/yr

Python programming, with the ability to write clean, well-tested, production-quality code ... Bayesian approaches, with comfort working across techniques as problems demand. * Proficiency in ...

MassMutual

Lead AI Engineer

Manhattan, NY

$172K - $225K/yr

... probabilistic modeling, with accountability for reliability, performance, and maintainability ... Strong programming skills in Python , with the ability to write clean, well-tested, production ...

Aaru

Simulation Engineer

New York, NY · On-site

$200K - $540K/yr

ABOUT THE ROLE Simulation Engineering owns the path from research idea to production system ... Have worked on probabilistic modeling, Bayesian inference, or causal reasoning * Have familiarity ...

next page

Showing results 1-20

All JobsProbabilistic Programming Bayesian JobsProbabilistic Programming Bayesian Jobs in New York

Probabilistic Programming Bayesian information

What are the typical challenges faced by professionals working in Probabilistic Programming with a Bayesian focus, and how can they be addressed?

Professionals working in Probabilistic Programming with a Bayesian focus often encounter challenges related to model complexity, computational efficiency, and communicating results to non-technical stakeholders. Building accurate Bayesian models requires careful selection of priors and an understanding of underlying data distributions, which can be demanding without robust domain expertise. Additionally, computational demands can be high, especially for large datasets or complex hierarchical models, making efficient sampling and approximation methods essential. Collaborating closely with domain experts and leveraging modern probabilistic programming frameworks can help address these challenges and ensure practical, interpretable results.

What is probabilistic programming in the context of Bayesian statistics?

Probabilistic programming in the context of Bayesian statistics refers to writing computer programs that use probability distributions and Bayesian inference to model uncertainty and learn from data. These programs allow users to define complex probabilistic models using code, making it easier to specify, fit, and analyze Bayesian models. Probabilistic programming languages, such as Stan, PyMC, or Edward, provide tools to automate inference, enabling practitioners to focus on modeling rather than mathematical derivations. This approach is widely used in fields like machine learning, data science, and scientific research to handle uncertainty and make predictions.

What is the difference between Probabilistic Programming Bayesian vs Data Scientist?

AspectProbabilistic Programming BayesianData Scientist
Required credentialsBackground in statistics, probability, programmingStatistics, computer science, or related degree
Work environmentResearch, modeling, algorithm developmentData analysis, visualization, business insights
Industry usageAI, machine learning, research projectsBusiness, finance, tech, healthcare

Probabilistic Programming Bayesian focuses on developing models using Bayesian methods and probabilistic programming languages, often in research or AI development. Data Scientists analyze data to extract insights, build predictive models, and support decision-making. While both roles require statistical knowledge, Bayesian programmers specialize in probabilistic modeling, whereas Data Scientists apply a broader set of data analysis techniques.

What are the key skills and qualifications needed to thrive as a Probabilistic Programming Bayesian specialist, and why are they important?

To thrive as a Probabilistic Programming Bayesian specialist, you need a strong background in statistics, probability theory, and Bayesian inference, often supported by a degree in mathematics, statistics, computer science, or a related field. Expertise with probabilistic programming languages (such as Stan, PyMC, or TensorFlow Probability) and familiarity with statistical modeling software are also essential. Analytical thinking, problem-solving, and effective communication skills help translate complex models into actionable insights and collaborate with interdisciplinary teams. These skills and qualities are crucial for developing robust, interpretable models that inform decision-making in research and industry applications.
What are popular job titles related to Probabilistic Programming Bayesian jobs in New York? For Probabilistic Programming Bayesian jobs in New York, the most frequently searched job titles are:
What job categories do people searching Probabilistic Programming Bayesian jobs in New York look for? The top searched job categories for Probabilistic Programming Bayesian jobs in New York are:
What cities in New York are hiring for Probabilistic Programming Bayesian jobs? Cities in New York with the most Probabilistic Programming Bayesian job openings:
Probabilistic Programming Bayesian jobs near you
Infographic showing various Probabilistic Programming Bayesian job openings in New York as of July 2026, with employment types broken down into 100% Full Time. Highlights an 50% In-person, and 50% Hybrid job distribution.

Research Engineer, Platform

Basis Research

New York, NY • On-site

Full-time

Posted 22 days ago

Job description

About Basis
Basis is a nonprofit applied AI research organization with two mutually reinforcing goals.
The first is to understand and build intelligence. This means to establish the mathematical principles of what it means to reason, to learn, to make decisions, to understand, and to explain; and to construct software that implements these principles.
The second is to advance society's ability to solve intractable problems. This means expanding the scale, complexity, and breadth of problems that we can solve today, and even more importantly, accelerating our ability to solve problems in the future.
To achieve these goals, we're building both a new technological foundation that draws inspiration from how humans reason, and a new kind of collaborative organization that puts human values first.
About the Role
Research Engineers on the Platform team at Basis advance research methods and package them into reusable modules that others can build on. You will develop Basis core technology modules (fundamental algorithmic components like ChiRho, Effectful, Weighted), contribute research-driven capabilities to commercial platform offerings, and ensure research advances have clear paths to impact-whether commercial or societal.
We are looking for people who bridge research excellence and engineering rigor. The ideal Research Engineer has both published research and shipped production code, understands how to translate experimental techniques into robust implementations, and thinks carefully about software architecture that enables others to build on your work. You will identify high-value research problems aligned with Basis mission and develop them from ideation through concrete implementation.
This role is distinguished from Operations Research Engineers (who focus on internal tooling) by emphasis on advancing research methods for platform and commercial applications. You will work on Core Tech Module Teams and contribute to research that defines Basis's technological foundation.
We seek individuals who excel technically and value probing concepts at their foundations. Our research engineers aspire to do rigorous, high-quality, robust science and engineering, but are not afraid to tinker, make mistakes, and explore radically different ideas to get there.
Basis is a collaborative effort, both internally and with our external partners; we are looking for people who enjoy working with others on problems larger than ones they can tackle alone.
We expect you to:
  • Have demonstrated ability to conduct research that is of high quality. Possible ways to demonstrate this include:
    • Publications at top-tier conferences (NeurIPS, ICML, ICLR, POPL, PLDI, OOPSLA)
    • Technical reports or preprints showing novel research contributions
    • Open-source research projects with significant adoption or citations
  • Have demonstrated ability to drive software projects from start to finish. This could be evidenced by:
    • Research code released as production-quality libraries or frameworks
    • Contributions to major open-source projects (PyTorch, TensorFlow, JAX, or equivalents)
    • Systems built that span research prototypes through deployable implementations
  • Possess deep knowledge of relevant technical areas including probabilistic programming, causal inference, program synthesis, neural architectures, or related fields central to Basis research directions.
  • Be proficient in research engineering tools including Python, PyTorch/JAX, version control, testing frameworks, documentation systems, and software engineering practices that make research code maintainable and extensible.
  • Understand paths from research to impact. You think about how research advances can translate to commercial applications, platform capabilities, or open-source contributions that benefit broader communities.
  • Value software quality and reusability. You design modules that others can build on, write comprehensive documentation and tests, and architect systems that remain coherent as they evolve.
  • Progress with autonomy and intellectual curiosity. You can identify valuable research directions, design experiments, implement solutions, and evaluate results without extensive direction.
  • Be excited about solving real-world problems and having positive societal impact through research that advances our understanding of intelligence and our ability to tackle intractable challenges.

In addition, the following would be an advantage:
  • PhD in Computer Science, Machine Learning, Statistics, or related field with publications at top-tier venues.
  • Experience at research organizations that successfully productionized innovations (Bell Labs, PARC, Microsoft Research, Google Research, Meta FAIR, DeepMind).
  • Contributions to widely-used research libraries or frameworks.
  • Background spanning multiple areas (ML + PL, causal inference + probabilistic programming, theory + systems).
  • Experience with Bayesian methods, probabilistic programming languages, or causal modeling.
  • Track record of research that influenced commercial products or open-source ecosystems.

Responsibilities:
  • Advance research methods in areas aligned with Basis mission including probabilistic programming, causal inference, reasoning under uncertainty, program synthesis, and foundations of machine learning.
  • Build Basis core technology modules producing fundamental algorithmic components that provide reusable capabilities across multiple projects and applications.
  • Package research into production-ready code by translating experimental techniques into robust implementations with comprehensive documentation, tests, and examples that enable others to build on your work.
  • Contribute to Platform development by developing research-driven capabilities that enhance commercial offerings, integrating novel techniques into platform APIs and features.
  • Identify high-value research problems by staying connected to research frontiers, understanding commercial and societal needs, and recognizing opportunities where Basis capabilities can make significant impact.
  • Develop new project concepts from ideation through concrete vision, scoping research questions, defining technical approaches, and creating compelling cases for new initiatives.
  • Collaborate across research and engineering teams to ensure research advances integrate smoothly with platform infrastructure and that engineering constraints inform research directions.
  • Ensure research has path to impact whether through commercial deployment, open-source release, academic publication, or transfer to other projects within Basis portfolio.
  • (Optionally) Publish and present findings in journals and conferences to contribute to broader research community and establish Basis thought leadership.
  • Contribute to the culture and direction of Basis by modeling excellence at the intersection of research and engineering, rigorous thinking about foundations, and commitment to building tools that advance societal capability.
Role Details
Exceptional candidates who may not meet all of the following criteria are still encouraged to apply.
  • FT/PT: This is a full-time position
  • In-person Policy: We are in the office four days a week. Be prepared to attend multi-day Basis-wide in-person events.
  • Location: New York City
  • Salary range: Competitive salary.

Non-Discrimination Notice
Basis Research Institute provides equal employment opportunities without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or genetics and prohibits discrimination based on all protected characteristics.
Privacy Notice
By submitting your application, you grant Basis permission to use your materials for both hiring evaluation and recruitment-related research and development purposes. Your information may be processed in different countries, including the US. You retain copyright while providing Basis a license to use these materials for the stated purposes.
Read our full Global Data Privacy Notice here.

Apply