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Probabilistic Programming Bayesian Jobs (NOW HIRING)

Instacart

Senior Risk & Compliance Engineer - Data

OR · On-site +1

$105K - $143K/yr

This is an engineering role with a data science expertise needed. You'll write production-level ... or probabilistic models (e.g., Bayesian models) in a production environment * Experience building ...

Yum Brands

Sr Machine Learning Engineer

Irvine, CA · On-site

$112K - $154K/yr

Experience with Bayesian or probabilistic modeling frameworks such as PyMC or ArviZ. * Familiarity ... Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field ...

Yum! Brands

Sr Machine Learning Engineer

Irvine, CA · On-site

$112K - $154K/yr

Experience with Bayesian or probabilistic modeling frameworks such as PyMC or ArviZ. * Familiarity ... Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field ...

Quantum Machines

Machine Learning Engineer

Develop reinforcement learning, Bayesian inference, and probabilistic modelling approaches for ... Solid software engineering fundamentals (architecture, Git workflows, testing, code review)

Snowflake

Senior Data Scientist

Menlo Park, CA · On-site

... probabilistic models (e.g., hierarchical models, state-space models, Bayesian approaches ... Engineering, Computer Science) or equivalent practical experience. • 8+ years of experience ...

Instacart

Senior Risk & Compliance Engineer - Data

$108K - $147K/yr

This is an engineering role with a data science expertise needed. You'll write production-level ... or probabilistic models (e.g., Bayesian models) in a production environment * Experience building ...

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How much do probabilistic programming bayesian jobs pay per year?

As of Sep 2, 2026, the average yearly pay for probabilistic programming bayesian in the United States is $280,147.00, according to ZipRecruiter salary data. Most workers in this role earn between $260,500.00 and $322,500.00 per year, depending on experience, location, and employer.

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 are the key skills and qualifications needed to thrive as a probabilistic programming Bayesian specialist?

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 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 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.

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Cities with the most Probabilistic Programming Bayesian job openings:

What states have the most Probabilistic Programming Bayesian jobs?

States with the most job openings for Probabilistic Programming Bayesian jobs include:

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Probabilistic Programming Bayesian jobs near you
Infographic showing various Probabilistic Programming Bayesian job openings in the United States as of August 2026, with employment types broken down into 91% Full Time, and 9% Contract. Highlights an 82% In-person, and 18% Remote job distribution, with an average salary of $280,147 per year, or $134.7 per hour.

Technical Lead - State Estimation

Overland AI

Seattle, WA • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 24 days ago

Job description

About Overland AI
Founded in 2022 and headquartered in Seattle, Washington, Overland AI is transforming land operations for modern defense. The company leverages over a decade of advanced research in robotics and machine learning, as well as a field-test forward ethos, to deliver combined capabilities for unit commanders. Our OverDrive autonomy stack enables ground vehicles to navigate and operate off-road in any terrain without GPS or direct operator control. Our intuitive OverWatch C2 interface provides commanders with precise coordination capabilities essential for mission success.
Overland AI has secured funding from prominent defense tech investors including 8VC and Point 72, and built trusted partnerships with DARPA, the U.S. Army, Marine Corps, and Special Operations Command. Backed by eight-figure contracts across the Department of Defense, we are strengthening national security by iterating closely with end users engaged in tactical operations.
Role Summary
Overland AI is building autonomous ground vehicles capable of operating where GPS is unreliable, terrain is unpredictable, and failure is not an option. We're looking for a Technical Lead in State Estimation to define the algorithms that allow our vehicles to understand exactly where they are-and keep them operating confidently in the world's most demanding environments.
In this role, you'll lead the architecture and development of our state estimation stack, solving challenging problems across localization, mapping, sensor fusion, and probabilistic inference. You'll work at the intersection of cutting-edge robotics research and production autonomy, turning advanced estimation techniques into robust systems that perform reliably in real-world deployment.
As a technical leader, you'll set the direction for state estimation at Overland, mentor a team of exceptional robotics engineers, and collaborate across perception, planning, controls, and platform engineering to build the next generation of autonomous off-road vehicles.
Key Responsibilities
  • Design and implement odometry, localization, and mapping algorithms that enable reliable autonomy in GPS-denied and degraded environments.
  • Develop robust multi-sensor fusion systems combining IMUs, LiDAR, cameras, GNSS, wheel encoders, and other onboard sensors.
  • Formulate and solve estimation problems using Kalman filtering, Bayesian inference, factor graphs, nonlinear optimization, and modern probabilistic techniques.
  • Evaluate and integrate learned approaches-including learned odometry, feature representations, and neural mapping methods-where they deliver measurable improvements over classical techniques.
  • Develop high-performance, production-quality C++ (C++23) software optimized for real-time robotic systems.
  • Build tooling, simulation infrastructure, and evaluation pipelines that enable rapid algorithm development and validation using large-scale field datasets.
  • Lead verification and validation efforts across diverse terrain, weather conditions, and operational environments.
  • Partner closely with perception, planning, controls, and systems engineers to deliver an integrated, reliable autonomy stack.
  • Mentor engineers, drive technical excellence, and establish best practices for robotics software development.

Minimum Qualifications
  • MS or PhD in Robotics, Computer Science, Electrical Engineering, or a related technical field with specialization in state estimation, SLAM, localization, or probabilistic inference.
  • 5+ years developing production-grade state estimation or SLAM systems deployed on physical robotic platforms.
  • Deep expertise in probability theory, Bayesian estimation, optimization, and nonlinear inference, including:
    • EKF, UKF, Error-State Kalman Filters
    • Factor graph optimization (GTSAM, Ceres, g2o)
    • MAP/MLE estimation
  • Demonstrated experience deploying robust estimation systems in complex, unstructured, or off-road environments.
  • Expert-level C++ and strong Python development skills.
  • Experience building low-latency, real-time robotics software.
  • Strong understanding of inertial navigation, sensor calibration, and multi-modal sensor fusion (IMU, LiDAR, cameras, GNSS).
  • Publications or significant technical contributions in SLAM, visual-inertial odometry, LiDAR-inertial odometry, localization, or related fields.

Desired Qualifications & Experience
  • PhD in Robotics, Computer Science, Electrical Engineering, or a related field.
  • Experience incorporating machine learning into estimation pipelines.
  • Experience with ROS 2 and real-time middleware (DDS, shared-memory transport).
  • Experience with terrain-relative navigation, prior-map localization, or GPS-denied navigation.
  • Contributions to widely used open-source robotics or estimation libraries.
  • Experience leading technical teams, mentoring engineers, and driving architecture decisions.
  • Experience shipping autonomy systems on production robotic or autonomous vehicle platforms.

Benefits
Overland AI believes in creating a work environment that you look forward to embracing every day.
  • The salary range for this position is $200K to $260K annually
  • Equity compensation
  • Best-in-class healthcare, dental and vision plans.
  • Flexible PTO
  • 401k with company match
  • Parental leave

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