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Probabilistic Programming Bayesian Jobs in Wake Forest, NC

NCBiotech

Principal Machine Learning Scientist

Durham, NC · On-site

$180 - $260/hr

Design and deploy closed‑loop learning systems using methods including Bayesian optimisation ... Work directly with biologists and automation engineers to translate biological questions into ...

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All JobsProbabilistic Programming Bayesian JobsProbabilistic Programming Bayesian Jobs in Wake Forest, NC

Probabilistic Programming Bayesian information

See Wake Forest, NC salary details

$131.7K

$240.3K

$295.1K

How much do probabilistic programming bayesian jobs pay per year?

As of Aug 8, 2026, the average yearly pay for probabilistic programming bayesian in Wake Forest, NC is $240,284.00, according to ZipRecruiter salary data. Most workers in this role earn between $223,400.00 and $276,600.00 per year, depending on experience, location, and employer.

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?

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 job categories do people searching Probabilistic Programming Bayesian jobs in Wake Forest, NC look for? The top searched job categories for Probabilistic Programming Bayesian jobs in Wake Forest, NC are:
What cities near Wake Forest, NC are hiring for Probabilistic Programming Bayesian jobs? Cities near Wake Forest, NC with the most Probabilistic Programming Bayesian job openings:
Probabilistic Programming Bayesian jobs near you
Infographic showing various Probabilistic Programming Bayesian job openings in Wake Forest, NC as of August 2026, with employment types broken down into 1% Internship, 79% Full Time, 14% Part Time, 1% Temporary, 4% Contract, and 1% Nights. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $240,284 per year, or $115.5 per hour.

Principal Machine Learning Scientist

NCBiotech

Durham, NC • On-site

$180 - $260/hr

Other

Medical, Dental, Vision

Posted 3 days ago

New

Job description

Principal Machine Learning Scientist

RTP, NC - Hybrid | Full-Time

The mission

CellForge is an AI-guided robotic platform for scalable, reproducible human cell manufacturing. We combine single-cell transcriptomics, imaging, and robotic culture systems with machine learning to discover and implement optimal differentiation protocol, and close the loop between what the cells tell us and what we do next.

The role

You will build the intelligence layer: models that listen to cells, predict where they're going, and actively steer them toward target states. This is a hands‑on principal‑level IC role reporting directly to the CEO.

Core work
  • Design and deploy closed‑loop learning systems using methods including Bayesian optimisation, model predictive control, reinforcement learning and active learning to guide experimental campaigns in real time
  • Build multimodal models across single‑cell RNA‑seq, imaging, time‑series sensor data, and experimental metadata
  • Develop trajectory‑aware dynamics models for cell fate prediction and protocol optimisation
  • Define data pipelines and ML infrastructure for high‑throughput biological experimentation
  • Work directly with biologists and automation engineers to translate biological questions into tractable ML problems
What we're looking forRequired
  • Strong ML fundamentals - probabilistic modelling, optimisation, experimental design
  • Hands‑on experience with biological data, especially scRNA‑seq (scanpy, anndata, scVI etc); imaging processing is a plus
  • Proficiency in Python and PyTorch (or equivalent)
  • Demonstrated ability to ship models in complex, real‑world settings with noisy, sparse data
  • PhD or equivalent practical depth in ML, computational biology, bioinformatics, or related field
Preferred
  • Experience with closed‑loop or real‑time learning systems
  • Prior biotech / scientific ML work
  • Experience in an early‑stage company environment
  • Publications in top domain‑relevant journals
What we offer
  • Ground‑floor ML ownership at a seed‑stage deep tech company
  • We pay a real salary - just below market - and weight the rest toward meaningful equity. Ideal if you're excited to build long‑term value with us.
  • Direct access to founders and the full experimental platform - you see the data you generate
  • High autonomy, fast pace, real biological impact
  • Excellent medical benefits, dental, eyecare, and more
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