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Internship Bayesian Jobs (NOW HIRING)

Socket.dev

Associate Data Scientist

Richmond, VA · On-site

$105 - $128/hr

Up to 2 years of experience (including internships, academic, or personal projects) building and ... Bayesian models). * Exposure to data and compute platforms such as Snowflake and Databricks.

New

Costello Medical

Senior Statistician

New York, NY · On-site

$95K - $105K/yr

... complex Bayesian network meta-analysis using both standard and emerging methods. The Senior ... There may also be opportunities to line manage and mentor our Statistician Interns. Career ...

Costello Medical

Senior Statistician

Boston, MA · On-site

$95K - $105K/yr

... complex Bayesian network meta-analysis using both standard and emerging methods. The Senior ... There may also be opportunities to line manage and mentor our Statistician Interns. Career ...

Costello Medical

Senior Statistician

Boston, MA · On-site

$95K - $105K/yr

... complex Bayesian network meta-analysis using both standard and emerging methods. The Senior ... There may also be opportunities to line manage and mentor our Statistician Interns. Career ...

Costello Medical

Senior Statistician

Boston, MA

$95K - $105K/yr

... complex Bayesian network meta-analysis using both standard and emerging methods. The Senior ... There may also be opportunities to line manage and mentor our Statistician Interns. Career ...

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How much do internship bayesian jobs pay per hour?

As of Aug 21, 2026, the average hourly pay for internship bayesian in the United States is $17.31, according to ZipRecruiter salary data. Most workers in this role earn between $14.42 and $19.23 per hour, depending on experience, location, and employer.

What is an internship in Bayesian analysis?

An Internship in Bayesian analysis is a temporary, practical position focused on applying Bayesian statistical methods to real-world problems. Interns typically work under the supervision of experienced data scientists or statisticians, assisting with research, data modeling, and computational analysis using Bayesian techniques. These internships are valuable for students or recent graduates looking to gain hands-on experience in probabilistic modeling, data analysis, and statistical inference. Such internships often require a strong mathematical background and familiarity with programming languages like Python or R.

What are the key skills and qualifications needed to thrive as a Bayesian intern?

To thrive in a Bayesian Internship, you need a solid background in statistics, probability theory, and data analysis, typically supported by coursework or a degree in mathematics, statistics, or a related field. Familiarity with programming languages such as Python or R, and experience with statistical software and Bayesian modeling tools (e.g., Stan, PyMC) are commonly required. Strong analytical thinking, problem-solving abilities, and effective communication skills help interns interpret results and collaborate with research teams. These skills are essential for accurately applying Bayesian methods to real-world data and effectively communicating insights.

What are some common challenges interns face when working on Bayesian analysis projects, and how can they overcome them?

Interns working on Bayesian analysis projects often encounter challenges such as understanding complex statistical principles, learning new software (like Stan or PyMC), and interpreting probabilistic results. To overcome these obstacles, it's helpful to actively seek guidance from mentors, participate in team discussions, and utilize available learning resources. Collaborating closely with experienced team members and regularly reviewing project code and results can accelerate learning and help interns gain confidence in applying Bayesian methods to real-world problems.

What is the difference between Internship Bayesian vs Data Analyst Intern?

AspectInternship BayesianData Analyst Intern
Required CredentialsRelevant coursework in Bayesian statistics, basic programming skillsStatistics, data analysis, programming knowledge
Work EnvironmentResearch-focused, collaborative teams in tech or research firmsBusiness or tech companies, data-driven projects
Employer & Industry UsageUsed in research, AI, machine learning sectorsCommon in finance, marketing, tech industries
Search & Comparison IntentUnderstanding roles involving Bayesian methodsExploring data analysis internship opportunities

Internship Bayesian typically involves applying Bayesian statistical methods in research or AI projects, requiring knowledge of Bayesian theory and programming. Data Analyst Internships focus on analyzing datasets, creating reports, and supporting business decisions. While both roles involve data skills, Internship Bayesian emphasizes probabilistic modeling, whereas Data Analyst Internships focus on data visualization and reporting.

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Internship Bayesian jobs near you
Infographic showing various Internship Bayesian job openings in the United States as of August 2026, with employment types broken down into 10% Internship, 56% Full Time, 32% Part Time, 1% Temporary, and 1% Contract. Highlights an 77% Physical, 2% Hybrid, and 21% Remote job distribution, with an average salary of $35,995 per year, or $17.3 per hour.

Internship - Machine Learning Engineer

Smule

Salt Lake City, UT • Remote

Other

Re-posted 19 days ago

Job description

Smule has been on a mission to bring the world together through music since 2008. Music is much more than listening… it's about creating, sharing, discovering, participating, and connecting with people. With dozens of millions of monthly active users creating over 20 million songs every day, Smule is connecting people all over the world through the joy of making music and transforming the music landscape from one of passive listening to collaborative creative expression and active engagement.


About the Role:

We are looking for a Machine Learning Engineer to own the end-to-end lifecycle of ML models in production at Smule, from training and optimization through deployment, monitoring, and iteration. You will work closely with research scientists to bring models off the bench and into scalable, reliable systems that serve millions of users. The ideal candidate is a strong engineer first, with deep practical knowledge of ML systems, a passion for reliability, and an eye for performance.


We strongly encourage candidates with non-traditional backgrounds to apply. If your path into ML engineering came through backend systems, DevOps, audio software, data engineering, or another field, we want to hear from you.


What You'll Be Doing:

  • Design, build, and maintain production ML pipelines encompassing data ingestion, feature engineering, model training, evaluation, and deployment.
  • Optimize models for production constraints including latency, throughput, memory footprint, and cost, using techniques such as quantization, distillation, pruning, and efficient serving architectures.
  • Implement robust monitoring, alerting, and observability for deployed models, covering data drift, prediction quality, and system health.
  • Collaborate with research scientists to integrate new model architectures and training techniques into production systems with minimal friction.
  • Build and improve CI/CD pipelines for ML, including automated testing, validation gates, and staged rollouts.
  • Manage compute infrastructure and costs, making informed tradeoffs between performance, reliability, and budget.


What We're Looking For:

  • Degree (B.S., M.S., or Ph.D.) in Computer Science, Software Engineering, Electrical Engineering, or a related technical discipline, or currently pursuing one.
  • Strong proficiency in Python and experience with deep learning serving (TorchServe, Triton, vLLM, or equivalent).
  • Solid understanding of systems engineering: networking, storage, containerization, orchestration, and monitoring.
  • Ability to reason about tradeoffs between latency, throughput, cost, and model quality.


Bonus Points For:

  • Experience serving large language models or other generative models at scale.
  • Familiarity with audio/music processing pipelines and real-time inference constraints.
  • Experience with Bayesian optimization, bandit algorithms, or adaptive experimentation platforms.
  • Contributions to open-source ML infrastructure projects.


Smule is an Equal Opportunity Employer and considers all qualified applicants without regard to race, color, religion, sex, gender identity or expression, sexual orientation, national origin, ancestry, age, disability, medical condition, genetic information, marital status, military or veteran status, or any other protected characteristic under federal, state, or local law.


We are committed to creating an inclusive environment for all employees and applicants. If you require a reasonable accommodation during the application or interview process, please let us know.