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Bayesian Optimization Jobs (NOW HIRING)

... Bayesian Optimization. • Develop custom, complex solvers and hybrid algorithms leveraging open-source optimization frameworks. • Implement objective functions, constraint models, surrogate models ...

This role is a specialized technical position focused on applying AI + Physics into predictive modeling, experimental design, and Bayesian optimization to enable faster, more confident decisions in ...

Data Scientist

San Francisco, CA · Remote

$160K - $200K/yr

Graduate work in an optimization related field (e.g RL, Convex Optimization, Bayesian Optimization), either PhD or Advanced MS degree. * Comfortable with Python, Flask/Django, Pandas and Numpy

We combine sequence-based models and variational autoencoders (VAEs) with Bayesian optimization, using experimental data to rapidly design and refine proteins into impactful therapeutics. The Applied ...

Data Scientist

San Francisco, CA · On-site +1

$160K - $200K/yr

Graduate work in an optimization related field (e.g RL, Convex Optimization, Bayesian Optimization), either PhD or Advanced MS degree. * Comfortable with Python, Flask/Django, Pandas and Numpy

Lead adaptive experimentation - contextual bandit systems, Bayesian optimization, automated allocation beyond simple A/B tests. * Drive the platform roadmap with product, design, and data science.

Lead inverse design and model-based discovery efforts using Bayesian optimization, diffusion models, or related methods. * Collaborate with scientists to integrate domain knowledge into deep learning ...

Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.

Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.

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Bayesian Optimization information

What is the difference between Bayesian Optimization vs Data Scientist?

AspectBayesian OptimizationData Scientist
Primary FocusOptimizing complex functions and hyperparametersAnalyzing data, building models, deriving insights
Required SkillsStatistics, probability, machine learning, programmingStatistics, programming, data analysis, visualization
Work EnvironmentResearch labs, AI/ML teams, R&D departmentsBusiness, tech companies, consulting firms
Common ToolsPython, R, Bayesian libraries (e.g., GPy, scikit-optimize)Python, R, SQL, visualization tools

Bayesian Optimization is a specialized technique used within machine learning and AI to efficiently tune hyperparameters or optimize functions. Data Scientists often utilize Bayesian Optimization as part of their toolkit but have broader responsibilities, including data analysis, modeling, and reporting. While Bayesian Optimization focuses on optimization tasks, Data Scientists work on understanding and interpreting data to inform business decisions.

Infographic showing various Bayesian Optimization job openings in the United States as of May 2026, with employment types broken down into 92% Full Time, and 8% Part Time. Highlights an 67% Physical, 3% Hybrid, and 30% Remote job distribution.

Full-time

Posted 14 days ago


Job description

Overview:
Summary
Key Responsibilities
• Formulate complex optimization problems (nonlinear, nonconvex, stochastic, constrained, multi-objective).
• Build advanced optimization pipelines using:
oSciPy Optimize, PySwarms, mystic, pymoo, Bayesian Optimization.
• Develop custom, complex solvers and hybrid algorithms leveraging open-source optimization frameworks.
• Implement objective functions, constraint models, surrogate models, and penalty formulations.
• Integrate optimization techniques into ML workflows (hyperparameter tuning, black-box optimization, surrogate modeling).
• Conduct convergence, sensitivity, robustness, and stability analysis of optimization methods.
• Scale optimization systems using Python, distributed computing, and numerical acceleration.
• Communicate complex mathematical concepts to cross-functional audiences.
Required Qualifications
• Masters/PhD in Operations Research, Applied Mathematics, Computer Science, Engineering, or related quantitative field.
• Expertise in nonlinear, global, evolutionary, and multi-objective optimization (e.g., NSGA-II/III, CMA-ES, DE).
• Strong knowledge of Bayesian Optimization and Gaussian Process modeling.
• Deep mathematical foundation (numerical methods, probability, linear algebra).
• Proficiency in the Python scientific ecosystem (NumPy, SciPy, pandas, scikit-learn).
• Demonstrated ability to design custom solvers for high-dimensional, ambiguous, or poorly behaved optimization landscapes.