Scientific AI

EducationPals; San Francisco, New York (CA, NY); Híbrido; USD 120 000 - 180 000; Tempo integral

Resumo da oferta

EducationPals in San Francisco is seeking researchers for Applied Research – Scientific AI. The role focuses on depth in reinforcement learning, agents, or post-training systems to help build an open superintelligence stack.

You will run experiments, craft reusable Python artifacts, and clearly document results. Experience in ML for science and reproducible research is valued, with a track record in publications or open-source work.

Qualificações

• Depth in RL, agents, or post-training systems.
• Hands-on experience building and debugging model-training loops.
• Strong experimental judgment with clear failure attribution.
• Evidence of exceptional work, including publications or open-source work.

Responsabilidades

• Design and run experiments to advance the open superintelligence stack.
• Build and share Python artifacts that researchers and engineers can run.
• Analyze results, compare baselines, and trace failures to causes.
• Communicate findings and adapt direction as evidence evolves.

Conhecimentos

RL

Agents

Post-training

Python

Experimental judgment

Publications

Open-source

Descrição da oferta de emprego

Applied Research - Scientific AI San Francisco; Berlin; London; New York; Remote Employment Type Applied Research OverviewApplication Own Your Intelligence Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team. Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale

• We are looking first for depth in RL, agents, or post-training systems and the ability to acquire the relevant scientific context.
• Prior work in autonomous research, ML for science, scientific computing, or reproducible computational research is useful but not required.
• Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely adjacent experimental loops.
• Strong experimental judgment: meaningful baselines, careful trace inspection, clear failure attribution, and a willingness to change direction when the evidence changes.
• Strong Python and the ability to build artifacts that other researchers and engineers can run, inspect, and extend.
• Comfort owning ambiguous problems and keeping partners and teammates current as you resolve them.
• Evidence of exceptional work
• We value publications, open-source systems, benchmarks, and shipped products as evidence of exceptional work. Show us something difficult you built, what failed, and how the evidence changed your approach.

Courses relevant to this posting

Relevance is based on skills and concepts, not a guarantee that you qualify.

Covered skills: AI fundamentals Python LLM evaluation

Skills to close: None listed

Build the skills for this job

Coming-soon courses matched to this posting: outline, waitlist, and skill path.

1. When Full Simulation Is Too Expensive

Identify problems where reduced order models add value and understand the tradeoffs between accuracy and speed.

2. Proper Orthogonal Decomposition for Basis Extraction

Extract low-dimensional bases from snapshot data that capture dominant system behavior.

3. Operator Inference for Dynamical Systems

Learn operators governing reduced dynamics from data while preserving structure.

Test surrogate models for accuracy, stability, and trustworthiness across parameter ranges.

5. Parametric Reduced Order Models

Build surrogate models that adapt to varying parameters, geometries, and boundary conditions.

6. Integration into Engineering Workflows

Deploy reduced order models for optimization, sensitivity analysis, and decision support.

Apply operator inference to fluid dynamics, structural mechanics, thermal systems, and energy applications.

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