Research Scientist - World Modeling

Percepta

• $120K — $145K *
Technical Services
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • MS/PhD in Computer Science, ML, or a related field, or equivalent experience
  • Expertise in LLM and ML fundamentals, including optimization and large-scale training
  • Experience with Reinforcement Learning (RL)
  • Ability to implement and debug large-scale ML systems
  • Motivated by impact in critical industries like healthcare and finance
  • Strong communication skills for both technical and non-technical audiences
  • Demonstrated ability to take extreme ownership of projects

Responsibilities

  • Identify impactful problems and design research strategies for LLM and RL
  • Prototype and scale training pipelines for large language models
  • Contribute to infrastructure for high-performance distributed training
  • Evaluate models in real-world settings to drive significant business value
  • Collaborate with Applied AI engineers to translate research into features
  • Communicate research outcomes effectively to diverse stakeholders

Benefits

  • Opportunity to work on groundbreaking AI projects in critical industries
  • Collaborative work environment with embedded product teams
  • Access to advanced AI toolkit and infrastructures
  • Engagement with strategic partners including Anthropic and AWS
  • Chance to make a significant impact in transformative technology
Full Job Description
About the role

As a Research Scientist - World Modeling at Percepta, you'll build the systems that let us understand an operation at its full complexity. Real operations rarely arrive as clean, structured data: the ground truth about the operation lives scattered across claims, clinical notes, call transcripts, contracts, and the tacit judgment of operators. You'll build models and agents that learn to compress this mess into a tractable, continuously-updated representation - effectively a digital twin of the operation - that forecasters, user models, and optimizers downstream can all reason and plan against.

Responsibilities
  • Design and build learned world models that compress messy, multi-modal operational data (notes, transcripts, contracts, telemetry) into tractable, decision-relevant representations.
  • Model the transition dynamics of real operations - how a workforce, facility, or network evolves state-to-state - so downstream systems can run counterfactuals before a decision touches a real person or asset.
  • Build and validate digital twins of customer operations, benchmarked against replayable, real-world testbeds with defensible ground truth.
  • Partner closely with the forecasting and optimization teams to ensure your representations are the right substrate for calibrated predictions and for finding optimal actions.
  • Bridge research into practice by partnering with engineers to deploy world models into live customer environments and push toward end-to-end production systems.

You may be a good fit if you:
  • PhD degree in Computer Science, Operations Research, Industrial Engineering, or Applied Mathematics or have equivalent research/industry experience.
  • Have depth in simulation or world modeling
  • Have experience in novel machine learning techniques for control and optimization, including test-time search and reinforcement learning for sequential decision-making.
  • Are comfortable implementing and debugging large-scale optimization systems, and designing benchmarks with real, defensible ground truth.
  • Are motivated by impact in critical industries including healthcare, supply chains, energy, and finance.
  • Have a proven track record of execution.
  • Are an excellent communicator with both technical and non-technical stakeholders.
  • Enjoy extreme ownership.
  • Are passionate about AI's transformative potential

We're working against an incredibly ambitious mission. It won't be easy, but it will likely be the most fulfilling work of your career. If this excites you, let's chat, even if you don't meet all of the qualifications above.

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