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Executive Training Ai Models Jobs in Indiana (NOW HIRING)

Review and assess AI responses to contract scenarios, providing expert feedback to improve model ... Prior exposure to AI, legal tech, or training initiatives. Why Join: * This is an opportunity to ...

Review and assess AI responses to contract scenarios, providing expert feedback to improve model ... Prior exposure to AI, legal tech, or training initiatives. Why Join: * This is an opportunity to ...

Review and assess AI responses to contract scenarios, providing expert feedback to improve model ... Prior exposure to AI, legal tech, or training initiatives. Why Join: * This is an opportunity to ...

Review and assess AI responses to contract scenarios, providing expert feedback to improve model ... Prior exposure to AI, legal tech, or training initiatives. Why Join: * This is an opportunity to ...

Review and assess AI responses to contract scenarios, providing expert feedback to improve model ... Prior exposure to AI, legal tech, or training initiatives. Why Join: * This is an opportunity to ...

Review and assess AI responses to contract scenarios, providing expert feedback to improve model ... Prior exposure to AI, legal tech, or training initiatives. Why Join: * This is an opportunity to ...

Review and assess AI responses to contract scenarios, providing expert feedback to improve model ... Prior exposure to AI, legal tech, or training initiatives. * Experience at a corporate law firm in ...

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Infographic showing various Executive Training Ai Models job openings in Indiana as of June 2026, with employment types broken down into 93% Full Time, 5% Part Time, and 2% Contract. Highlights an 74% Physical, 3% Hybrid, and 23% Remote job distribution.

Advisor - Agent Research

Scorpion Therapeutics

Indianapolis, IN • On-site

$150 - $200/hr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 5 days ago


Job description

Responsibilities:

  • Partner with scientists to build autonomous agents for molecule discovery tasks.

  • Design and build reinforcement learning (RL) environments with appropriate state/action/termination semantics for discovery.

  • Curate and engineer reward functions from noisy scientific signal.

  • Post-train domain models (SFT, DPO/GRPO/PPO, reward modeling, distillation) on chemistry and biology tasks.

  • Integrate learned policies with domain tools (RDKit, molecular graph ML, ELN/LIMS APIs, instrument drivers) to execute real DMTA tasks.

  • Build evaluation infrastructure (task suites, scoring harnesses, regression/experiment tracking e.g., MLflow).


Basic Qualifications:

  • PhD (or MS + 3 yrs / BS + 5 yrs) in ML, Bioinformatics, Cheminformatics, Computer Science, or related field with demonstrated wet-lab collaboration/hands-on experience.

  • ~1–2 years applying AI/ML in scientific disciplines (biology, chemistry, neuroscience, etc.).

  • Hands-on experience training/post-training AI models.


Preferred Qualifications/Skills:

  • Python; deep learning frameworks (PyTorch, TensorFlow, JAX, HuggingFace).

  • RL and post-training methods (PPO, GRPO, DPO, reward modeling, RLHF/RLAIF) and libs (TRL, verl, or equivalents).

  • Molecular representation learning/generative chemistry/protein-nucleic acid models.

  • Agentic AI systems experience (OpenAI/Anthropic Agent SDK, LangChain, Smol agents).

  • Cloud end-to-end system experience (APIs/frontends/agent platforms); GitHub portfolio a plus.

  • Cloud-native pipeline knowledge (AWS/Azure), Nextflow/Argo on Kubernetes.

  • Research contributions/publications; mentoring experience.


Benefits:

  • Eligible for company bonus; 401(k), pension, vacation; medical/dental/vision/prescription; flexible benefits; life insurance; time off/leave; well-being benefits (EAP/fitness/clubs).

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