Senior AI/ML Engineer — Healthcare Language Models

Simulation 95; Menlo Park (CA); Hybrid; USD 180,000 - 260,000; Full time

Job summary

Simulation95 seeks a senior individual-contributor to advance model training, post-training, and rigorous experimentation. You will own data-to-evaluation pipelines alongside applied AI engineers and domain experts, delivering measurable improvements and reproducible checkpoints.

You will build fine-tuning and RL pipelines, prepare datasets with provenance, and establish baselines with controlled experiments to ensure robust results in real workflows. Hybrid work availability in Menlo Park.

Qualifications

• Strong Python and PyTorch skills, with practical experience training or fine-tuning substantial neural networks and explaining your technical choices.
• Understanding of optimization, model architectures, learning objectives, and the factors that affect training stability and generalization.
• Experience with reproducible ML workflows, dataset versioning, experiment tracking, checkpoint management, and reliable evaluation.
• Ability to debug across data, model, and compute layers, including practical experience with GPU workloads and distributed training.
• Evidence of independently delivering meaningful ML work, communicating results clearly, and changing your approach when experimental evidence warrants it.

Responsibilities

• Build supervised fine-tuning, preference optimization, and reinforcement learning pipelines for language models and AI agents, choosing methods that fit the problem.
• Prepare, validate, and version training datasets with documented provenance and clear separation of training, validation, and held-out evaluation data.
• Establish baselines and controlled experiments that distinguish useful capability gains from data leakage, benchmark overfitting, and measurement noise.
• Work with domain experts and environment engineers to develop feedback, preference data, reward signals, and tasks that reflect intended applications.
• Diagnose training instability, regressions, memory constraints, and throughput bottlenecks; collaborate with infrastructure engineers to use GPU resources effectively.
• Deliver documented checkpoints, reproducible configurations, evaluation findings, and known limitations; verify improvements in representative workflows with applied AI engineers.

Skills

Python

PyTorch

ML training

Experiment tracking

GPU training

Debugging

Communication results

Job description

Simulation95 seeks a senior individual-contributor to advance model training, post-training, and rigorous experimentation. You will own data-to-evaluation pipelines alongside applied AI engineers and domain experts, delivering measurable improvements and reproducible checkpoints.

You will build fine-tuning and RL pipelines, prepare datasets with provenance, and establish baselines with controlled experiments to ensure robust results in real workflows. Hybrid work availability in Menlo Park.