Research Engineer

Harnham; California (MO); On-site; USD 120,000 - 180,000; Full time

Job summary

Harnham is seeking a data-centric ML researcher to design multimodal datasets, run experiments, and build scalable data pipelines that power next-generation AI models. You will shape model capabilities by controlling data generation, filtering, and quality assurance, collaborating with cross-functional teams to translate product goals into data strategies.

Ideal candidates will have 4+ years in ML, experience with large multimodal datasets and generative models, and proficiency in PyTorch or

Qualifications

• 4+ years of experience in machine learning, ideally with a data-centric focus.
• Experience with large multimodal datasets and generative models.
• Strong intuition for how data quality and composition impact model behavior.
• Experience across the full ML lifecycle, from data to training to evaluation.
• Proficiency with ML frameworks such as PyTorch or JAX.
• Experience with distributed systems or compute tools (e.g., Ray, Kubernetes).
• Strong interest in advancing next-generation AI systems.

Responsibilities

• Design multimodal, multitask datasets to unlock new model capabilities.
• Run controlled experiments to understand how data impacts model performance.
• Build and scale pipelines for synthetic data generation, filtering, and quality control.
• Define evaluation frameworks and benchmarks to measure real-world model improvement.
• Partner with cross-functional teams to translate product goals into data strategies.

Skills

ML experience

Multimodal data

PyTorch/JAX

Distributed systems

Data lifecycle

Model evaluation

Experiment design

Tools

Ray

Kubernetes

Job description

We’re partnered with a well-funded AI research company focused on building next-generation multimodal models for media and interactive experiences. Their work spans cutting-edge generative systems and is increasingly moving toward real-time, interactive environments, pushing beyond static outputs into dynamic, AI-driven applications.

This is a high-impact, research-meets-engineering role focused on the data that powers advanced AI systems. You’ll own how models learn, designing datasets, running experiments, and building data pipelines that directly shape model capabilities across a wide range of applications.

What You’ll Do

• Design multimodal, multitask datasets to unlock new model capabilities
• Run controlled experiments to understand how data impacts model performance
• Build and scale pipelines for synthetic data generation, filtering, and quality control
• Define evaluation frameworks and benchmarks to measure real-world model improvement
• Partner with cross-functional teams to translate product goals into data strategies

Requirements

• 4+ years of experience in machine learning, ideally with a data-centric focus
• Experience working with large multimodal datasets and generative models
• Strong intuition for how data quality and composition impact model behavior
• Experience across the full ML lifecycle, from data to training to evaluation
• Proficiency with ML frameworks such as PyTorch or JAX
• Experience with distributed systems or compute tools (e.g., Ray, Kubernetes)
• Strong interest in advancing next-generation AI systems

Nice to Have

• Experience with synthetic data generation or data curation at scale
• Background working on multimodal or video-based models
• Exposure to evaluation and benchmarking for generative systems

  If you're interested in shaping the data that defines what AI models can learn and do, this is a unique opportunity to work at the forefront of multimodal AI.