Applied Researcher

River AI Inc.•Palo Alto, CA

8h•$200,000 - $420,000

About The Position

River is seeking applied researchers to work directly with customers across industries to train and improve models. The role translates real-world problems, domain expertise, and customer data into models that deliver measurable value in customer workflows. The researcher owns the applied research lifecycle, from defining problems and preparing data through designing experiments, training models, and evaluating results. In collaboration with customer teams and River’s research and infrastructure teams, the researcher applies and adapts modern deep learning techniques to practical problems and helps customers bring models into production.

Requirements

BS, MS, or Ph.D. in Computer Science, Machine Learning, Mathematics, or equivalent practical industry experience.
Strong understanding of modern deep learning architectures and practical experience training, fine-tuning, and evaluating language or multimodal models.
Extensive hands-on experience with PyTorch or JAX and a track record of writing clean, scalable code for model training and experimentation.
Proven ability to turn open-ended problems and imperfect real-world data into working models, using rigorous experiments and evaluation to guide decisions.
Strong communication and collaboration skills, including the ability to work directly with customers, learn new domains, and explain technical tradeoffs to technical and non-technical audiences.
Collaborative mindset, ownership of outcomes, and bias for action in a fast-paced environment.

Nice To Haves

Experience delivering models for customer or industry use cases, with measurable improvements in model performance or business outcomes.
Expertise in reinforcement learning, preference optimization, synthetic data generation, or adapting models to specialized domains.
Experience with distributed training, experiment management, or deploying and monitoring models in production.
Experience partnering with domain experts and working with proprietary or sensitive datasets under customer privacy and security requirements.

Responsibilities

Partner with customers across industries to understand workflows, identify valuable modeling problems, and define success criteria.
Develop data preparation and curation pipelines with customer teams, turning domain-specific data and expert feedback into training datasets and representative evaluations.
Design and run training experiments, selecting techniques such as continued pre-training, supervised fine-tuning, reinforcement learning, and parameter-efficient fine-tuning (PEFT) to fit customer goals, data, and compute budgets.
Build evaluation suites, analyze model failures, and iterate on data and training methods to improve quality, reliability, and performance on customer tasks.
Work with customer engineers and River’s infrastructure team to scale training runs, resolve bottlenecks, and support deployment and ongoing model improvement.
Turn lessons from customer engagements into reusable training recipes, evaluation tools, and improvements to River’s platform.