
Applied Researcher
Palo Alto, CAJob$200–420K/yrPosted 1 day agoStill listed today
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At a glance
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Job overview
River AI is seeking an applied researcher to work with customers across industries to train and improve models for real-world workflows. The researcher will own the applied research lifecycle, from problem definition and data preparation through experimentation, training, and evaluation. They will collaborate with customer teams and River’s research and infrastructure teams to adapt deep learning techniques, support production deployment, and turn engagement lessons into reusable tools and platform improvements.
Skills & qualifications
Skills
Qualifications
Benefits
Full job description
At River, our mission is to create personal AI owned and shaped by each individual. To achieve this, we are rewriting the entire stack from scratch: personal hardware for local inference, bespoke training infrastructure, next-generation UIs, and frontier deep learning research.
Who we are
We are scientists, engineers, and builders from the industry's top tech companies and AI labs. We bring a proven track record of scaling consumer systems for hundreds of millions of users and architecting the pre-training infrastructure behind today's frontier models.
About the Role
We are looking for exceptional applied researchers to work directly with customers across industries to train and improve their own models. Your goal is to translate real-world problems, domain expertise, and customer data into models that deliver measurable value in their workflows.
You will own the applied research lifecycle, from defining the problem and preparing data to designing experiments, training models, and evaluating results. Working closely with customer teams and River's research and infrastructure teams, you will apply and adapt modern deep learning techniques to solve practical problems and help customers bring their models into production.
What You’ll Do
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Partner directly with customers across industries to understand their workflows, identify high-value modeling problems, and define success criteria.
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Develop data preparation and curation pipelines with customer teams, turning domain-specific data and expert feedback into training datasets and representative evaluations.
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Design and run training experiments using techniques such as continued pre-training, supervised fine-tuning, reinforcement learning, and parameter-efficient fine-tuning (PEFT), selecting approaches that fit each customer's goals, data, and compute budget.
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Build evaluation suites, analyze model failures, and iterate on data and training methods to improve quality, reliability, and performance on real customer tasks.
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Work with customer engineers and River's infrastructure team to scale training runs, resolve bottlenecks, and support deployment and ongoing model improvement.
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Turn lessons from customer engagements into reusable training recipes, evaluation tools, and improvements to River's platform.
Skills & Qualifications
Minimum Qualifications:
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BS, MS, or Ph.D. in Computer Science, Machine Learning, Mathematics, or equivalent practical industry experience.
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Strong understanding of modern deep learning architectures and practical experience training, fine-tuning, and evaluating language or multimodal models.
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Extensive hands-on experience with PyTorch or JAX, with a track record of writing clean, scalable code for model training and experimentation.
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Proven ability to turn open-ended problems and imperfect real-world data into working models, using rigorous experiments and evaluation to guide decisions.
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Strong communication and collaboration skills, with the ability to work directly with customers, learn new domains, and explain technical tradeoffs to technical and non-technical audiences.
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A highly collaborative mindset, ownership of outcomes, and a bias for action in a fast-paced environment.
Preferred Qualifications: (We encourage you to apply even if you don't meet all of these)
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Experience delivering models for customer or industry use cases, with measurable improvements in model performance or business outcomes.
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Expertise in reinforcement learning, preference optimization, synthetic data generation, or adapting models to specialized domains.
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Experience with distributed training, experiment management, or deploying and monitoring models in production.
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Experience partnering with domain experts and working with proprietary or sensitive datasets under customer privacy and security requirements.
Logistics & Benefits
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Location: Palo Alto, California.
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Compensation: Depending on experience and skills the expected base pay is $200,000 - $420,000 USD per year, plus equity.
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Benefits: Comprehensive health, dental, and vision insurance; unlimited PTO; and relocation assistance as needed.
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Visa Sponsorship: We sponsor visas and are committed to supporting the process for the right candidate.
How this job compares
Set against the 580 open Machine Learning Researcher jobs on Olive Jobs, counted November 6, 2026.
- Pay
- This posting$200–420K/yr
- California median$184–300K/yr161 salary ranges
- US median$172–257K/yr353 salary ranges
This range's midpoint, $310K, is 30% above the California median midpoint ($239K) — in the top quarter of Machine Learning Researcher salary ranges in California.
- Openings
- Olive Jobs lists 278 open Machine Learning Researcher jobs in California and 580 across the US. 7% of them nationwide are listed as remote or flexible.
- Experience
- Machine Learning Researcher postings that state a minimum ask for 3 years of experience at the median; the middle half ask for 2 to 5 years (216 postings).
- Education
- 238 of the 580 open Machine Learning Researcher jobs (41%) ask for a bachelor's degree or higher.
Counted across the open, public US postings on Olive Jobs on November 6, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.
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