Research Scientist

The Role

As a research scientist, you will design, implement, and optimize our frontier reinforcement learning stack as well as the recipes that we run on it. This work sits where algorithms and systems meet—we believe that the best results come from people who can move between them seamlessly. Algorithmic ideas only matter if they run reliably at scale. Systems only matter if they make models learn better and faster. Working closely with other researchers, you’ll close that loop: algorithmic advances get tested at scale, systems work gets judged by what it does for learning, and what you learn goes straight into the recipes and infrastructure our customers rely on.

What you’ll do

• Design, run, and analyze RL experiments, scoping them carefully to separate real algorithmic gains from noise

• Develop and improve post-training algorithms, reasoning about why they work (or don’t) and diagnosing failures like instability, reward hacking, and collapsing exploration

• Design and optimize our RL training and inference pipelines across large GPU clusters

• Understand how systems choices affect learning (low-precision numerics, off-policy staleness, distributed training edge cases, etc.)

• Partner with applied researchers to bring frontier post-training capabilities into production deployments

What we’re looking for

• A strong foundation in probability, statistics, machine learning, and reinforcement learning, including policy gradient methods, variance reduction, and the tradeoffs between on-policy and off-policy training

• The ability to learn quickly and navigate ambiguous or underspecified problem settings

• Fearlessness and curiosity to understand things, with an eye for details in specific algorithms or the training stack

• Experience programming with and managing training jobs on large-scale GPU systems

• A bias toward fast implementation, paired with a high bar for rigor, reliability, and efficiency

• Familiarity with open-weights models (architecture and inference)

• Background in reinforcement learning or integration of inference with RL training loops

Strong candidates also have

• Published research or substantial projects in RL, post-training, or LLM reasoning and alignment

• Demonstrated technical creativity through published projects, OSS contributions, or side projects

• Experience with distributed training frameworks (PyTorch, JAX, DeepSpeed)

• Background in high-performance computing or working with large-scale clusters

• Contributions to open-source ML infrastructure

The U.S. base salary range for this full-time position is $200,000–$335,000. Total compensation for this role also includes meaningful equity in a fast-growing startup, along with a comprehensive benefits package designed to support you both personally and professionally. Base salary is determined by a range of factors including individual qualifications, experience, skills, interview performance, market data, and work location. The listed salary range is intended as a guideline and may be adjusted.

Benefits & Logistics

This role is based in San Francisco. We work from our office in the Mission. We offer:

• Competitive compensation and equity

• 100% employer-paid health insurance coverage for you and your family

• Unlimited PTO

• Paid parental leave

• Daily lunches and dinners

• Transportation and relocation support

• 5% 401(k) match

We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the process with you. We encourage you to apply even if you do not believe you meet every single qualification. As set forth in Applied Compute’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.