Research Systems Engineer
Applied Compute8 months ago
Base Salary
$190k - $310k/yr
Responsibilities
- Post-train frontier-scale language models on enterprise tasks and environments.
- Explore and develop reinforcement-learning techniques while co-designing algorithms and systems.
- Build high-performance internal tools for probing, debugging, and analyzing training runs.
- Partner with infrastructure engineers to scale training and inference efficiently.
- Design and run experiments that answer meaningful research questions.
Requirements
- Experience training or serving large language models.
- Experience building reinforcement-learning environments and evaluations for language models.
- Proficiency in PyTorch, JAX, or similar machine-learning frameworks, with distributed-training experience.
- Strong experimental design skills.
- Preferred: background in pre-training or post-training research.
- Preferred: experience in high-performance computing environments or large-scale clusters.
- Preferred: contributions to open-source machine-learning research or infrastructure.
- Preferred: demonstrated technical creativity through published research, open-source contributions, or side projects.
Benefits
- San Francisco office-based role working from the Mission office.
- Competitive compensation and equity.
- 100% employer-paid health insurance coverage for the employee and family.
- Unlimited PTO.
- Paid parental leave.
- Daily lunches and dinners.
- Transportation and relocation support.
- 5% 401(k) match.
- Visa sponsorship with a commitment to work through the process for the right fit, without guaranteeing success for every candidate or role.
Tech Stack
Categories
AI Research
About Applied Compute
Applied Compute builds enterprise AI systems that turn internal knowledge into custom models and deployable agent workforces. Its platform and forward-deployed teams create evals, train models on proprietary data, and run continually learning agents inside customer environments. The company is privately held and headquartered in San Francisco, serving enterprises that want in-house, domain-specific AI rather than generic foundation models.