Applied Research Engineer
Applied Compute1 year ago
Base Salary
$200k - $320k/yr
Responsibilities
- Own customer engagements end-to-end, from understanding data, workflows, and requirements through building and deploying agents.
- Train and post-train enterprise models, including environment design and deployment.
- Work directly with technical and business stakeholders across customer organizations.
- Navigate unfamiliar customer architectures, data systems, and requirements.
- Bring customer learnings back to improve the company’s general agents and platform.
- Collaborate with research, product, and infrastructure teams on architectures, evaluation frameworks, and best practices.
Requirements
- Meaningful AI research experience in industry or academia.
- Ability to work in unfamiliar codebases and train models on messy real-world data.
- Strong problem-solving skills in ambiguous and rapidly changing environments.
- Ability to work directly with customers and understand how their organizations operate.
- Desire to own problems end-to-end and deliver outcomes.
- Preferred qualifications include production experience training or fine-tuning language models, building RL environments, evaluation frameworks, or agent systems.
- Preferred qualifications include founder or early-stage zero-to-one company experience, technical side projects, open-source contributions, published work, or experience in forward-deployed or customer-facing engineering cultures.
Benefits
- The role is full-time and based in San Francisco, with work from the company’s Mission office.
- U.S. base salary is $200,000–$320,000, with equity and a comprehensive benefits package.
- 100% employer-paid health insurance is provided for the employee and family.
- Unlimited PTO and paid parental leave are offered.
- Daily lunches and dinners are provided.
- Transportation and relocation support are available.
- The company provides a 5% 401(k) match.
Categories
Forward Deployed
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.