Machine Learning Research Engineer, GenAI Applied ML
Listed on 2026-04-20
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Software Development
AI Engineer, Data Scientist, Machine Learning/ ML Engineer
About This Role
Lead applied ML engineering on Scale's Applied ML team, powering data infrastructure for leading agentic LLMs (ChatGPT, Gemini, Llama). You will build scalable multi-agent systems to validate agentic reasoning and behaviors, scale human expertise, and drive research into real‑world agent reliability failures despite strong benchmarks, shipping production fixes.
Ideal for exceptional engineers with deep research rigor and a relentless focus on practical, high‑impact systems. You will iterate rapidly with data, leverage AI tools to accelerate development, and collaborate tightly across engineering, product, and research.
If you excel at turning frontier agent research into reliable deployed systems, we want to hear from you.
You will- Build and deploy multi-agent systems for agentic reasoning validation
- Develop pipelines to detect errors and scale human judgment
- Combine classical ML, LLMs, and multi-agent techniques for reliability
- Lead research into agent failure modes and ship fixes
- Use AI tools to speed prototyping and iteration
- Build data‑driven evaluations and deploy rapid improvements
- Integrate systems into Scale's platform
- PhD or MSc in Computer Science, Mathematics, Statistics, or related field
- 3+ years shipping scaled production ML systems
- Demonstrated real‑world impact
- Mastery of PyTorch, Tensor Flow, JAX, or scikit‑learn
- Deep expertise in agentic LLMs and multi‑agent systems
- Strong software engineering and microservices (AWS/GCP)
- Rapid, data‑driven iteration
- Proficiency using AI tools to accelerate work
- Strong research depth with practical bias
- Excellent cross‑functional communication
- Experience prototyping agent evaluation/reliability systems
- Human‑in‑the‑loop or annotation pipeline work
- Open‑source contributions in agents, evaluation, or alignment
- Publications on agent reliability (NeurIPS, ICML, ICLR)
Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job‑related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval.
Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to:
Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.
Base salary range for this full‑time position in the locations of San Francisco, New York, Seattle: $189,600 — $237,000 USD.
Equal Employment Opportunity and Accessibility StatementWe believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.
We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at Please see the United States Department of Labor's Know Your Rights poster for additional information.
We comply with the United States Department of Labor's Pay Transparency provision.
Employment Policy NoteOur policy requires a 90‑day waiting period before reconsidering candidates for the same role.
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