Machine Learning Research Engineer, GenAI Applied ML

Scale
San Francisco, CA; New York, NYPosted 23 February 2026

Job Description

<h1>About This Role</h1> <p>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.</p> <p>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.</p> <p>If you excel at turning frontier agent research into reliable deployed systems, we want to hear from you.</p> <p><strong>You will:</strong></p> <ul> <li>Build and deploy multi-agent systems for agentic reasoning validation</li> <li>Develop pipelines to detect errors and scale human judgment</li> <li>Combine classical ML, LLMs, and multi-agent techniques for reliability</li> <li>Lead research into agent failure modes and ship fixes</li> <li>Use AI tools to speed prototyping and iteration</li> <li>Build data-driven evaluations and deploy rapid improvements</li> <li>Integrate systems into Scale's platform</li> </ul> <p><strong>Ideally You’ll Have: </strong></p> <ul> <li>PhD or MSc in Computer Science, Mathematics, Statistics, or related field</li> <li>3+ years shipping scaled production ML systems</li> <li>Demonstrated real-world impact</li> <li>Mastery of PyTorch, TensorFlow, JAX, or scikit-learn</li> <li>Deep expertise in agentic LLMs and multi-agent systems</li> <li>Strong software engineering and microservices (AWS/GCP)</li> <li>Rapid, data-driven iteration</li> <li>Proficiency using AI tools to accelerate work</li> <li>Strong research depth with practical bias</li> <li>Excellent cross-functional communication</li> </ul> <p><strong>Nice to Have: </strong></p> <ul> <li>Experience prototyping agent evaluation/reliability systems</li> <li>Human-in-the-loop or annotation pipeline work</li> <li>Open-source contributions in agents, evaluation, or alignment</li> <li>Publications on agent reliability (NeurIPS, ICML, ICLR)</li> </ul><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p><em>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.</em></p></div><div class="title">Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:</div><div class="pay-range"><span>$176,000</span><span class="divider"></span><span>$220,000 USD</span></div></div></div><div class="content-conclusion"><p><strong><em>PLEASE NOTE: </em></strong><em>Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.</em></p> <p><strong>About Us:</strong></p> <p><em>At Scale, our mission is to develop reliable AI systems for the w ... (truncated, view full listing at source)
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