- Published research, open-source contributions, or patents in agent training methods, LLM alignment, or applied ML
- Experience with online learning, continuous fine-tuning, or automated data/curriculum generation from production traces
- Experience with model or systems optimization (e.g., training efficiency, latency, cost, or inference efficiency at scale)
- Experience working in regulated or enterprise/government contexts
- Track record of taking a novel training method or agent architecture from prototype to something running reliably in production, navigating ambiguity along the way
- Prior experience as a technical lead setting direction across multiple teams or problem areas
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 and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, 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.
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:
$264,800—$331,000 USD
PLEASE NOTE: 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.
