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Plenful
Plenful
51 – 200 Darbuotojai
Dirbtinis intelektasSaaSSveikatos priežiūra
Plenful builds AI workflow automation software for pharmacy and infusion teams. Its platform connects data across APIs, HL7, SFTPs, PDFs, and databases to handle administrative work such as 340B audit and optimization, referral capture, rebate management, Maximum Fair Price intelligence, prior authorization, and order intake. Plenful serves health systems, specialty pharmacies, infusion practices, and provider groups, with a focus on compliant operations, revenue capture, and reducing manual workload.
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Staff Machine Learning Engineer, Plenful (San Francisco, Hybrid)

Set production strategy for machine learning agents, model post-training, and evaluation. Build healthcare AI automation to reduce administrative work and strengthen compliance.

USJungtinės ValstijosCAKalifornijaHibridinisVisas etatasVadovasPrivaloma Kalba: anglųMašininio mokymosi inžinierius
DockerKubernetesPythonPyTorch

Aprašymas

  • Architect production agents across tool use, retrieval, state and context management, routing, orchestration, tracing, and failure recovery.
  • Turn successful approaches into reusable engineering patterns.
  • Choose whether to build or buy models and platform components.
  • Set the company’s post-training strategy.
  • Train and deploy LLMs and vision-language models for healthcare tasks.
  • Develop in-house capabilities for continued model training.
  • Create evaluations that demonstrate production improvements.
  • Define AI quality strategy across automated graders, calibrated LLM judges, regression suites, human review, failure taxonomies, and offline-to-online validation.
  • Base release decisions on evaluation results.
  • Build systems that use production failures and user feedback to improve datasets, evaluations, and models.
  • Set standards for dataset versioning, observability, and MLOps.
  • Shape the 12-month ML roadmap with engineering and product leaders.
  • Lead technical reviews, mentor senior engineers, and contribute to hiring.
  • Design for privacy through minimum necessary data, de-identified or synthetic data where appropriate, and HIPAA-aware practices.
  • Review agents, evaluations, and the training pipeline in the first 30 days, identifying the three largest gaps.
  • By day 90, deliver a post-training or evaluation improvement that moves a production metric.
  • By day 90, publish the team plan for the next two quarters.

Reikalavimai

  • At least 8 years in ML or software engineering, including 3+ years leading production LLM or agent systems.
  • End-to-end applied post-training experience, such as supervised fine-tuning, preference optimization, distillation, or reinforcement learning.
  • Experience shipping a model that improved a production metric.
  • Experience designing LLM or agent evaluation strategies that informed release decisions.
  • Experience architecting production agent systems.
  • Experience with LangGraph, RAG, and vector search tools such as Pinecone.
  • Strong Python and PyTorch skills.
  • Familiarity with distributed training and serving tools such as vLLM, FSDP, or DeepSpeed.
  • MLOps experience with Docker, Kubernetes, MLflow or equivalent, CI/CD, and experiment and dataset tracking.
  • Able to influence without formal authority through design documents, reviews, and mentoring.
  • Up-to-date knowledge of new models and coding agents.
  • Able to turn papers and releases into experiments and report quantified results.
  • Evidence of work others use, such as open-source contributions, write-ups, talks, or evaluations.
  • Preferred: healthcare data experience with EHR, FHIR, claims, or prior authorization.
  • Preferred: publications or open-source contributions in LLM evaluation, post-training, or agents.
  • Preferred: experience interviewing and hiring ML engineers.
  • Preferred: experience in another regulated, high-stakes field.

Privalumai

  • Flexible hybrid work with a remote-first approach and meaningful office presence in San Francisco and New York.
  • Full medical, dental, and vision coverage for employees, with family participation.
  • 401(k) with a company match of 50% of the first 3% contributed.
  • Equity for all full-time employees.
  • Unlimited paid time off.
  • $100 weekly lunch stipend.
  • $100 monthly wellness stipend.
  • $100 monthly commuter benefit for employees based in San Francisco and New York.
  • Paid parental leave.
  • Opportunities for professional growth.
  • Mission-driven team of experienced colleagues.
  • Collaboration with experienced, high-performing leaders.

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