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Valkai

Valkai

Agent platform for pharma and device workflows

Member of Technical Staff - Research

Full-TimeUpdated on 9/16/2026
$200k - $350k/yr

+ Equity

Mid
San Francisco, CA, USA+1 more

More locations: New York, NY, USA

In Person

About the job

Requirements
  • Understand machine learning, optimization, probability, and statistics well enough to reason about model behavior and experimental results.
  • Have hands-on experience training, adapting, or improving models and be able to explain the decisions behind that work, with depth in areas such as post-training or reinforcement learning.
  • Turn ambiguous problems into clear hypotheses, design experiments, and develop new approaches.
  • Use applied judgment to determine whether to improve the model, data, or surrounding system while weighing quality, reliability, latency, and cost.
  • Help define research direction, take responsibility for outcomes, and move from investigation to execution without a detailed plan.
  • Communicate findings and uncertainty clearly and collaborate with domain experts, research, engineering, and product.
Responsibilities
  • Own applied research projects from problem definition and experiment design through implementation and production validation.
  • Train and adapt models for reasoning, retrieval, and decision-making tasks.
  • Develop and test improvements to agent systems, including tool use, context, memory, planning, and recovery from failures.
  • Build datasets, evaluation environments, and grading methods that capture the difficulty of real workflows.
  • Inspect model outputs and agent traces, identify recurring failure modes, and turn those findings into changes to training, data, or system design.
  • Build experimentation tools and pipelines needed to run, compare, and reproduce research efficiently.
  • Partner with AI Strategy, product, and engineering to understand workflows and ship improvements to production.
Desired Qualifications
  • Experience carrying a research idea into a product or system used in real workflows.
  • Research publications, open-source contributions, or substantial industry work in machine learning, large language models, agents, or evaluation.
  • Experience with reward modeling, synthetic data, or learning from human feedback.
  • Experience with distributed training, inference optimization, or large-scale experimentation.
  • Experience working in a high-growth early-stage company.

About the company

Valkai builds an agent orchestration layer for pharmaceutical, biotech, and medical device companies, connecting the siloed systems, teams, and outside data that make development slow. Its agents run on an organization's own SOPs, precedents, and regulatory context, draw on more than 100 data sources, and execute multi-step workflows that end in review-ready documents with citations, covering everything from discovery through commercialization. The company says more than 200 agent systems coordinate work across those stages, and it deploys through private cloud or managed SaaS. Unlike a general-purpose assistant, the agents are bound to the procedures and evidence standards a regulated submission requires. The company's goal is to compress work that currently takes months into weeks by removing the handoffs between systems.

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Simplify Jobs

Simplify's Take

What believers are saying

  • Valkai’s site says it serves the world’s largest pharma, biotech, and med-device companies.
  • Open roles in New York and San Francisco signal active hiring in 2026.
  • The company targets regulated workflows, where compliance pain creates sticky enterprise adoption.

What critics are saying

  • Simplify shows no independent funding, customers, or partnerships beyond Valkai’s own site.
  • Valkaai’s €12 million pre-seed news creates name confusion and weakens investor credibility.
  • If pharma pilots stall, the company’s broad platform story becomes an existential sales trap.

What makes Valkai unique

  • Valkai sells an agent orchestration layer across R&D, clinical, regulatory, quality, manufacturing.
  • It claims 200+ agent systems and 100+ data sources, spanning the full lifecycle.
  • The team says it was built by OpenAI, Glean, and life-sciences veterans.

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Benefits

Health Insurance

Dental Insurance

Vision Insurance

Paid Vacation

Paid Parental Leave

Company Equity