Builds and productionizes AI models and systems for life sciences document generation, focusing on LLMs, NLP robustness, and reliable deployment pipelines. Bridges ML research, software engineering, and product needs in a high-stakes domain.
Salary not listed
On-siteML Engineering
About the role
What You’ll Do
Prompt optimization, red-teaming, and improving robustness of LLMs for life-science NLP applications within Collate’s products.
Build pipelines and infrastructure to deploy AI systems reliably, safely, and at scale.
Collaborate with product, design, and engineering teams to translate user needs into AI-driven features.
Develop evaluation frameworks to ensure models are accurate, fair, and trustworthy in real-world life science settings.
Experiment rapidly, while balancing iteration speed with the rigor required for high-stakes applications.
Create tools and workflows that make AI development more efficient across the team.
What We’re Looking For
Hands-on experience building and deploying ML/AI systems in production.
Strong foundation in deploying search and retrieval systems and NLP applications.
Ability to bridge research and engineering — from prototyping models to shipping them in user-facing products.
Comfort working in an early-stage startup where ambiguity is high and ownership is expected.
Motivation to apply AI to life sciences in a way that prioritizes reliability, safety, and impact.
Characterize, analyze, and optimize performance of state-of-the-art AI models on Cerebras' wafer-scale hardware. Build performance models, optimize kernels and compilers, debug runtime behavior, and develop visualization tools to influence next-gen AI architecture.
Salary not listed
On-site3+ YOEML Engineering
Research Engineer, Privacy
OpenAISan Francisco, CA
Research Engineer on OpenAI's Privacy team designing and prototyping privacy-preserving ML algorithms like differential privacy and federated learning at scale. Requires hands-on PETs experience, fluency in PyTorch/JAX, and a track record implementing or publishing novel privacy work.
380k – 445k/yr
HybridML Engineering
Research Engineer
ConsoleSan Francisco, CA
Research Engineer building self-improving AI agent systems at Console. Develop eval/optimization loops, fine-tune specialist models, and improve agent reasoning over enterprise context using production data to drive measurable gains in quality, latency, and reliability.
200k – 350k/yr
On-siteML Engineering
Software Engineer, AI Platform
NotionSan Francisco, CA +1
Build and scale the shared AI platform foundations at Notion, enabling fast and safe shipping of AI products. Requires experience with LLM/ML platforms, strong ownership, and comfort across backend, infrastructure, and product code.
180k – 201k/yr
Hybrid5+ YOEML Engineering
Machine Learning Engineer
LiftoffCalifornia
Machine Learning Engineer building statistical models, optimization systems, and experiments for mobile ad tech economics on the Revenue Engine team. Requires PhD in CS/ML/Economics and industry experience applying ML or economics at scale.