Staff/Senior ML Engineer
Role summary
This company is seeking Staff/Senior ML Engineers to design and build core systems for agentic AI platforms that accelerate scientific discovery. The role involves working with complex scientific data, developing modern architectures like transformers and foundation models, and implementing reasoning-driven workflows. Candidates should have strong ML fundamentals, end-to-end model building experience, proficiency in PyTorch or JAX, and robust engineering skills for building scalable systems. Experience with LLMs, reasoning systems, or distributed training is a plus. This is an onsite, highly collaborative role in San Francisco.
🔸 Principal / Staff / Sr ML Engineer | Agentic AI Systems for Science
🔸 Competitive Base + Meaningful Equity
🔸 Onsite (USA)
🔸 AI-First Scientific R&D Company
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*PLEASE FOLLOW THE KENKOTECH PAGE AND CONNECT WITH THE JOB POSTER*
We’re partnering with a well-funded, AI-first company building next-generation machine learning systems to accelerate scientific discovery.
Rather than applying ML to static problems, this team is developing systems that can reason over complex datasets, generate hypotheses, and guide real-world experimentation. Their work sits at the intersection of large-scale modeling, scientific data, and emerging agentic AI paradigms.
They are hiring Principal / Staff / Senior ML Engineers to help design and build the core systems that power this platform. This is a high-impact, hands-on role working directly on cutting-edge ML systems embedded in real-world scientific workflows.
🔸 What You Will Work On
- Design and build ML systems that operate over complex, real-world scientific data
- Develop and implement modern architectures (transformers, foundation models, and beyond)
- Work on multi-step, reasoning-driven workflows and emerging agentic AI approaches
- Diagnose model behavior, iterate quickly, and improve system performance from first principles
- Build infrastructure and pipelines for training, evaluation, and deployment at scale
🔸 What They’re Looking For
- Strong ML fundamentals with experience building and training models end-to-end
- Hands-on experience with modern deep learning architectures (e.g. transformers, sequence models, multimodal models)
- Experience working with large-scale datasets and training systems
- Strong engineering ability - comfortable building systems, not just models
- Strong experience with PyTorch and/or JAX
🔸 Bonus Skills
- Experience with LLMs, reasoning systems, or agentic workflows
- Experience with RL, RLHF, or preference-based training
- Experience building evaluation frameworks or benchmarks
- Background in scientific domains (biology, chemistry, physics) - not required
- Experience with distributed training and large-scale infrastructure
🔸 Location & Work Environment
This is an onsite role based in San Francisco. The team operates in a highly collaborative, in-person environment and values close interaction between engineering, research, and domain experts.
Interested candidates should apply via direct message or LinkedIn application.