Senior Machine Learning Engineer (Python, LLM, Pytorch)
Role summary
We are seeking a Senior Machine Learning Engineer to develop advanced search, ranking, and recommendation systems using modern AI/ML technologies. This role involves building large-scale, production-grade ML systems with a focus on Python, PyTorch, and LLMs. Responsibilities include designing and implementing ML pipelines, deploying models to production, and architecting agentic workflows. The ideal candidate will have expertise in vector databases, embeddings, and the full ML lifecycle, with a strong understanding of NLP and document extraction. Experience with agent frameworks and hybrid search models is a plus.
We are looking for a
Senior Machine Learning Engineer
to build cutting-edge
search, ranking, and recommendation systems
powered by modern AI/ML technologies. This role offers the opportunity to work on large-scale, production-grade ML systems with real-world impact.
💰 Compensation:
$180K – $220K Base + Benefits + Equity
📍 Location: Bay Area (Hybrid) | Relocation support available for strong candidates
🔧
Must-Have Skills
- Strong programming in
Python
with hands-on ML development
- Expertise in
PyTorch
and deep learning model training
- Experience with
LLMs, prompt tuning, and fine-tuning models
- Solid understanding of
search, ranking, and recommendation systems
- Hands-on with
vector databases & embeddings
(e.g., Pinecone, Milvus)
- Experience building
end-to-end ML pipelines & data handling
- Proven experience
deploying ML models to production
🚀
Key Responsibilities
- Design and build
search, ranking, and recommendation engines
- Develop
NLP and document extraction pipelines
- Architect
agentic / multi-agent workflows
for complex problem solving
- Build
scalable, production-ready ML solutions
- Work across the
full ML lifecycle
(design → deployment → monitoring)
- Collaborate with product, engineering, and leadership teams
🧠
Nice to Have
- Experience with
agent frameworks
(LangGraph, AutoGen, CrewAI)
- Knowledge of
embedding-based retrieval & hybrid search models
- Strong foundation in
data structures & algorithms
- Research or open-source contributions in ML/NLP
🎯
Interview Process
- Recruiter Screen
- Director of Engineering
- Hiring Manager
- Take-home Assignment + Panel Presentation