Senior AI/ML Engineer

Clera; San Francisco (CA); On-site; USD 130,000 - 160,000; Full time

Job summary

Clera is seeking a Senior AI/ML Engineer to join their well-funded AI startup based in San Francisco. This full-time on-site role requires designing and shipping end-to-end machine learning systems, handling model performance, and mentoring other engineers on best practices.

The ideal candidate has 4+ years of experience in production environments with strong skills in Python and frameworks like PyTorch or JAX. Work directly with product teams to translate goals into measurable improvements.

Qualifications

• 4+ years of applied ML engineering experience.
• Experience with large-scale recommender systems.
• Strong understanding of GPU optimization.

Responsibilities

• Design and ship end-to-end ML systems including data pipelines.
• Own model performance in production environments.
• Mentor engineers on ML best practices.

Skills

Applied ML engineering in production environments

Hands-on experience with LLMs

Strong Python fundamentals

Experience with distributed training

Tools

PyTorch

JAX

Job description

About the role

Our client is a well‑funded AI startup building production‑grade ML infrastructure used by enterprise customers. They are looking for a Senior AI/ML Engineer to own model training pipelines, evaluation systems, and inference serving at scale. Full‑time, on‑site in San Francisco.

What you will do

• Design and ship end-to-end ML systems: data pipelines, training, evaluation, deployment
• Own model performance, latency, and cost trade‑offs in production
• Build evaluation harnesses and offline benchmarks for fast iteration
• Work directly with product to translate ambiguous goals into measurable model improvements
• Mentor other engineers on ML best practices and code quality

What we are looking for

• 4+ years of applied ML engineering in production environments
• Hands‑on experience with LLMs, fine‑tuning, RAG, or large‑scale recommender systems
• Strong Python and PyTorch (or JAX) fundamentals
• Experience with distributed training, GPU optimization, or inference serving
• Pragmatic about trade‑offs between research‑grade and ship‑grade work

This role is presented by a recruiting partner. Company name shared after an initial conversation.