Senior AI/ML Engineer
At a glance
Senior Machine Learning Engineering role at Clera. San Francisco · full-time · no salary stated · we estimate $208,750–$272,683.
Clera has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Build ML systems from data preparation through training, testing and deployment
- Tune production models by balancing prediction quality, response time and operating cost
- Create evaluation tools and offline benchmarks to speed up model iteration
- Turn unclear product goals into measurable model gains, and coach engineers on ML practices and code quality
What you bring
- At least four years building and operating machine-learning systems in production
- Practical work with LLMs, model fine-tuning, retrieval-augmented generation or large recommender systems
- Strong Python and PyTorch skills, or a solid foundation in JAX
- Experience in distributed model training, GPU tuning or serving model predictions
Who this fits
This is a senior, hands-on engineering role with mentoring responsibilities. You’ll need to work on-site in San Francisco.
From the employer
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.