AI/ML Engineer
Info Way Solutions LLC
- Ort
- San Francisco, US
- Arbetsplats
- På plats
- Anställningsform
- Heltid
- Lön
- $130k
- Publicerat
- I dag
- Källa
- DevITJobs
Requirements:
- At least 2 years of experience in machine learning and/or data science, including a track record of putting ML systems into production.
- Strong knowledge of machine learning and deep learning libraries such as Scikit-learn, Keras, TensorFlow, PyTorch, Theano, or DyLib.
- Deep expertise in at least one of the following areas: search, retrieval, ranking, or recommendation systems; generative AI product development; LLM agent products; or LLM fine-tuning.
- Practical understanding of LLM limitations, including bias, hallucinations, and knowledge cutoffs.
- Experience at an early-stage startup or launching a product from zero to one.
- Ability to contribute to both research prototypes and production-grade engineering.
- Local Bay Area candidates are sought. Preferred colleges include MIT, UC Berkeley, Santa Clara University, Stanford, California State University, Harvey Mudd, and the University of San Francisco.
- The role is aimed at junior-to-mid-level candidates, with a preference for top-performing applicants.
Responsibilities:
- Design, train, and deploy machine learning models for large-scale production use.
- Develop LLM-based systems, including fine-tuning, agents, and evaluation pipelines.
- Partner with engineering and product teams to scope problems, establish success metrics, and create robust evaluation frameworks.
- Address LLM challenges such as bias, hallucinations, and knowledge cutoffs to improve system reliability and performance.
- Optimize models for latency, accuracy, and scalability in real-world applications.
- Contribute throughout product development, from research and deployment to ongoing iteration.
Technologies:
- AI
- Fine-tuning
- Keras
- LLM
- Machine Learning
- PyTorch
- TensorFlow
More
We are hiring for a full-time, direct-hire AI/ML Engineer role based in Santa Clara, California. This opportunity suits someone who wants to build and deploy machine learning models at scale in an environment where research quickly becomes real-world systems.
last updated 44 week of 2026