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Aiml Engineer Jobs in California (NOW HIRING)

Qubeaxis

AIML Engineer

San Francisco, CA · On-site

We are looking for a driven and detail‑oriented AI/ML Engineer to join our growing applied AI team. In this role, you will design, build, and ship intelligent systems that directly impact our ...

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Aiml Engineer information

What is the difference between Aiml Engineer vs Data Scientist?

AspectAiml EngineerData Scientist
Required CredentialsBachelor's in CS, AI, or related; knowledge of AIML, programmingBachelor's or higher in CS, Statistics, or related; expertise in data analysis, programming
Work EnvironmentDeveloping AIML chatbots, virtual assistants, AI applicationsAnalyzing data, building predictive models, data visualization
Industry UsageTech companies, AI startups, customer service automationFinance, healthcare, marketing, tech firms

While Aiml Engineers focus on creating AIML-based chatbots and AI applications, Data Scientists analyze data to derive insights and build models. Both roles require programming skills and work in tech-driven environments, but Aiml Engineers specialize in AIML language and chatbot development, whereas Data Scientists work with broader data analysis and machine learning techniques.

What do Aiml engineers do?

Aiml engineers develop and implement artificial intelligence and machine learning algorithms using AIML (Artificial Intelligence Markup Language) to create chatbots and conversational agents. They analyze user interactions, design dialogue systems, and often work with programming languages like Python or Java, as well as AI development tools. Their role involves testing, refining, and maintaining AI models to improve system performance and user experience.

What is Aiml engineer salary?

The salary of an AIML engineer typically ranges from $70,000 to $130,000 annually, depending on experience, location, and skill level. Senior roles or those with expertise in deep learning and natural language processing may earn higher salaries. Certifications in AI and machine learning can also influence compensation.

What are popular job titles related to Aiml Engineer jobs in California?

For Aiml Engineer jobs in California, the most frequently searched job titles are:

What job categories do people searching Aiml Engineer jobs in California look for?

The top searched job categories for Aiml Engineer jobs in California are:

What cities in California are hiring for Aiml Engineer jobs?

Cities in California with the most Aiml Engineer job openings:

Aiml Engineer jobs near you
Infographic showing various Aiml Engineer job openings in California as of August 2026, with employment types broken down into 87% Full Time, 8% Part Time, and 5% Contract. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution.

AIML Engineer

San Francisco, CA • On-site

Qubeaxis
See more jobs at Qubeaxisexternal

Other

Medical, Dental, Vision, PTO

Posted 25 days ago

Key responsibilities

  • Design, build, and ship intelligent systems that impact the product and users.

  • Work across the full ML lifecycle, including data ingestion, experimentation, deployment, and monitoring.

  • Collaborate with product managers, platform engineers, and data scientists to bring AI-powered capabilities to production at scale.

Job description

Join our AI-first team shaping next‑gen intelligent systems. Work on cutting‑edge models, agentic workflows, and real‑world ML deployments.

We are looking for a driven and detail‑oriented AI/ML Engineer to join our growing applied AI team. In this role, you will design, build, and ship intelligent systems that directly impact our product and millions of users. You will work across the full ML lifecycle — from data ingestion and experimentation to model deployment and monitoring — collaborating closely with product managers, platform engineers, and data scientists to bring AI‑powered capabilities to production at scale.

About the Team

Our AI Platform team builds the intelligence layer that powers every product decision — from personalized recommendations and real‑time fraud signals to generative AI features used by millions daily. We operate with a startup mindset inside a scaled organisation: fast cycles, high ownership, and a direct path from research prototype to production impact. You'll be embedded alongside senior engineers and researchers, with mentorship, access to compute, and a genuine culture of learning.

Required Skills & Qualifications
  • Python proficiency — Must Have; production‑quality code, OOP, async programming, and familiarity with testing frameworks (pytest).
  • Machine learning fundamentals — Must Have; solid grasp of supervised/unsupervised learning, model evaluation, bias‑variance tradeoff, and regularisation.
  • LLM & NLP experience — Must Have; hands‑on work with transformer architectures, prompt engineering, fine‑tuning (LoRA / QLoRA), and tokenisation.
  • RAG pipeline development — Must Have; experience building retrieval‑augmented systems with vector databases (Pinecone, Weaviate, or pgvector).
  • LLM frameworks — Must Have; practical knowledge of LangChain, LlamaIndex, or equivalent orchestration tools.
  • Cloud & MLOps basics — Must Have; experience with at least one major cloud (AWS / GCP / Azure), containerisation (Docker), and CI/CD for ML workflows.
  • Version control & collaboration — Must Have; Git, code review culture, and documentation practices.
Preferred Qualifications
  • B.Tech / B.S. / M.S. in Computer Science, Data Science, Mathematics, or a related field — or equivalent industry experience. Nice to Have
  • Experience with PyTorch or TensorFlow for custom model training and fine‑tuning on domain datasets. Nice to Have
  • Familiarity with Hugging Face ecosystem — Transformers, PEFT, Datasets, Evaluate libraries. Nice to Have
  • Exposure to multi‑modal models (vision‑language, speech, or document understanding). Nice to Have
  • Knowledge of model serving frameworks — TorchServe, BentoML, vLLM, or Triton Inference Server. Nice to Have
  • Published work, open‑source contributions, Kaggle Top placements, or demonstrable personal AI projects on GitHub. Nice to Have
  • Understanding of data privacy, responsible AI principles, and model interpretability (SHAP, LIME). Nice to Have
What We Offer
  • Competitive salary benchmarked against top‑quartile market data, reviewed bi‑annually.
  • Performance bonus (up to 20% of base) tied to individual and team milestones.
  • Equity participation through stock options vesting over a 4‑year schedule.
  • Health, dental, and vision insurance fully covered for employee + dependants.
  • $3,000 annual learning & development budget — conferences, courses, certifications.
  • Access to premium compute (A100 / H100 clusters) for research and experimentation.
  • Flexible working hours with a core collaboration window; 25 days annual leave.
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