AIML Engineer

Qubeaxis; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Performance bonus (up to 20% of base)

Equity participation

Health, dental, and vision insurance

$3,000 annual learning & development

Premium compute access (A100/H100)

Flexible hours and 25 days leave

Job summary

Qubeaxis is seeking an AI/ML Engineer to design, build, and ship intelligent systems that power our product and millions of users.

You will own the full ML lifecycle—from data ingestion and experimentation to model deployment and monitoring—collaborating with product managers, platform engineers, and data scientists to bring AI-powered capabilities to production at scale.

Qualifications

• Proven ability to write production-grade Python code and tests.
• Strong foundation in ML concepts and model evaluation.
• Hands-on work with transformers, prompts, and fine-tuning.
• Experience building retrieval-augmented systems with vector DBs.
• Familiarity with LangChain or similar orchestration tools.
• Cloud experience (AWS/GCP/Azure), Docker, and ML CI/CD.
• Good collaboration, code reviews, and documentation practices.

Responsibilities

• Design, build, and ship intelligent systems at scale.
• Own ML lifecycle from data ingestion to deployment and monitoring.
• Collaborate with PMs, platform engineers, and data scientists.
• Develop RAG pipelines and integrate vector databases.
• Advance model quality, bias mitigation, and evaluation workflows.

Skills

Python proficiency

ML fundamentals

LLM & NLP experience

RAG pipeline development

LLM frameworks

Cloud & MLOps basics

Version control & collaboration

Education

B.Tech/B.S./M.S. in CS/DS/Math

Tools

PyTorch

TensorFlow

HuggingFace

TorchServe

Kubernetes

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.