Machine Learning Engineer

Qubeaxis; San Francisco (CA); On-site; USD 130,000 - 180,000; Full time

Benefits offered by this job

Competitive salary guidance

Performance bonus up to 20%

Equity options

Health insurance for employee + depend

Learning & development budget

Cloud compute access for ML work

Flexible hours, 25 days leave

Job summary

Qubeaxis is seeking a Machine Learning Engineer to design, train, deploy, and optimize ML models that power predictions, personalization, automation, and intelligent product experiences at scale.

You will work across the full ML lifecycle—from data prep and feature engineering to model validation, deployment, monitoring, and retraining—collaborating with data scientists, backend engineers, and product teams to turn data into measurable product impact.

Qualifications

• Proven Python production experience with clean architecture and production-ready code.
• Strong understanding of supervised and unsupervised learning, feature engineering, cross-validation, metrics, and model selection.
• Proficient in data handling with pandas, NumPy, SQL, and working with structured and unstructured data.
• Experience with ML frameworks such as scikit-learn and at least one deep learning framework (PyTorch or TensorFlow).
• Ability to deploy models via APIs or batch pipelines using FastAPI, Flask, Docker, or similar tools.
• Knowledge of MLOps practices including model versioning, experiment tracking, CI/CD, monitoring, and retraining workflows.
• Familiarity with Git, version control, code reviews, and documentation practices.

Responsibilities

• Design, train, deploy, and optimize ML models that solve business problems.
• Handle data preparation, feature engineering, validation, and monitoring across the ML lifecycle.
• Collaborate with data scientists, backend engineers, and product teams to translate data into product impact.
• Maintain production-grade pipelines and improve model reliability through retraining and monitoring processes.

Skills

Python programming

ML fundamentals

Data handling

ML frameworks

Model deployment

MLOps basics

Git and collaboration

Education

B.Tech / B.S. / M.S. in Computer Science, Data Science, Statistics, Mathematics, or related field

Tools

scikit-learn

PyTorch/TensorFlow

Docker

FastAPI/Flask

Git

Job description

Join our data-driven team building machine learning systems that power predictions, personalization, automation, and intelligent product experiences at scale.

We are looking for a motivated Machine Learning Engineer to design, train, deploy, and optimize ML models that solve real business problems. In this role, you will work across the full ML lifecycle — from data preparation and feature engineering to model validation, deployment, monitoring, and retraining. You will collaborate closely with data scientists, backend engineers, and product teams to turn data into measurable product impact.

Job Title

Machine Learning Engineer

Job ID

20985

Location

Work Mode

Onsite

About the Team

Our ML Platform team builds intelligent systems that power recommendations, forecasting, ranking, personalization, automation, and decision support. We focus on shipping reliable machine learning solutions to production, with a strong emphasis on data quality, model performance, scalability, and measurable business outcomes. You will join a collaborative environment where experimentation, ownership, and continuous improvement are part of the daily workflow.

Required Skills & Qualifications

• Python programming Must Have — strong coding skills, clean architecture, and experience writing production-ready Python.
• Machine learning fundamentals Must Have — supervised/unsupervised learning, feature engineering, cross-validation, metrics, and model selection.
• Data handling Must Have — pandas, NumPy, SQL, data cleaning, preprocessing, and working with structured and unstructured data.
• ML frameworks Must Have — experience with scikit-learn and at least one deep learning framework such as PyTorch or TensorFlow.
• Model deployment Must Have — ability to deploy models through APIs or batch pipelines using FastAPI, Flask, Docker, or similar tools.
• MLOps basics Must Have — model versioning, experiment tracking, CI/CD, monitoring, and retraining workflows.
• Git and collaboration Must Have — version control, code review, and documentation practices.

Preferred Qualifications

• B.Tech / B.S. / M.S. in Computer Science, Data Science, Statistics, Mathematics, or related field. Nice to Have
• Experience with feature stores, model serving, or distributed training. Nice to Have
• Familiarity with cloud platforms such as AWS, GCP, or Azure. Nice to Have
• Knowledge of time-series forecasting, ranking, recommendation systems, or NLP. Nice to Have
• Exposure to ML monitoring tools, A/B testing, and model observability. Nice to Have
• Experience with notebooks, pipelines, and reproducible research workflows. Nice to Have
• Personal projects, Kaggle experience, or open-source contributions in ML. 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 cloud compute and ML tooling for training and experimentation.
• Flexible working hours with a core collaboration window; 25 days annual leave.

Job Overview

Job ID 20985

Job Title Machine Learning Engineer

Work Mode Onsite

Experience 0–3 Years

Ready to Apply?

Submit your resume and portfolio. Our team reviews every application personally.

Frequently Asked Questions

Yes. Engineers are eligible for an annual performance bonus of up to 20% of their base salary, calculated on a combination of individual OKR achievement and overall company performance. Additionally, we run a quarterly spot-bonus programme where managers can recognise exceptional contributions with immediate cash awards ranging from $500 to $5,000. Long-term incentives include stock option grants that vest over four years with a one-year cliff.

Our end-to-end hiring process is designed to be thorough yet respectful of your time. From initial application to final offer, the typical timeline is 3–4 weeks. Recruiter screens are scheduled within 3–5 business days of application review. The take-home assignment window is flexible (up to 7 days). The onsite loop is usually completed within 2 weeks of passing the phone screen. We commit to providing written feedback or a decision within 2–3 business days after each stage.

Absolutely — this role is explicitly scoped for 0–3 years of experience, which means we actively welcome recent graduates. What matters most is demonstrated ability: strong fundamentals, a solid portfolio of personal or academic ML projects, and the curiosity to learn fast. We run a structured onboarding programme for junior hires including a dedicated mentor, a 90-day ramp plan, and weekly check-ins with the engineering manager to ensure a smooth transition into production work.

This role is posted as onsite in San Francisco, CA and requires the ability to work from our office at least 4 days per week. We do sponsor H-1B visas and have experience transferring O-1 and TN visa holders. If you are located outside the US and require full relocation, we offer a relocation assistance package of up to $10,000 for international moves. We encourage international candidates who are willing to relocate to apply — please mention your visa status in the application form so our recruiting team can provide accurate guidance.