Machine Learning Engineer

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

📍 San Francisco 🏢 Onsite ⏱️ 0–3 Years 🕒 Full-Time

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Role Summary

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

San Francisco

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.

Technology Stack

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.