Machine Learning Engineer (US)

Machine Learning Engineer

Experience: 2–8 Years

Location: San Francisco, CA | Seattle, WA | New York, NY | Boston, MA

Salary: $120,000–$250,000+ per year

Job Type: Full-Time

About the Role

We are seeking a talented Machine Learning Engineer to design, build, deploy, and optimize machine learning solutions that solve real-world business and technical problems. You’ll work across data, model development, software engineering, and MLOps to bring ML models from experimentation into reliable production systems.

Key Responsibilities

- Develop, train, evaluate, and optimize machine learning models.

- Build scalable ML pipelines for data preparation, training, validation, and deployment.

- Productionize ML models and monitor performance, reliability, and model drift.

- Implement MLOps practices for automated model deployment and lifecycle management.

- Collaborate with Data Scientists, Data Engineers, Software Engineers, and Product teams.

- Improve model accuracy, scalability, latency, and overall system performance.

- Design APIs and services for integrating ML models into production applications.

- Implement testing, CI/CD, version control, and automated ML workflows.

- Troubleshoot production ML systems and continuously improve reliability.

- Stay current with emerging machine learning, MLOps, and AI technologies.

Required Qualifications

- 2–8 years of experience in Machine Learning Engineering, Software Engineering, Data Science, or a related field.

- Strong proficiency in Python and experience with ML libraries such as scikit-learn, PyTorch, or TensorFlow.

- Solid understanding of machine learning algorithms, statistics, model evaluation, and optimization.

- Hands-on experience building and deploying ML models into production.

- Experience with MLOps, ML pipelines, model monitoring, and CI/CD.

- Strong SQL and data-processing skills.

- Experience with cloud platforms such as AWS, Azure, or Google Cloud.

- Strong software engineering, debugging, and problem-solving skills.

Preferred Qualifications

- Experience with Kubernetes, Docker, Airflow, MLflow, Kubeflow, or similar tools.

- Experience with feature stores and model-serving platforms.

- Knowledge of distributed computing and large-scale data processing.

- Experience with Generative AI, LLMs, RAG, NLP, or computer vision.

- Experience optimizing ML systems for production-scale workloads.

What We Offer

- Competitive salary of $120K–$250K+, based on experience, skills, and location.

- Medical, dental, and vision insurance.

- 401(k) and retirement benefits.

- Paid time off and company holidays.

- Flexible and hybrid work options, depending on role.

- Professional development and learning opportunities.

- Opportunity to work on high-impact AI and ML products.

- Collaborative, technology-driven work environment.

How to Apply

If you’re passionate about building production-grade machine learning systems and want to work on challenging AI/ML problems, apply today with your resume and relevant project or professional experience.