Machine Learning Engineer

Summary

Join Splash Financial as a Machine Learning Engineer and be a key player in cross-functional teams, driving projects from conception to launch. You will develop and implement cutting-edge CI/CD pipelines for AI/ML models and applications, collaborate with data scientists and engineers to refine model pipelines, and stay updated on MLOps and cloud technologies. This role requires 1–2 years of experience in a related field, a solid grasp of software engineering and MLOps, understanding of ML algorithms, and proficiency with AWS Sagemaker. A Bachelor's degree in a quantitative field is required, with a Master's degree preferred. Splash offers a competitive salary, flexible PTO, equity, comprehensive insurance, paid parental leave, and more. The company is remote-first and values a collaborative, supportive work environment.

Requirements

• 1–2 years of experience in a machine learning engineering or related role
• Solid grasp of Software Engineering practices, coupled with a good understanding of Data Engineering and MLOps principles
• Understanding of common ML algorithms and hands-on experience in crafting ML models coding in Python (pandas, numpy, sklearn)
• Experience building data infrastructure in a cloud environment using one or more infrastructure as code tools (Terraform, AWS CloudFormation, etc.)
• Expertise in working with cloud data integration platforms (Airflow, DBT, Snowflake, Databricks, etc.)
• Experience manipulating and analyzing data using SQL and Python
• Track record of successfully collaborating with product managers in delivering impactful products
• Hold a Bachelor's degree in quantitative fields such as Computer Science, Software Engineering, or Physics

Responsibilities

• Develop and implement cutting-edge CI/CD pipelines, automating the deployment, testing, and monitoring of AI/ML models and applications
• Collaborate closely with data scientists, data engineers, and software engineers to refine model training, deployment, and inference pipelines
• Keep abreast of the latest advancements and trends in MLOps, DevOps, and cloud technologies, actively sharing insights with the team to foster continuous improvement

Preferred Qualifications

• Master’s degree preferred
• Hands-on proficiency with AWS Sagemaker, and ML deployment lifecycle is a PLUS

Benefits

• Fully remote work freedom
• Competitive salary packages
• Flexible PTO + 9 company holidays
• Equity: Share in our start-up success
• Comprehensive and affordable insurance benefits
• Paid parental leave for both caregivers
• Essential equipment to get the job done
• 401(k) for your future savings
• Quarterly meet-ups: In person & virtual fun
• Awesome Splash swag to flaunt your team spirit