Software Engineer II - MLOps
TLDR
Operates at the core of production ML, building end-to-end pipelines and infrastructure to deploy, monitor, and optimize models with cross-functional collaboration.
- Design, build, and maintain robust infrastructure for deploying, monitoring, and managing machine learning models in production environments.
- Develop and optimize end-to-end ML pipelines, including feature engineering, model training workflows, deployment, and continuous evaluation.
- Collaborate closely with data scientists and product engineers to productionize models and ensure operational readiness.
- Build and maintain CI/CD pipelines to support automated, reliable, and reproducible machine learning deployments.
- Implement monitoring, logging, and alerting systems to ensure model performance, system reliability, and early detection of issues.
- Improve system architecture for scalability, uptime, and cost efficiency across distributed environments.
- Evaluate and integrate new tools, frameworks, and best practices to enhance the MLOps ecosystem.
- Document engineering standards, workflows, and operational procedures to support knowledge sharing and consistency across teams.
- 3+ years of experience in MLOps, Data Engineering, or infrastructure-focused software engineering roles.
- Strong proficiency in Python and backend engineering principles.
- Hands-on experience deploying, monitoring, and maintaining machine learning models in distributed production systems.
- Solid understanding of workflow orchestration tools such as Apache Airflow.
- Experience with distributed data processing or streaming technologies such as Kafka or Spark.
- Proven experience building CI/CD pipelines and automated software delivery workflows.
- Familiarity with cloud-based infrastructure and modern DevOps practices.
- Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent practical experience.
- Strong communication and collaboration skills in cross-functional engineering environments.
- Proactive, detail-oriented mindset with a strong focus on automation and system reliability.
- Demonstrated ability to leverage AI tools to improve productivity and engineering outcomes.
- Remote-first work environment with global collaboration across distributed teams.
- Flexible working hours supporting work-life balance and autonomy.
- Self-managed PTO allowing full control over personal time off.
- Monthly compensation starting from USD $4,500.
- Home office support including equipment choice (Mac or PC) and setup stipend.
- Culture of innovation with strong emphasis on learning, experimentation, and career growth.
- Inclusive, mission-driven engineering culture focused on meaningful real-world impact.
Requirements:
Benefits:
Benefits
Education Stipend
Culture of innovation with strong emphasis on learning, experimentation, and career growth.
Flexible Work Hours
Flexible working hours supporting work-life balance and autonomy.
Home Office Stipend
Home office support including equipment choice (Mac or PC) and setup stipend.
Learning Budget
Culture of innovation with strong emphasis on learning, experimentation, and career growth.
inclusive, mission-driven engineering culture
Inclusive, mission-driven engineering culture focused on meaningful real-world impact.
Paid Time Off
Self-managed PTO allowing full control over personal time off.
Remote-Friendly
Remote-first work environment with global collaboration across distributed teams.
Jobgether runs the largest remote job platform, effectively linking job seekers with over 200,000 flexible and remote opportunities that match their unique skills and preferences. Our focus is on enhancing the hiring process, ensuring efficiency while prioritizing the candidate experience, particularly in the growing health and wellness sector.
- Founded
- Founded 2020
- Employees
- 11-50 employees
- Industry
- Professional Services