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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** Committee Of Interns And Residents (inc) - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Airflow, Computer Vision, Continuous Integration, Github, Python (Programming Language), Machine Learning, Software Engineering, Reliability of Systems, Backend, Containerization, Kubernetes, Apache Flink, Deployment Automation, Machine Learning Operations, Api Design, Terraform, Docker, Microservices - **Published:** September 5, 2026 - **Apply:** https://startup.jobs/senior-machine-learning-engineer-forecasting-apella-io-9881022 ## About the Role We're looking for a senior machine learning engineer who would thrive at the intersection of MLOps, infrastructure, and production forecasting systems. You excel at building robust, automated ML pipelines that enable data scientists to iterate quickly while maintaining the reliability that our healthcare customers depend on. You are a systems thinker who flourishes in a startup environment, working across the stack to productionize ML models, automate deployment pipelines, and establish the infrastructure that makes our forecasting platform scalable and maintainable. As a critical member of our Forecasting team, you'll work on production ML lifecycle - from training automation to serving infrastructure - and have the unique opportunity to shape the technical foundation of Apella's ML platform while growing our team's capabilities., * 5+ years of experience building and maintaining production ML systems, with deep expertise in MLOps, deployment automation, and model serving infrastructure * Strong software engineering skills with proficiency in Python, containerization (Docker/Kubernetes), CI/CD systems (GitHub Actions, ArgoCD), and infrastructure-as-code (Terraform, Helm) * Production ML deployment experience including model training orchestration (Dagster, Airflow, or similar), automated retraining pipelines, and A/B testing/variant management * Systems design expertise with experience building scalable microservices, API design, and managing complex service dependencies * Ownership mentality with a track record of driving projects from concept to production, maintaining them over time, and continuously improving system reliability * Excellent collaboration skills working with data scientists to productionize research, with backend teams on API integration, and with product org to meet customer needs * Passion for writing tested, maintainable, well-documented code that enables team velocity, * Experience working in healthcare or other regulated industries. * Experience with Forecasting / Time Series algorithms * Experience with Computer Vision * Experience with DAG frameworks, Flink ## Description * Collaborate closely with engineering, product, and data science teams to understand business challenges and the potential for machine learning and AI solutions. * Develop tools and automate manual processes to improve operational efficiency, accelerate experimentation velocity, and minimize human error. * Build, integrate, and monitor the end-to-end lifecycles of large-scale, distributed machine learning systems. * Investigate model performance and identify data quality and performance issues. * Enhance the ML pipeline for our forecasting platform, managing weekly automated model retraining and deployment across a range of production models * Elevate the team's technical capabilities in MLOps best practices, automation, and production ML systems. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)