Senior Machine Learning Engineer
Remote
Goodleap
We are a sustainable home solutions marketplace. Providing simple, fast, and frictionless point-of-sale technology for countless mission-driven professionals serving millions of people who want to upgrade their homes and save money.Position Summary
Senior Machine Learning Engineer will work closely with engineering tech leads to deploy LLM models into production, build scalable ML infrastructure, and optimize ML workflows. This role will play a crucial role in defining and scaling ML/AI applications in production, ensuring efficiency, reliability, and automation across model development, training, and evaluation.
Essential Job Duties and Responsibilities
- Collaborate with engineering tech leads to integrate LLM models into production systems.
- Identify and solve engineering pain points by building scalable, general-use ML platforms.
- Design and develop scalable infrastructure and pipelines for data/feature processing, model training, and evaluation.
- Automate ML workflows to improve productivity across training, evaluation, testing, and results generation.
- Partner with cross-functional teams to define the long-term vision for ML/AI applications and contribute to roadmap planning.
- Implement ML Ops best practices, ensuring efficient model deployment, monitoring, and versioning.
- Optimize and manage distributed processing architectures using Spark, Databricks, Airflow, Kubeflow, MLflow, etc.
- Develop microservices-based architectures for ML applications, including RESTful APIs for model serving.
- Ensure compliance with scalability, reliability, and security standards in ML production systems.
Required Skills, Knowledge, and Abilities
- Master’s degree in computer science, Machine Learning, or a related field with 5+ years of experience as an ML Engineer or ML Scientist in an industry setting.
- Strong programming skills in Java, Python, and SQL/MySQL.
- Hands-on experience in ML Ops, including large-scale ML applications, services, pipelines, and architectures.
- Solid understanding of system design for ML systems, including design patterns, OOD (Object-Oriented Design), and interface design.
- Experience with distributed processing architectures and ML/data workflow management platforms (e.g., Spark, Databricks, Airflow, Kubeflow, MLflow).
- Experience with containerization and orchestration tools like Docker and Kubernetes
- Ph.D. in Computer Science, Machine Learning, or a related field, or 3+ years of ML Engineering experience in addition to a Master’s degree.
- Strong theoretical and practical understanding of machine learning models and frameworks (Scikit-Learn, TensorFlow, PyTorch, etc.).
- Experience working with cloud-based solutions, especially AWS and Databricks.
- Experience with CI/CD pipelines, automated testing, and test-driven development for ML applications.
- Knowledge of microservice architectures and best practices for RESTful web services.
Preferred Qualifications
Job duties include additional responsibilities as assigned by one's supervisor or other managers related to the position/department. This job description is meant to describe the general nature and level of work being performed; it is not intended to be construed as an exhaustive list of all responsibilities, duties and other skills required for the position. The Company reserves the right at any time with or without notice to alter or change job responsibilities, reassign or transfer job position or assign additional job responsibilities, subject to applicable law. The Company shall provide reasonable accommodations of known disabilities to enable a qualified applicant or employee to apply for employment, perform the essential functions of the job, or enjoy the benefits and privileges of employment as required by the law.
If you are an extraordinary professional who thrives in a collaborative work culture and values a rewarding career, then we want to work with you! Apply today!
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Airflow APIs Architecture AWS Business Intelligence CI/CD Computer Science Databricks Docker Engineering Java Kubeflow Kubernetes LLMs Machine Learning Microservices MLFlow ML infrastructure ML models Model deployment Model training MySQL Nonprofit Pipelines Python PyTorch Scikit-learn Security Spark SQL TDD TensorFlow Testing
Perks/benefits: Career development
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