Machine Learning Engineer

We are seeking an experienced and driven Machine Learning Engineer to design, build, and optimize scalable machine learning models that drive intelligent applications. In this role, you will develop advanced algorithms, implement natural language processing (NLP) solutions, and streamline the end-to-end model lifecycle from research to production.

This is a hands-on technical role where you will leverage your expertise in supervised/unsupervised learning, neural networks, and modern ML frameworks. Working closely with data science and engineering teams, you will utilize cloud platforms, implement robust MLOps and DevOps practices, and ensure high-performance model deployment and management. If you are passionate about turning complex data into production-ready AI solutions, we want to hear from you., Model Development & Architecture

• Algorithm Design: Develop, train, and optimize supervised and unsupervised learning algorithms to solve complex business challenges.
• Neural Networks & NLP: Design and implement deep learning architectures and natural language processing (NLP) pipelines for text analysis and intelligent automation.
• Data Integration: Utilize Python, R, and advanced SQL queries to extract, clean, and manipulate large datasets for model training and evaluation.

MLOps & Production Deployment

• Framework Implementation: Build and fine-tune models using industry-standard machine learning frameworks such as TensorFlow, Keras, and PyTorch.
• Pipeline Automation: Implement robust DevOps and MLOps practices, managing model registries, automated testing, continuous integration/continuous delivery (CI/CD) for ML, and model monitoring in production.
• Cloud Infrastructure: Deploy and scale machine learning workloads across cloud platforms and technologies, ensuring cost-efficiency, security, and low-latency inference., AllSTEM Connections participates in the E-Verify program in certain locations as required by law. Learn more about the E-Verify program. _Participation_Poster_ES.pdf

We also consider for employment qualified applicants regardless of criminal histories, consistent with legal requirements, including, if applicable, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance. Pursuant to applicable state and municipal Fair Chance Laws and Ordinances, we will consider for employment-qualified applicants with arrest and conviction records, including, if applicable, the San Francisco Fair Chance Ordinance. For Los Angeles, CA applicants: Qualified applications with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

• Experience Baseline: Professional experience designing, deploying, and maintaining machine learning models in production environments.
• Technical Mastery:
• Deep theoretical and practical knowledge of supervised and unsupervised learning algorithms.
• Proven experience building neural networks and natural language processing (NLP) applications.
• Strong programming proficiency in Python, R, and SQL.
• Hands-on experience with core ML frameworks: TensorFlow, Keras, and PyTorch.
• Core Competencies: Working knowledge of cloud platforms (AWS, Azure, or Google Cloud Platform) and practical implementation of DevOps/MLOps pipelines for model serving and monitoring.

Preferred Attributes

• Experience optimizing model inference latency and resource utilization in cloud-native environments.
• Strong collaboration and communication skills, with the ability to bridge data science research and software engineering production standards.