Top Remote Senior Machine Learning Engineer Jobs in Boston, MA

Design, build, and deploy high-throughput ML and LLM-powered personalization and agentic systems at consumer scale. Process petabyte-scale data, optimize real-time inference, implement resilient fallback paths, and balance latency, cost, and model quality while collaborating across engineering and product teams.

The Senior Machine Learning Engineer will develop core ML models, manage data pipelines, and collaborate with stakeholders to deliver scalable ML solutions.

As a Senior ML Ops Engineer, you will bridge Data Science and Engineering to automate ML workflows, manage CI/CD for ML systems, and collaborate with various teams to implement solutions for AI features and services.

Build and scale ML development infrastructure and Agentic AI tooling. Optimize distributed training (PyTorch/DDP), improve GPU utilization, design multi-cloud Kubernetes environments, and collaborate with ML researchers and engineers to streamline the ML lifecycle.

Leads the architecture, development, deployment, and optimization of enterprise machine learning solutions. Responsibilities include designing scalable ML systems, developing predictive models, supporting MLOps and model lifecycle management, improving data quality and feature engineering, implementing governance and validation frameworks, and mentoring technical teams. The role also contributes to AI strategy, innovation initiatives, responsible AI practices, and organizational modernization.

Lead relevance evaluation and quality improvements by fine-tuning LLMs, building low-latency reranking and filtering systems, scaling LLM deployments, creating automated monitoring pipelines and agentic insights, and validating impact via A/B tests while collaborating with cross-functional teams.

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

Develop and deploy advanced ML solutions for OCR and NLP, optimizing models for performance and integrating them into production workflows while collaborating with cross-functional teams.

Design, build, and operate core AI capabilities and reusable model integrations for an enterprise platform. Integrate foundation models into production, optimize performance and reliability, prototype new techniques, and support scalable backend services and distributed AI systems in collaboration with cross-functional teams.

Build and productionize large-scale ML models and pipelines for query understanding and personalization. Work on autocomplete, query tagging/NER, query expansion, intent modeling, and LLM-powered natural-language search. Collaborate cross-functionally to design, deploy, and monitor batch and real-time systems, leveraging modern ML infrastructure and tooling to improve search relevance across Airbnb products.

The Senior Machine Learning Engineer will utilize deep experience and insights to enhance communication and digital interactions through advanced machine learning tools and algorithms, focusing on unstructured data.

Build and maintain ML and generative AI tooling, training and inference platform services, model evaluation and testing frameworks, and APIs to expose ML products. Deploy and manage applications across the AI/ML SDLC, mentor teammates, and write production-quality code daily.

The Senior ML Engineer will automate component placement on PCBs, working on R&D and production using ML and optimization techniques.

The role involves managing the ML lifecycle, developing scalable ML systems, and collaborating on AI-first product solutions focusing on Large Quantitative Models.

As a Senior Machine Learning Engineer, develop LLM evaluation frameworks and agentic AI workflows to extract insights from conversations and optimize customer experiences.

Lead design, development, and production deployment of ML models and GenAI/LLM solutions to optimize clinical trial workflows. Build scalable pipelines, monitoring, and CI/CD for robust production use. Collaborate cross-functionally, mentor junior engineers, and communicate technical results to technical and non-technical stakeholders.

The role involves building a large-scale data ingestion system using Python, web scraping, and data processing technologies to extract and classify data from diverse sources, and optimize data pipelines.

Design, implement, and scale ML-based solutions for search and advertising; define data collection, evaluation, and production serving strategies; analyze data to inform development; communicate cross-functionally; produce technical reports; and stay current with advances in LLMs, vision models, and generative AI.

As a Senior Machine Learning Engineer, you will enhance diffusion models, conduct research, collaborate on system implementations, and communicate findings effectively.

Responsible for fine-tuning state-of-the-art LLMs, optimizing models for deployment, and driving AI architectural decisions. Collaborate with cross-functional teams to develop impactful AI products.

Build and maintain scalable ML and generative AI platforms and backend infrastructure; deploy and manage production applications; create services exposing ML/AI products; implement RAG solutions and vector databases; integrate LLM orchestration and foundation model APIs; collaborate cross-functionally and mentor teammates.

The Senior Databricks AI/ML Engineer will design and deploy AI/ML solutions in the insurance sector, focusing on Databricks architecture, data pipelines, and MLOps workflows, while collaborating with various teams to operationalize models and optimize processes.

Design and implement machine learning models to solve business problems, analyze data for insights, and optimize model performance.

Senior MLOps engineer to productionize NLP/GenAI models and RAG systems: build and automate ML pipelines, CI/CD, model registries, search/vector/graph-based retrieval, evaluation metrics and cost-optimized scalable infrastructure, collaborating with data scientists, product and operations teams.

Design, implement, and own end-to-end scalable ML systems and MLOps lifecycle (deployment, monitoring, metrics). Optimize models and system performance, work with Java/Python and TF/PyTorch, use CI/CD, Terraform, Docker/Kubernetes, lead inter-team communication, document work, and meet deadlines.