Top Remote Machine Learning Jobs in Boston, MA

Design, develop, and operate scalable VoIP infrastructure supporting AI-driven voice workloads. Integrate telephony systems, LLM applications, and real-time communication platforms while improving observability, reliability, deployment, and SRE practices. Build tested, maintainable software across telephony and AI layers, support production environments, collaborate with engineering and product teams, and mentor colleagues.

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.

Lead the architecture and development direction of NVIDIA ALCHEMI and related AI software for chemistry and materials science. Design developer tools, APIs, and workflows; translate advances in machine learning, generative modeling, interatomic potentials, and quantum chemistry into product capabilities; coordinate researchers, engineers, product teams, partners, and customers; and represent NVIDIA at scientific and industry events.

Lead AI/ML initiatives for Air Force condition-based maintenance, from predictive model development through production deployment. Architect data pipelines, develop machine learning, deep learning, NLP, and generative AI solutions, and build knowledge graph capabilities. Provide technical leadership, integrate models into applications and workflows, and monitor data quality, model performance, drift, and bias. Collaborate with engineering, cloud, software, and product teams while supporting defense aviation logistics and sustainment missions.

Develop AI agents, prompts, workflows, contextual data layers, orchestration frameworks, and machine learning models. Test and optimize solutions using structured and unstructured data, evaluate accuracy and scalability, analyze edge cases and failure modes, and document experiments and model performance. The 10-week remote internship is intended for students at least in their third year studying data science, AI, computer science, or a related field.

Develop and refine machine learning models using Python, analyze datasets with MongoDB, and integrate preprocessing and data pipelines. Evaluate models, tune hyperparameters, benchmark performance, document experiments, and deliver data-driven recommendations. Collaborate remotely with cross-functional contributors to improve and operationalize machine learning solutions in cloud or enterprise environments.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Lead the technical direction of Quora’s recommendation ecosystem, including feed retrieval, ranking, re-ranking, optimization, data pipelines, feature engineering, model training, and production integration. Drive measurable improvements through experimentation, apply generative AI, mentor engineers, and partner across product, data science, and infrastructure teams.

Develop and productize machine learning and digital signal processing algorithms for speech and audio applications, including noise suppression, echo cancellation, beamforming, spatial audio, and enhancement. Research modern deep learning approaches, prototype with Python or MATLAB, and deploy optimized real-time solutions on consumer hardware. Collaborate across research, systems engineering, and product development to deliver robust audio technologies at scale.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Own and advance Wonderlic’s AI-powered Jobs Engine, using production machine learning and NLP to analyze labor-market data and infer job characteristics at scale. Partner with product, engineering, and I-O psychology teams to integrate responsible AI into assessment and workforce solutions. Apply fairness, explainability, legal defensibility, and rigorous evaluation practices to employment-related AI systems, including generative AI outputs and assessment interpretation.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Lead the design, productionization, and operation of machine learning and agentic AI capabilities. Partner with product engineering, shape AI/ML architecture, build evaluation systems and data pipelines, and develop reliable MLOps tooling. Champion responsible AI, safety, latency, cost, and error handling. Serve as a technical resource for engineers, influence direction, mentor others, and occasionally explain AI functionality to customers or customer-facing teams.

Lead architecture and development of advanced perception systems for Level 4 autonomous vehicles. Build multimodal foundation models, synthetic data and sensor simulation capabilities, knowledge-distillation pipelines, and efficient real-time onboard models. Deploy solutions to autonomous vehicle fleets while mentoring engineers and providing technical leadership. The role focuses on object detection, segmentation, tracking, classification, sensor fusion, closed-loop evaluation, and robust generalization across vehicle generations.

Designs, trains, evaluates, and operationalizes intelligent document-processing solutions using OCR, machine learning, LLMs, Python, and cloud data pipelines. Builds extraction, validation, reconciliation, exception-handling, and human-review workflows for sensitive information. Develops secure, auditable, configuration-driven systems with API integrations, monitoring, testing, CI/CD, and infrastructure as code. Partners with business experts to define document fields, quality thresholds, validation rules, and acceptance criteria.

Principal Machine Learning Engineer setting technical strategy for ZoomInfo’s data graph, entity resolution, buying intent, and AI agent context. The role requires hands-on production machine learning, evaluation standards, data contracts, model cost optimization, and scalable Python and SQL systems. Responsibilities include leading design reviews, mentoring engineers, influencing hiring, and establishing technical defaults across teams. Preferred expertise includes ranking, retrieval, knowledge graphs, information extraction, PyTorch model serving, GPU planning, agent memory, and LLM security.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

Own and improve Quora’s second-phase ranking models for feed and digest experiences. Build end-to-end machine learning systems covering data pipelines, candidate extraction, feature engineering, model training, and production integration. Identify new product applications for machine learning, develop efficient algorithms with other ML engineers, and contribute methodologies for large-scale recommendation systems.

Build and deploy scalable AI/ML systems for retrieval, ranking, categorization, triage, and generative AI across healthcare data. Own solutions end-to-end from problem framing and model selection through production deployment and optimization. Collaborate with software engineers, product managers, clinicians, and operations teams to translate complex business problems into explainable, maintainable systems with measurable outcomes.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

Develop and productize machine learning models for autonomous vehicle perception, including LiDAR and camera perception, sensor fusion, detection, classification, segmentation, and tracking. Build scalable data, training, metrics, and performance workflows, and implement efficient C++ code for the NVIDIA DRIVE AV platform. Collaborate across engineering teams, track advances in machine learning, and optimize models for real-time embedded inference.

Build and ship production AI systems for enterprise portfolio and planning workflows. Develop anomaly and signal detection, LLM-powered insight synthesis, natural-language planning rule translation, evaluation frameworks, and MCP tools. Partner with external AI providers initially, then establish internal standards for prompt design, model selection, agent architecture, reliability, and evaluation. Collaborate closely with product, design, and domain engineering teams.

Designs and delivers end-to-end AI/ML solutions on Google Cloud, including data ingestion, feature engineering, model training, deployment, and monitoring. The role leads client discovery, translates business needs into AI/ML strategies, develops roadmaps, provides technical leadership, and supports testing and user training. It requires hands-on Gemini and GCP expertise, customer-facing consulting experience, and collaboration across engineering and product teams.

Leads Hightouch’s machine learning organization, including product development, technical roadmapping, recruiting, reliability, and people management. The role focuses on personalization, product recommendations, probabilistic identity resolution, LLM-powered content generation, and scalable machine learning infrastructure for model training and inference.