Top Remote Entry Level Machine Learning Engineer Jobs in Boston, MA

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.

Design, develop, and productionize machine learning models for fraud detection and identity verification. Perform end-to-end model lifecycle work including data acquisition, feature engineering, training, experimentation, deployment, and monitoring. Research new fraud types, build product-focused models, write production-ready code, and collaborate with engineering, operations, and data teams to support product and business needs.

Develop and optimize applied AI systems, including LLM applications and agentic platforms, for enterprise use cases. Build system APIs and scalable software in Golang or JVM languages, collaborate with engineering teams, and write clean, efficient code using computer science fundamentals. The role requires knowledge of machine learning, deep learning, NLP, reinforcement learning, mathematics, Python, and API development. Data engineering experience with batch processing and query engines is preferred.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

Instructor-led AI and machine learning training covering theory, data preprocessing, algorithms, model building, analytics, real-time projects, case studies, and practical business and IT applications. Participants use Python, TensorFlow, and Scikit-learn and receive certification upon successful completion.

Develop, train, evaluate, and deploy machine learning models and AI-powered applications. Responsibilities include data preprocessing, feature engineering, deep learning development, cloud-based model deployment, and collaboration with data engineers, MLOps teams, and business stakeholders. The role also requires applying strong analytical and research skills while staying current with advances in artificial intelligence.

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.

Develops and deploys machine learning systems for high-stakes decisions in pricing, claims, underwriting, and predictive health. Responsibilities span problem framing, quantitative research, experimentation, causal inference, predictive modeling, multimodal and foundation model development, agent systems, production deployment, and ongoing iteration. The role requires strong mathematical and statistical foundations, Python fluency, and expertise in machine learning, statistics, econometrics, or causal inference.

Build and deploy low-latency machine learning models and inference infrastructure for Netflix’s advertising platform. Develop identity resolution, behavioral and contextual targeting, lookalike expansion, user signals, and audience features at massive scale. Collaborate with science, product, engineering, operations, design, and research teams to productionize models while supporting privacy, governance, security, and advertiser brand safety.

Deploy and optimize frontier AI models for real-time production use at scale. Responsibilities include improving inference latency, throughput, and cost through quantization, distillation, KV-cache optimization, batching, and custom kernels; building high-performance streaming serving systems; and creating tooling that enables safe, efficient model deployment.