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.

Lead and grow the Serving/API engineering team responsible for OpenAI-compatible APIs, tokenization, structured generation, tool calling, reasoning, speculative decoding, multimodal serving, and model integration. Set technical direction, establish release and conformance gates, improve latency and API fidelity, partner across compiler, hardware, and production teams, and translate customer needs into scalable engineering roadmaps and ownership structures.

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.

Design and deploy production machine learning models and conversational AI agents that transform advertiser briefs into optimized media plans. Build evaluation systems to improve model and agent quality, collaborate across science and engineering teams, investigate ambiguous problems, and iterate using performance data and seller feedback. Communicate effectively with technical and non-technical stakeholders while contributing broadly across machine learning domains.

Lead the design, development, testing, deployment, and productization of scalable AI/ML solutions. Build and optimize machine learning and deep learning models, transform prototypes into secure enterprise systems, and develop production-grade MLOps pipelines and CI/CD workflows. Guide AI/ML strategy, roadmaps, cloud deployments, and enterprise integration while mentoring engineering teams, advising customers, supporting workforce planning, and collaborating with technical and government stakeholders.

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.

Build and productionize machine learning systems that convert complex documents into executable, verifiable logic. Responsibilities include developing parsing and transformation pipelines, experimenting with language models, designing datasets and evaluation metrics, creating adversarial tests, analyzing model failures, and implementing rule-based checks using logical reasoning and program analysis. The role requires close collaboration with founders and subject-matter experts to validate system outputs.

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.

Build and maintain Quora’s machine learning platform and distributed model-serving infrastructure. Develop scalable systems for recommendation and language models, optimize GPU serving latency, throughput, and cost, improve ML tooling and feature stores, and contribute to platform modernization. The role includes production deployment, collaboration with senior engineers, mentoring, and participation in an on-call rotation.

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.