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

Leads the architecture, development, deployment, and optimization of large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establishes enterprise AI architecture, performance and safety standards, and multi-year platform strategies. Partners across engineering, research, product, and program management while mentoring technical leaders and driving responsible AI adoption.

Lead architecture and low-level optimizations for LLM inference to maximize throughput and minimize latency. Profile and optimize GPU kernels, implement quantization and parallelization strategies (FP8/INT8/FP4), tune libraries (AITER, Triton, CUDA, ROCm), and mentor engineers while translating hardware limits into shippable platform features.

Leads the architecture and evolution of DraftKings’ machine learning platform, including real-time data pipelines, lakehouse governance, distributed data systems, and scalable ML serving infrastructure. Establishes engineering standards, drives complex initiatives, oversees reliability and cloud cost optimization, and partners across engineering and data teams. The role also mentors engineers, advances automation, supports incident response, and applies AI-assisted development practices.

Apply established machine learning techniques—including clustering, classification, and anomaly detection—to ICS/OT cybersecurity data. Integrate model outputs into batch and near-real-time pipelines, evaluate third-party models, maintain reliable data contracts and observability, and troubleshoot production issues such as data drift and output quality. Collaborate with AI engineers, data engineers, and product teams while clearly communicating model behavior, uncertainty, and downstream use.

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.

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.

Own the reliability and quality of an AI copilot for a trading platform. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement workflows. Partner with engineering and product to create safe, auditable tool interactions and improve assistant behavior. Develop domain expertise in trading, risk, margin, execution, and portfolio reasoning while operating across model, backend, and frontend systems.

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.

Leads and mentors Applied Scientists developing, evaluating, and deploying machine learning and language-model solutions for healthcare workflows. Translates clinical and business problems into scalable AI systems, applies rigorous statistical methods and experimentation, partners with engineering and cross-functional stakeholders, and ensures models are reliable, reproducible, and safe. The role also contributes hands-on technical work, recruiting, onboarding, and team development.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.