Machine Learning Jobs in Cambridge, MA

Lead multiple AI/ML computational projects across early-stage ventures, define AI strategy, oversee method and pipeline development (including protein design, docking, LLM agents), ensure benchmarking/scaling/productionization, mentor and manage cross-functional teams, scout literature, and represent the organization to partners and conferences.

Lead and grow a team of ML, backend, and data engineers to design, deploy, and operate production-scale personalization and recommendation systems. Set technical direction, partner with Product and Data Science, mentor engineers, prioritize delivery, and champion adoption of modern ML techniques including large language models and generative AI to improve user personalization.

Design and develop large-scale AI systems for healthcare, focusing on ML infrastructure, real-time analytics, and collaboration with teams. Requires extensive experience in software engineering and AI.

Design, develop, and deploy scalable ML software systems for healthcare. Collaborate with teams to improve products and ensure performance and reliability.

Design and develop scalable ML systems for healthcare, collaborating with teams to enhance products and maintain efficient infrastructures.

Design, develop, and maintain scalable AI/ML systems and end-to-end pipelines (data preprocessing, model training, prompt engineering). Collaborate cross-functionally, optimize model performance, ensure fairness and security, monitor production systems, troubleshoot issues, mentor junior engineers, and recommend improvements to data infrastructure and tooling.

Lead and build an ML Acceleration team to define strategy and implement scalable solutions to speed ML training. Hands-on role combining technical design (distributed training, hardware and model optimization) with people management, cross-functional collaboration, impact measurement, and continuous performance improvements for production autonomous vehicle ML systems.

Develop and deploy LLM-powered ML applications for psychological wellbeing. Collaborate with psychologists to translate insights into computational systems, advance RLHF and agentic AI methods, build scalable data and training pipelines, integrate ML systems into real-world products, and communicate results to multidisciplinary teams.

Design and optimize computer-vision and robotics ML models for constrained edge devices, build large-scale training and fine-tuning pipelines, compress and deploy models to embedded accelerators, profile on-device inference for latency and accuracy, and write maintainable Python/C++ code while staying current on state-of-the-art techniques.

Lead design, build, and productionalize generative AI applications and platform capabilities. Automate ML pipelines, optimize models (LoRA/QLoRA/PEFT), implement guardrails and monitoring, support RAG and vector DB tuning, mentor engineers, and collaborate with product and data teams to deploy safe, reliable GenAI features across web and mobile.

Lead the scientific direction and machine learning development for climate modeling. Manage model training, evaluation, and collaboration across teams to develop predictive systems based on hydrology and climate data.

Lead training, adaptation, evaluation, and productionization of Lila's AI models for scientific customer workflows. Build evaluation loops, design experiments, debug model failures, integrate model improvements into product workflows, and create reusable tooling for model adaptation, evaluation, and deployment while collaborating across research and software teams.

Design and optimize on-vehicle perception inference: allocate CPU/GPU resources, compress and quantize large multimodal models, build TensorRT model conversion pipelines, and deliver production-quality low-latency C++/CUDA inference for power- and thermal-constrained vehicle SoCs.

Lead development and deployment of AI/ML solutions for target discovery, translational research, biomarker identification, and scientific decision-making. Partner with scientists and engineering teams to build scalable workflows for complex biological and multimodal data, promote best practices, mentor colleagues, and recommend emerging AI technologies to accelerate research.

Lead development and deployment of AI/ML solutions for target discovery, translational research, and biomarker identification. Partner with scientists and data engineers to build scalable workflows for multimodal biological data, promote reproducibility and model validation, mentor colleagues, and recommend emerging AI applications to accelerate discovery.

Lead development, training, validation, and application of generative chemistry and molecular models. Curate datasets and benchmarks, optimize workflows, collaborate with wet lab scientists, and translate experimental data into insights to shape the company's scientific direction.

Lead SRE ownership of ML platform SLOs and operational health for Ray on EKS and Databricks on EC2. Maintain observability with CloudWatch/Datadog, tune autoscaling and GPU scheduling, manage Databricks workspaces and IAM, optimize cost/capacity, codify infrastructure with Terraform and CI/CD, lead incident response and postmortems, perform security/OS maintenance, and participate in on-call rotation.

The Cloud and ML Infrastructure Engineer will maintain complex systems, manage AWS production infrastructure for ML/AI workloads, and design monitoring systems for algorithms.

Build and ship full-stack features for a production AI/LLM platform: implement polished React UIs from specs, own database and access-control/security, integrate multi-provider LLMs (RAG, agentic workflows), and formalize QA by extending test suites with integration and end-to-end coverage.

Lead discovery and design of end-to-end enterprise AI/ML architectures, define MLOps/LLMOps and data lifecycles, integrate solutions with enterprise systems and security, produce reference patterns and runbooks, mentor teams, and scale repeatable delivery through accelerators while partnering with clients and risk/security stakeholders.

The MLOps Engineer focuses on developing tools and processes for ML workflows, empowering engineers, and ensuring high-quality model deployment. Key responsibilities include managing distributed systems, improving observability, and advocating best practices across teams.

Own and build end-to-end matching and ranking systems for out-of-home advertising: design candidate generation, re-ranking, geospatial scoring, data warehouse and feature pipelines, evaluation infrastructure, low-latency ranking APIs, and model monitoring with drift detection and retraining triggers.