AI / ML Engineer in Cambridge

About the position

We're seeking a Senior ML Engineer to build next-generation AI systems that help millions of patients access care faster.

You'll architect production ML infrastructure handling thousands of hours of service interactions daily in a highly regulated healthcare environment.

This is a high-impact individual contributor role—ideal for someone eager to “own the outcome” and push the boundaries of “high tech + high touch” care experiences.

Responsibilities

• Architect and build large-scale AI systems that integrate high-volume voice, text, and contextual event streams with extensive knowledge bases to deliver real-time recommendations, automations, and decision support.
• Design and operate workflow-oriented AI systems, including DAG-based execution graphs, stateful pipelines, and agent-driven workflows with clear observability, reproducibility, and fault tolerance.
• Build agent architectures spanning agent-to-agent coordination, feedback loops, tool-calling systems, and long-running autonomous workflows, balancing control, safety, and adaptability.
• Design and implement data models, feature pipelines, and APIs to support model training, low-latency inference, and continuous learning.
• Develop predictive, real-time analytics systems that combine streaming data, ML inference, and event-driven triggers to surface insights and automate actions at scale.
• Implement and maintain end-to-end ML platforms, including model training, evaluation, deployment, online inference, monitoring, and drift detection.
• Partner closely with product managers, data scientists, and QA engineers to translate experimental models into reliable, production-grade AI services.
• Identify, diagnose, and resolve performance and scaling bottlenecks across data pipelines, inference services, and orchestration layers as production workloads grow.
• Required skills and qualifications
• 5+ years of software engineering experience, with 3+ years focused on machine learning or applied AI systems.
• Strong proficiency in Python, particularly for ML pipelines, frameworks, inference services, and APIs (e. g., scikit-learn, Sanic API, Py Torch Lightning, Pydantic AI, Lang Graph, Bedrock, Open AI / Anthropic SDKs).
• Experience designing ML-centric data architectures, including feature stores, vector databases, and time-series systems for monitoring and analytics.
• Hands-on experience with cloud-native inference: containerized model serving, autoscaling, GPU/accelerator workloads, and low-latency production deployments.
• Experience operating end-to-end MLOps platforms (e. g., MLflow, Kubeflow), including CI/CD for models, experiment tracking, and rollout strategies.
• Solid understanding of workflow orchestration (graph-based execution, retries, state management) in ML and agent-based systems.
• Excellent communication skills, with the ability to collaborate effectively across engineering, product, and non-technical stakeholders.
• Strong interest in healthcare innovation and building AI systems that meaningfully improve health outcomes.
• Working knowledge of AI safety, bias detection, and responsible AI practices.
• Desired skills and qualifications
• Experience building AI systems in healthcare or regulated environments, with familiarity with standards such as HIPAA, GDPR, or FDA guidance.
• Proven experience leading complex technical initiatives and mentoring junior engineers.
• Strong applied knowledge of event-driven architectures and streaming systems (Kafka, Pub/Sub, Kinesis, Rabbit MQ).
• Hands-on experience designing and operating vector search, RAG pipelines, and hybrid retrieval systems.
• Experience with agent frameworks, multi-agent coordination patterns, and long-running agent loops in production environments.

Familiarity with real-time analytics stacks combining streaming data, ML inference, and operational dashboards.