Artificial Intelligence Jobs in Cambridge, MA

Design, develop, test, deploy, and support production AI systems, including foundation-model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI training and inference for scalability, latency, throughput, hardware utilization, and cost. Shape the technical vision and roadmap for foundational AI platforms while partnering with engineers, researchers, program managers, and product leaders. Mentor engineering teams and influence senior cross-functional stakeholders.

Designs, develops, tests, deploys, and supports foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Develops optimization techniques to improve scalability, latency, throughput, hardware utilization, and cost. Partners with engineering, research, product, and program teams; contributes to technical vision and roadmaps; and mentors teams while applying emerging AI research to production systems.

Define Order.co’s company-wide data and applied AI architecture, standards, governance, and investment strategy. Build production AI systems spanning data platforms, retrieval, model serving, evaluation, guardrails, and agentic workflows. Partner with the Head of Data, product, engineering, and operations leaders to fund and deliver initiatives that improve company KPIs. Mentor senior contributors, establish reusable engineering patterns, and ensure AI systems meet security, reliability, observability, privacy, and operational requirements.

Build and support foundational AI platform components, including LLM inference, model training, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize large-scale AI systems for scalability, latency, throughput, hardware utilization, and cost. Contribute to technical strategy and roadmap, collaborate cross-functionally, and lead or mentor engineering teams while applying emerging AI research in production.

Lead the AI strategy, roadmap, architecture, and development of production-grade AI systems within a strategic business domain. Identify high-value opportunities, drive adoption and measurable business outcomes, establish evaluation, testing, guardrail, and observability practices, and mentor AI engineers and agent builders. Partner with product managers, business leaders, and end users to advance AI transformation while ensuring scalable, reliable, and maintainable solutions.

Lead research, development, and deployment of large-scale multimodal foundation models integrating wearable, language, biomarker, clinical, and behavioral data. Develop self-supervised and representation-learning methods, distributed training pipelines, and post-training techniques. Collaborate with engineering, MLOps, data, product, and research teams to operationalize robust, observable, privacy-preserving models and translate capabilities into member-facing features. Contribute to technical roadmaps and architectural decisions for WHOOP’s foundation AI systems.

Develop regulatory strategies for digital health, SaMD, wearable, and AI/ML-enabled products. Advise Product, Engineering, and AI/ML teams on regulatory pathways, claims, intended use, risk classification, software lifecycles, and clinical evidence. Lead or support FDA and EU submissions, including Q-Submissions, 510(k)s, De Novo requests, and EU MDR documentation. Monitor evolving regulations, engage with authorities, and communicate regulatory risks and opportunities to stakeholders and leadership.

Build and maintain backend infrastructure and member-facing generative AI features that deliver personalized health and performance insights. Collaborate with Product, Design, Analytics, and Data Science teams to define requirements and deliver scalable solutions. Develop reliable software using Java, Kafka, AWS, and PostgreSQL; design RESTful APIs and relational databases; optimize performance; and maintain observability and monitoring systems.

Leads and manages an AI Quality engineering team responsible for evaluating and improving AI outputs. Defines testing frameworks for model performance and drift, establishes technical strategy and architecture, provides technical direction, manages technology vendors and partnerships, drives engineering quality improvements, and mentors team members.

Designs and operates Kubernetes clusters on AWS, builds scalable and secure AI runtime infrastructure, and develops tooling for developer productivity and automated development workflows. The role improves platform reliability, supports long-running tasks on cost-efficient infrastructure, partners with application, security, and data teams, participates in incident response, and provides technical leadership and mentorship across the Platform organization.

Build and scale WHOOP AI platform systems and member-facing experiences across the frontend and backend. Design scalable web applications, APIs, and intuitive interfaces; contribute to agent orchestration architecture; translate business requirements into reliable solutions; write tested code; and monitor, debug, and improve production performance and stability. Collaborate with product managers, designers, data scientists, and other cross-functional partners in a fast-paced environment.

Lead governance, risk, and compliance activities for AI/ML systems, LLM integrations, AI agents, and related vendors. Conduct AI risk and third-party assessments, map controls to frameworks, manage remediation, support audits, develop AI-specific policies, report risk metrics, monitor regulatory developments, and coordinate incident governance with Legal, Security, Product, and Engineering teams.

Leads end-to-end design of AI-first consumer product experiences, from research and problem framing through prototypes and polished interfaces. Translates complex AI insights into intuitive, trustworthy workflows; conducts user research and usability testing; and collaborates with product, engineering, and data science teams on production-ready solutions. Advocates for accessible, inclusive, ethical, and transparent AI design while using Figma and AI-enabled tools throughout the design process.

Lead the technical direction and execution of WHOOP’s foundation AI modeling efforts across wearable sensor data, language, biomarkers, and behavioral signals. Drive high-risk research from hypothesis to production, shape AI strategy and infrastructure decisions, collaborate across research, product, and engineering, mentor senior technical talent, establish evaluation standards, and represent WHOOP externally through recruiting, partnerships, and the machine learning research community.

Build and operate production AI systems powering WHOOP Coach, AI Support, and other intelligent product features. Lead AI/ML initiatives across data pipelines, dataset design, LLM fine-tuning, evaluation, deployment, experimentation, observability, and continuous feedback loops. Partner with product and data science to create reliable systems based on member needs and real-world usage. Mentor engineers and data scientists while establishing best practices for applied AI/ML, privacy, security, and production lifecycle management.

Build and maintain performant, responsive, mobile-first web applications using Next.js, React, and Tailwind CSS. Translate product goals into polished member-facing experiences, prototype and ship features with AI-assisted workflows, integrate RESTful APIs, and collaborate with product, design, and backend teams. Establish frontend best practices for architecture, accessibility, performance, and maintainability while contributing to UX decisions and navigating ambiguous problems.

Own the AI product platform, including evaluation methodology, model and provider selection, internal tooling, adoption, and AI unit economics. Partner with engineers, researchers, and data scientists to improve quality, latency, and cost. Drive cross-functional programs, assess emerging ML techniques, and turn ambiguous opportunities into focused bets for internal productivity and member experiences.

Build and lead development of an enterprise AI platform supporting large-scale AI-assisted analysis. Responsibilities include designing, developing, testing, deploying, and supporting foundation model training, LLM inference, similarity search, guardrails, evaluation, experimentation, governance, and observability. The role also requires advancing LLM optimization for scalability, latency, throughput, hardware utilization, and cost while shaping the long-term roadmap for foundational AI systems and mentoring engineering teams.

Design, develop, test, deploy, and support foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize large-scale AI systems for scalability, latency, throughput, hardware utilization, and cost. Partner with engineering, research, product, and program teams; contribute to technical vision and roadmap; and lead or influence teams developing responsible cloud-based AI solutions.

Lead the design, development, deployment, and support of foundational AI systems, including LLM inference, model training, similarity search, guardrails, evaluation, governance, and observability. Optimize production AI systems for scalability, cost, latency, throughput, and hardware utilization. Partner with engineers, scientists, program managers, and product managers while shaping the technical vision and roadmap for responsible AI platforms.

The Distinguished Engineer will drive innovation in banking technology, lead software engineering projects, mentor teams, and utilize AI and advanced architectures for risk management.

Leads clinical informatics strategy and delivery across clinical development programs, including data flows, integrations, analytics, dashboards, IRT, and eCOA systems. Oversees vendors, configurations, testing, validation, data governance, regulatory compliance, audits, and inspection readiness in a GxP environment. Partners with clinical, data management, biostatistics, and drug supply teams to implement scalable solutions, automation, interoperability, and advanced analytics.

Leads a computational sciences team supporting biotherapeutic programs from antibody discovery through candidate nomination. Applies bioinformatics, machine learning, AI-enabled design, and predictive modeling to molecule selection, optimization, experimental planning, and go/no-go decisions. Prioritizes portfolio work, validates computational models against experiments, communicates decision-ready recommendations, and develops computational scientists through mentorship and technical leadership.

Leads Ipsen’s global omnichannel customer experience strategy across pharmaceutical brands and markets. Responsibilities include developing multi-year digital roadmaps, supporting integrated campaigns, driving AI and technology innovation, monitoring KPIs, ensuring regulatory and data privacy compliance, managing agencies and vendors, optimizing campaigns through analytics, and leading a high-performing digital team. The role partners with commercial, medical, legal, regulatory, marketing, global, and local stakeholders to improve engagement with healthcare professionals, patients, and other key audiences.

Design and execute cellular and mechanistic screening assays for antibody-based biologics, including T-cell engagers, antibody-drug conjugates, multispecifics, agonists, and antagonists. Develop cell lines, characterize protein reagents, support target validation, and identify workflow improvements using automation and AI/ML. Represent Functional Sciences on multidisciplinary teams, present findings, document experiments in Electronic Notebook, and manage multiple discovery projects while mentoring junior scientists.