AI Engineer

• AI Implementation - Design, build, and maintain all of our AI systems across the company. Everything from internal marketing, sales, finance, and operations systems to local AI systems in our engineering workflows, and the Chamber itself.
• AI Adoption - Educate, Train, Support, and Uplift everyone in the company with their AI adoption. Running office hours, making tutorials, providing resources, sourcing solutions, and more.
• AI Infrastructure - Architect and operate a scalable set of infrastructure that allows non-technical, and technical teams to use AI in a secure and productive manner.
• AI Systems & Agents - In conjunction with departments, design, build and maintain all AI systems and agents. This includes administering and managing our off the shelf AI systems like Claude, Viktor, Cursor, etc. It also includes our custom built Agents, Skills, Plugins, Tools, and more.
• AI Security - You are responsible for creating, maintaining, and enforcing all data, privacy, and security policies across our AI systems and Agents. Organization, Department, and Role based permissions and settings across all our products.
• Other Projects - As this is a technical role, you will also be asked to help build other internal systems like automations, micro apps, and more.

AI Solutions Lead

• Define product vision and prioritize initiatives across multiple applications
• Oversee requirements for gathering, feature design, and release planning
• Guide product owners and analysts to align deliverables with business outcomes
• Engage with stakeholders at all levels to champion product usability and value
• Coordinate with QA and development managers to ensure on-time delivery

Lead AI Engineer – Capital Project Delivery

Machine Learning Engineer

• Performance optimization, analysis, and tuning of DL models in various domains like LLM, Multimodal and Generative AI.
• Scale performance of DL models across different architectures and types of NVIDIA accelerators.
• Contribute features and code to NVIDIA’s inference libraries, vLLM and SGLang, FlashInfer and LLM software solutions.
• Work with cross-collaborative teams across frameworks, NVIDIA libraries and inference optimization innovative solutions.

Deep Learning Software Engineer, Inference

• Define and evolve the standards, architecture patterns, and tooling choices that govern how engineers use AI across the codebase — including where to invest in codebase readiness (instrumentation, specification, evaluation) versus targeted rewrites.
• Own execution of the multi-milestone plan (readiness → validated → governed) applied to corgi and secondary repos, in partnership with squad leads — without being their manager.
• Decide what AI tooling, agent infrastructure, and evaluation pipelines the org needs, and build or oversee the build of the ones that don't exist yet.
• Set the policy and technical controls for where human-in-the-loop review is required versus where autonomous agent action (PR review, deployment) is safe — resolving the tension between adoption speed and security/quality risk.
• Write the guidance engineers actually use — AGENTS.md/CLAUDE.md conventions, review norms for AI-generated code, eval standards — and make sure it's adopted, not just published.

AI Architect

• Design, build, test, and deploy AI legal workflows and skills across firm-approved AI tools, supporting use cases such as contract review, due diligence, drafting, research, and summarization
• Partner directly with attorneys and knowledge management professionals to identify high-value use cases, map workflows, and translate practice needs into structured AI solutions
• Develop and maintain retrieval pipelines, document templates, and reusable workflow components with clear, attorney-ready documentation
• Build and execute evaluation frameworks to measure workflow quality, accuracy, and reliability; monitor performance and iterate based on feedback and outcomes
• Serve as a go-to resource for enablement and change management for developed workflows, partnering with the AI Adoption team to drive usage, deliver training sessions and workshops, 1:1 coaching, and develop practical playbooks and materials that support sustained adoption
• Develop and scale best-practice standards, use-case libraries, and playbooks that empower attorneys to responsibly and independently leverage AI tools while maintaining quality and risk controls
• Embed human-review checkpoints and audit mechanisms aligned with Firm governance, confidentiality, and professional responsibility requirements
• Collaborate closely with cross-functional stakeholders including the AI Product team, AI Adoption team, Knowledge & Innovation team, IT, Innovation Practice Group, and Professional Development to ensure aligned delivery and support
• Track adoption, usage, and impact metrics for deployed workflows and recommend refinements or expansion opportunities
• Manage ongoing maintenance of production workflows, including updates to prompts, retrieval sources, and guardrails in response to model and platform changes, user feedback, or evolving practice needs
• Represent the AI Products & Adoption team in internal meetings, committees, and practice group sessions

AI Legal Engineer

• Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.
• Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc.
• Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.
• Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
• Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.

Senior Lead AI Engineer – SDKs, Gen AI Evaluation, MCP

• Help turn the models built by our ML Scientists, Data Scientists, and Perception Engineers into reliable, production-grade services.
• Work on the infrastructure, pipelines, and tooling that take a model or an LLM/agent-backed workflow from a research notebook to a fully monitored deployment.
• Partner daily with the R&D team to understand what a model needs to run in production (compute, data inputs, versioning, post-processing).
• Coordinate with the Platform and DevOps teams to provision the infrastructure, permissions, and deployment pathways.
• Maintain and extend the model registry, build and debug deployment pipelines and cloud infrastructure, and set up model and pipeline monitoring and testing.
• Troubleshoot issues, such as failed deployments, permissions errors, or inconsistent environments.
• Help shape and document standards for how models move from staging to production.
• Ensure R&D goals and challenges are well understood by Software Engineering and DevOps teams.

Senior MLOps Engineer

• Research, design, develop, and evaluate signal processing, image processing, and computational algorithms for medical device applications
• Assess technical feasibility, performance, and design tradeoffs of new concepts
• Implement complex real-time or near-real-time algorithms in software
• Analyze bench, preclinical, and clinical data to develop/train algorithms, verify and validate them, and guide technical decision making
• Collaborate with cross-functional partners across engineering, design, clinical, and research teams to translate unmet needs into solutions
• Provide technical leadership and mentorship in signal processing and algorithm development

Principal Algorithm Engineer – Imaging, Ultrasound, ML

• Collaborate with domain experts to understand business needs and translate them into clear and effective LLM prompts.
• Develop, test, and refine prompts for a variety of use cases, ensuring high accuracy and relevance in generated outputs.
• Work closely with software developers to integrate LLM solutions into existing or new applications.
• Contribute to the development of a scalable framework for prompt engineering, promoting reuse and knowledge sharing across different business units.
• Assist in gathering and analyzing user feedback to continually improve prompt effectiveness.
• Stay up-to-date with advancements in LLM technology and contribute to strategic decisions on tool and platform selection.
• Document prompt engineering processes, best practices, and use case outcomes to build a comprehensive knowledge base.

Senior Staff GenAI Engineer

• Ship and debug code on a live, real-time voice pipeline where latency and correctness are user-facing
• Design control systems around LLMs: guardrails, budgets, watchdogs, safe fallbacks
• Build and operate LLM evaluation and batch-analysis pipelines
• Own traditional ML workflows from data to scheduled production inference
• Trace production issues from a metric anomaly to root cause, including building the evidence when the cause is a vendor

Senior Software Engineer – Applied AI

AI Research Scientist

• Develop AI/ML models to predict structural biomolecular interactions for novel modalities leveraging protein-protein interactions.
• Design innovative AI/ML approaches to predict protein cooperativity, affinity & other biological properties based on structures.
• Develop co-folding models that incorporate structural and non-structural priors using combinations of public and proprietary BMS data.
• Develop AI/ML models that leverage chemoproteomics data contributing to the generation of novel druggability hypotheses for hard-to-drug targets.
• Author scientific reports, and present methods, results, and conclusions to publishable standard.
• Contribute to the planning and execution of collaborative projects with leading academic and commercial research groups worldwide.

Senior Scientist, AI Computational Structural Biology

• As a Senior AI Engineer on the AI Venture Studio team, you will be a hands-on senior individual contributor who leans into artificial intelligence (AI) to design and build impactful solutions to transform patients’ lives.
• This role is accountable for the AI-first engineering of cloud-native applications, agentic AI products, and the knowledge and context infrastructure that powers them.
• You will have access to the latest AI-centric tools and technologies to support activities such as the design of APIs and MCPs, cloud services orchestration, agent runtime deployment, workflow pipeline implementation, and reusable platform pattern development that enables AI Accelerator projects to move fast without giving up reliability, observability, security, or enterprise architectural alignment.
• This role lives inside the AI Accelerator delivery model: six fully agile two-week sprints across a twelve-week cycle to build, test, validate, and prepare minimum viable products (MVPs) for broader organizational adoption and scaling.
• AI Accelerator projects focus on the most challenging and highest-upside pharma-specific problems across R&D, Commercialization, Manufacturing, and Enabling Functions where critical context is buried in unstructured knowledge files, multimodal documents and reports, operational records, and scientific evidence packages.
• Ship in cycles by demonstrating engineering progress and lessons learned every two weeks.
• Focus on AI-first solutioning that prioritizes BMS technology investments (AWS, Claude, LangSmith, etc.) with the best chance of meeting use case and project success metrics.
• Collaborate effectively with other engineers (AI, data, UI/UX, machine learning) and broader agile AI accelerator product teams.
• Demonstrate a curious and inquisitive mindset with broad technical adaptability while staying hands-on with frontier AI technologies, AI coding agents, and the latest agentic engineering capabilities.

Senior AI Engineer

• Own stakeholder relationships across Medbridge’s executive team, working with them to find the problems and AI enabled solutions that can expedite, or remove, the current blockers in the business.
• Design, build, and ship AI-powered features across our internal teams, from the user interface to the backend services and AI workflows behind it.
• Build full-stack applications using AI-native engineering workflows and tools such as Claude, Cursor, Codex, and OpenAI and Anthropic APIs.
• Develop APIs, backend services, and automation pipelines that support intelligent product capabilities and production-ready deployments.
• Apply software engineering, DevOps, and MLOps best practices to the testing, deployment, monitoring, evaluation, and optimization of AI solutions.
• Partner closely with our Product and Engineering teams to turn ambiguous goals into scalable internal products.
• Use AI tooling throughout your workflow (coding, code review, testing, and planning) and help push the team’s adoption forward.
• Research emerging AI technologies, LLM implementation patterns, and modern product engineering frameworks, and bring what works back to the team.
• Instrument what you ship and use usage data and evidence to decide what to build, fix, or kill next.

Product Engineer – AI

LLM Engineer

• Define product strategy and roadmap for GPU instances, clusters, and services
• Manage the entire product lifecycle from planning to end-of-life
• Develop and execute go-to-market strategies, messaging, and launch plans
• Collaborate with ecosystem partners on roadmap and technical needs
• Translate AI, HPC and graphics workload needs into specifications and performance goals
• Oversee GPU infrastructure lifecycle components and implement data-driven decisions
• Identify enhancements for proactive monitoring and support processes

Senior Product Manager, AI Infrastructure, GPU

• Design and refine scalable pipelines for training, evaluating, and deploying machine learning models across diverse use cases.
• Build rigorous testing and statistical validation frameworks to measure model performance and ensure reliability in production.
• Optimize models for production readiness, improving computational efficiency, latency, and resource utilization.
• Implement real-time monitoring systems to detect data drift, model decay, and performance degradation, proactively maintaining model accuracy.
• Partner with cross-functional teammates to design, build, and deploy end-to-end machine learning applications that scale with business growth.
• Champion machine learning best practices, including responsible and ethical AI development, documentation, and reproducibility.
• Identify and lead technical initiatives that elevate platform capabilities and advance the team’s machine learning standards.

Senior Machine Learning Engineer

• Collaborate with domain experts to understand business needs and translate them into clear and effective LLM prompts
• Develop, test, and refine prompts for a variety of use cases, ensuring high accuracy and relevance in generated outputs
• Work closely with software developers to integrate LLM solutions into existing or new applications
• Contribute to the development of a scalable framework for prompt engineering, promoting reuse and knowledge sharing across different business units
• Assist in gathering and analyzing user feedback to continually improve prompt effectiveness
• Stay up-to-date with advancements in LLM technology and contribute to strategic decisions on tool and platform selection
• Document prompt engineering processes, best practices, and use case outcomes to build a comprehensive knowledge base

Senior Principal AI Engineer

• Collaborate with domain experts to understand business needs and translate them into clear and effective LLM prompts
• Develop, test, and refine prompts for a variety of use cases, ensuring high accuracy and relevance in generated outputs
• Work closely with software developers to integrate LLM solutions into existing or new applications
• Contribute to the development of a scalable framework for prompt engineering, promoting reuse and knowledge sharing across different business units
• Assist in gathering and analyzing user feedback to continually improve prompt effectiveness
• Stay up-to-date with advancements in LLM technology and contribute to strategic decisions on tool and platform selection
• Document prompt engineering processes, best practices, and use case outcomes to build a comprehensive knowledge base.

• Build statistical models and production systems to balance advertiser performance with business goals.
• Tune optimization parameters, measure internal competition, and model dynamic environments.
• Design and run experiments to validate theories underpinning the mobile ad tech economy.
• Develop applications in the areas of advertiser budget retention and growth, optimal margin allocation, and bidding innovations.
• Collaborate with a team of world-class engineers with diverse backgrounds as well as peers across the broader company (e.g. Operations, GTM).
• Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences.
• Be part of an “engineering excellence” culture through state-of-the-art tools, risk-driven testing, explainable systems, and design/code review.

• Designing and implementing seamless integration solutions and robust platform architectures to optimize technology infrastructure
• Analyzing business requirements and developing technical designs to enable successful delivery of innovative technology solutions
• Leading strategic technology planning and promoting technological advances to create environments where people and technology thrive together
• Overseeing multiple projects and maintaining executive-level client relations to drive business growth and shape client engagements
• Mentoring the next generation of leaders and cultivating potential within teams to uphold PwC's reputation for quality and integrity
• Conducting research and feasibility studies to identify gaps in the market and convert opportunities into success for the firm
• Developing new ideas, solutions, and structures to drive thought leadership and enhance business performance
• Collaborating across the PwC Network to leverage collective strength and maximize client satisfaction

AI Architect Director

• Design, develop, and deploy machine learning models and AI-powered applications
• Collaborate with cross-functional teams to identify and implement AI/ML solutions for client needs
• Build and maintain data pipelines, model training workflows, and inference systems
• Evaluate and integrate large language models (LLMs), generative AI tools, and emerging AI frameworks
• Monitor model performance, conduct testing, and iterate on improvements
• Document technical architectures, methodologies, and best practices
• Stay current with AI/ML research and translate advancements into practical applications

Machine Learning Engineer jobs in Massachusetts

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AI Solutions Lead

Lead AI Engineer – Capital Project Delivery

Deep Learning Software Engineer, Inference

AI Architect

AI Legal Engineer

Senior Lead AI Engineer – SDKs, Gen AI Evaluation, MCP

Senior MLOps Engineer

Principal Algorithm Engineer – Imaging, Ultrasound, ML

Senior Staff GenAI Engineer

Senior Software Engineer – Applied AI

Senior Scientist, AI Computational Structural Biology

Senior AI Engineer

Product Engineer – AI

Senior Product Manager, AI Infrastructure, GPU

Senior Machine Learning Engineer

Senior Principal AI Engineer

Senior Principal AI Engineer

Machine Learning Engineer

AI Architect Director

AI Engineer