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machine learning, software engineer Jobs in Boston

machine learning, software engineer

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ml-ai engineering intern

principal ai ml engineer

Pragmatike

<div>
 <strong>
  Location:
 </strong>
 Cambridge, MA (Eastern Time / UTC -4)
 <strong>
  Relocation package available
  <br/>
  <br/>
 </strong>
 <strong>
  Start date:
 </strong>
 ASAP
 <br/>
 <br/>
 <strong>
  Languages:
 </strong>
 English (required)
 <br/>
 <br/>
 <strong>
  About The Role
  <br/>
  <br/>
 </strong>
 Pragmatike is hiring on behalf of a
 <strong>
  fast-growing AI startup recognized as a Top 10 GenAI company by GTM Capital
 </strong>
 , founded by MIT CSAIL researchers.
 <br/>
 <br/>
 We are seeking a
 <strong>
  Staff / Principal ML Ops Engineer
 </strong>
 to lead the design, implementation, and scaling of the companys ML infrastructure and production AI systems. This is a high-impact, architecture-defining role where youll work across the entire model lifecycletraining, evaluation, deployment, observability, and continuous optimization.
 <br/>
 <br/>
 You will partner closely with AI researchers, GPU systems engineers, backend teams, and product stakeholders to ensure the companys large-scale AI systems are
 <strong>
  robust, efficient, automated, and production-grade
 </strong>
 . This role is ideal for someone who has already built and owned ML platforms at scale and can drive strategy as well as hands-on execution.
 <br/>
 <br/>
 <strong>
  What Youll Do
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Architect, build, and scale the end-to-end ML Ops pipeline, including training, fine-tuning, evaluation, rollout, and monitoring.
  </li>
  <li>
   Design reliable infrastructure for model deployment, versioning, reproducibility, and orchestration across cloud and on-prem GPU clusters.
  </li>
  <li>
   Optimize compute usage across distributed systems (Kubernetes, autoscaling, caching, GPU allocation, checkpointing workflows).
  </li>
  <li>
   Lead the implementation of observability for ML systems (monitor drift, performance, throughput, reliability, cost).
  </li>
  <li>
   Build automated workflows for dataset curation, labeling, feature pipelines, evaluation, and CI/CD for ML models.
  </li>
  <li>
   Collaborate with researchers to productionize models and accelerate training/inference pipelines.
  </li>
  <li>
   Establish ML Ops best practices, internal standards, and cross-team tooling.
  </li>
  <li>
   Mentor engineers and influence architectural direction across the entire AI platform.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  What Were Looking For
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Deep hands-on experience designing and operating production ML systems at scale (Staff/Principal-level expected).
  </li>
  <li>
   Strong background in ML Ops, distributed systems, and cloud infrastructure (AWS, GCP, or Azure).
  </li>
  <li>
   Proficiency with Python and familiarity with TypeScript or Go for platform integration.
  </li>
  <li>
   Expertise in ML frameworks: PyTorch, Transformers, vLLM, Llama-factory, Megatron-LM, CUDA / GPU acceleration (practical understanding)
  </li>
  <li>
   Strong experience with containerization and orchestration (Docker, Kubernetes, Helm, autoscaling).
  </li>
  <li>
   Deep understanding of ML lifecycle workflows: training, fine-tuning, evaluation, inference, model registries.
  </li>
  <li>
   Ability to lead technical strategy, collaborate cross-functionally, and operate in fast-paced environments
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Bonus Points
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Experience deploying and operating LLMs and generative models in production at enterprise scale.
  </li>
  <li>
   Familiarity with DevOps, CI/CD, automated deployment pipelines, and infrastructure-as-code.
  </li>
  <li>
   Experience optimizing GPU clusters, scheduling, and distributed training frameworks.
  </li>
  <li>
   Prior startup experience or comfort operating with ambiguity and high ownership.
  </li>
  <li>
   Experience working with data engineering, feature pipelines, or real-time ML systems.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Why This Role Will Pivot Your Career
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Research pedigree: MIT CSAIL founders recognized for breakthrough AI and systems contributions.
  </li>
  <li>
   Customer impact: Deploy AI solutions powering Fortune 500 clients.
  </li>
  <li>
   Industry momentum: Lab alumni have led high-value acquisitions (MosaicML Databricks, Run:AI Nvidia, W&amp;B CoreWeave).
  </li>
  <li>
   Funding &amp; growth: Oversubscribed seed round, next funding in 2026.
  </li>
  <li>
   Career growth &amp; influence: Lead AI initiatives, optimize pipelines, and directly impact production AI systems at scale.
  </li>
  <li>
   Culture &amp; autonomy: Own critical systems while collaborating with world-class engineers.
  </li>
  <li>
   Aspirational impact: Solve AI performance challenges few engineers ever face.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Benefits
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Competitive salary &amp; equity options
  </li>
  <li>
   Sign-on bonus
  </li>
  <li>
   Health, Dental, and Vision
  </li>
  <li>
   401k
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  <em>
   Pragmatike
  </em>
 </strong>
 <em>
  is an Equal Opportunity Employer and is committed to providing equal employment opportunities to all applicants without discrimination. We recruit on behalf of our clients and prohibit discrimination and harassment based on race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.We are committed to a fair and inclusive hiring process. We process your personal data solely for recruitment purposes, in accordance with applicable privacy laws, and maintain reasonable safeguards to protect your information. Your data may be shared with our client(s) for hiring consideration, but will not be disclosed to third parties outside of the recruitment process.
 </em>
</div>


Principal AI/ML Engineer

Rapid7

<div><h1 class="jobSectionHeader"><b>Principal AI Engineer &ndash; AI Centre of Excellence</b></h1><p></p><p>
We are expanding our global AI footprint and are looking for a passionate Principal AI Engineer to join Rapid7&rsquo;s AI Centre of Excellence. You should be ready to provide technical guidance and mentoring to the team while understanding the trade-offs between technical, UX, and product needs. You should excel in interacting and negotiating with key stakeholders, leading to solutions that take all of these needs into account.</p><p></p><h2 class="jobSectionHeader"><b>
About the Team</b></h2><p>
The AI Centre of Excellence designs, prototypes, and ships production AI models and systems. Our mission is to leverage the best available technology to protect our customers&rsquo; attack surfaces. For example, we partner closely with Rapid7&rsquo;s Detection and Response teams, including our MDR service, to leverage AI/ML for enhanced customer security and threat detection.</p><p>
We operate with a creative, iterative approach, building on over 20 years of threat analysis and a growing patent portfolio. The environment is collaborative, focused on knowledge sharing and continuous learning. If you&rsquo;re passionate about AI and want to make a major impact in a fast-paced, innovative environment, this is your opportunity.</p><p></p><h2 class="jobSectionHeader"><b>
About the Role</b></h2><p>
The Principal AI Engineer is part of our AI Centre of Excellence team at the forefront of designing and delivering customer-focused AI systems. In this role, you will design and implement AI models and systems using the most appropriate techniques&mdash;conventional machine learning, deep learning neural networks, or generative and agentic AI.</p><p>
You will be working on a high-impact, cross-functional team, ensuring our AI solutions are scalable, secure, and deliver real customer value. Speed to market is key: you&rsquo;ll operate in an agile, iterative way, making fast, informed decisions about what to build, what to pause, and where to innovate further.</p><p>
You will have the opportunity to further enhance your skills surrounded by an incredibly talented group of engineers and data scientists, while mentoring others and driving forward Rapid7&rsquo;s AI innovation.</p><p></p><h2 class="jobSectionHeader"><b>
In This Role, You Will:</b></h2><ul>
<li><p>
Develop and ship AI systems with a focus on delivering customer value.</p></li>
<li><p>
Tackle a diverse range of complex problems, often reframing existing solutions with new perspectives.</p></li>
<li><p>
Take ownership of multiple projects as the principal go-to expert.</p></li>
<li><p>
Act as a first-class stakeholder throughout the AI R&amp;D process.</p></li>
<li><p>
Build strong cross-functional relationships with engineering, UX, product management, and customer research teams.</p></li>
<li><p>
Mentor colleagues with less experience, acting as a trusted technical resource.</p></li>
</ul><p></p><h2 class="jobSectionHeader"><b>
The Skills You&rsquo;ll Bring Include:</b></h2><p><b>
Technical Expertise</b></p><ul>
<li><p>
Significant hands-on experience in applied AI, able to independently design and build AI models and systems.</p></li>
<li>
<p>Strong Python skills and practical expertise with:</p>
<ul>
<li><p>scikit-learn for conventional ML and data science.</p></li>
<li><p>
PyTorch, TensorFlow, or Keras for deep learning.</p></li>
<li><p>
AWS Bedrock, SageMaker, HuggingFace, or Transformers for LLM-based solutions.</p></li>
<li><p>
CrewAI (or similar frameworks) for agentic AI projects.</p></li>
<li><p>
Pandas/NumPy for data processing and transformation.</p></li>
</ul>
</li><li><p>Intuition for selecting the right models and approaches for different use cases.</p></li>
<li><p>
Cybersecurity expertise is a plus, but experience from other domains (e.g., finance, healthcare, marketplaces, insurance, legal, social media) is also highly valuable.</p></li>
</ul><p></p><p><b>
Personal Attributes</b></p><ul>
<li><p>
Positive, can-do, solution-oriented mindset with persistence in tackling complex challenges.</p></li>
<li><p>
Bias for action and delivery, balancing speed with care.</p></li>
<li><p>
Ability to thrive in a fast-paced environment, juggling multiple projects effectively.</p></li>
<li><p>
Natural leader and collaborator, helping peers grow as problem solvers and communicators.</p></li>
<li><p>
Curiosity and passion for continuous learning and self-development.</p></li>
<li><p>
Open-minded approach, valuing feedback and new ideas from colleagues.</p></li>
<li><p>
Strong interpersonal and communication skills, with the ability to explain complex AI concepts to diverse audiences and document work clearly.</p></li>
</ul><p></p><p>
#LI-WP1</p><p></p><h2 class="jobSectionHeader"><b>
About Rapid7</b></h2><p>
At Rapid7, our vision is to create a secure digital world for our customers, our industry, and our communities. We do this by harnessing our collective expertise and passion to challenge what&rsquo;s possible and drive extraordinary impact. We&rsquo;re building a dynamic and collaborative workplace where new ideas are welcome.</p><p></p><p>
Protecting 11,000+ customers against bad actors and threats means we&rsquo;re continuing to push the envelope just like we&rsquo; ve been doing for the past 20 years. If you &rsquo;re ready to solve some of the toughest challenges in cybersecurity, we&rsquo;re ready to help you take command of your career. Join us.</p><p></p><p>
Rapid7, Inc. is committed to fair and equitable compensation practices. A candidate&rsquo;s salary is determined by various factors including, but not limited to, relevant work experience, skills, and certifications. We evaluate compensation decisions on a case-by-case basis, and it is not typical for an individual to be hired at the very top of the salary range.</p><p></p><p>
The salary range for this role in the US is:</p>
$236,500.00 - 320,000.00 USD Annual<p></p><p>
Salary ranges may vary based on geographical location. This range does not include variable/incentive compensation, equity and benefits (where applicable/eligible).</p><p></p><p><i>
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or any other status protected by applicable national, federal, state or local law.</i></p></div>

Principal AI Engineer

Bain & Company

<div><p><b>What Makes Us A Great Place To Work</b></p><p>
We are proud to be consistently recognized as one of the world&rsquo;s best places to work. We are currently the top ranked consulting firm on Glassdoor&rsquo;s Best Places to Work list and have earned the #1 spot a record seven times. Extraordinary teams are at the heart of our business strategy, but these don&rsquo;t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.</p><p></p><p><b><br>
About Bain AI, Insights &amp; Solutions (AIS)</b></p><p>
Bain&rsquo;s AI, Insights &amp; Solutions (AIS) team works with clients to design and deliver AI-powered solutions that create measurable business impact. You&rsquo;ll operate in multidisciplinary teams alongside Bain consultants, other experts in product, design, architecture and engineering, and client stakeholders, translating ambiguous business problems into robust AI applications that can be piloted, scaled, and adopted.</p><p></p><p><b><br>
The Impact You&rsquo;ll Have</b></p><p>
Bain works with clients on board-level and executive priorities, helping deliver step-change results across growth, productivity, and resilience. In that context, AI is rarely a point solution. The most meaningful outcomes come from building AI as part of an integrated system that combines technology with redesigned processes, operating model changes, and adoption at scale across the organization.</p><p></p><p><br>
As an ML-AI Engineer Intern in AIS, you will build the technical core of these transformations and work as part of broader Bain consulting teams to move solutions from prototype to real adoption. The result is measurable impact at the company or enterprise level and, in many cases, helps clients set new performance standards for their industries.</p><p></p><p><b><br>
The Role</b></p><p>
We are hiring an ML-AI Engineer Intern to help build GenAI and agentic AI applications for enterprise use cases, ranging from rapid proofs of concept (POCs) to MVPs and, where appropriate, scaled production deployments. You will design and implement LLM-driven applications and agentic workflows that use tools, data, and enterprise systems to execute multi-step tasks reliably and safely.</p><p></p><p><br>
While GenAI and agentic AI are the primary focus, you will also draw on data science and ML engineering skills as needed, including building evaluation approaches, working with data pipelines, and developing or integrating ML models when they materially improve performance or reliability.</p><p></p><p><br>
You will have opportunities to work with major AI ecosystem partners through Bain&rsquo;s partnerships, collaborating on real client deployments and helping shape how emerging capabilities are applied in enterprise settings.</p><p></p><p><br>
Bain offers significant learning and growth opportunities through the breadth and depth of problems we solve, the level of impact we help clients achieve, and our apprenticeship model. You will learn by doing, with support from experienced teammates, frequent feedback, and increasing responsibility over time.</p><p></p><p><b><br>
What You&rsquo;ll Do</b></p><p><b>
Build AI applications that drive real business outcomes</b></p><ul><li>
Contribute to the design and development of GenAI applications (e.g., copilots, workflow automation, decision support) using modern LLM stacks.</li><li>
Support the implementation of agentic workflows where they add clear value (e.g., tool use, multi-step execution, human-in-the-loop controls), with attention to reliability, safety, and clear failure modes.</li><li>
Assist in building robust agent capabilities including context engineering, memory/state management (short-term and long-term), orchestration, routing, and tool integration patterns.</li></ul><p><b>
Build and apply data science and machine learning capabilities</b></p><ul><li>
Contribute to ML solutions end-to-end: data preparation, feature engineering, model selection, training, validation/testing, and performance analysis.</li><li>
Apply appropriate ML methods for the problem, spanning classical ML and deep learning (including sequence, text, and image models when relevant).</li><li>
Develop working knowledge of modern deep learning concepts, including transformer fundamentals and LLM pre-training vs post-training concepts (e.g., instruction tuning, preference optimization).</li></ul><p><b>
Engineer for real delivery: POC MVP production</b></p>
Write clean, testable, maintainable code and ship AI services through the full SDLC: build test deploy monitor<ul><li> iterate.</li><li>
Support implementation of MLOps and GenAIOps practices: CI/CD, reproducibility, environment parity, model/prompt/agent versioning, and operational readiness.</li><li>
Contribute to evaluation and observability for GenAI and agentic systems: tracing and instrumentation, regression test suites, automated scoring where appropriate, and iteration loops for prompt/policy optimization.</li><li>
Gain exposure to secure enterprise deployment practices, including access controls, auditability, data handling for sensitive/PII data, and responsible AI guardrails.</li><li>
Contribute to reusable components and accelerators (templates, evaluation harnesses, connectors, orchestration patterns) that scale across client contexts.</li></ul><p><b>
Thrive in a consulting environment</b></p><ul><li>
Communicate clearly with technical and non-technical audiences; present in working sessions, share recommendations, and help write crisp technical documentation.</li><li>
Support client discussions, demos, and documentation development as part of broader engagement teams.</li></ul><p><b>
What We&rsquo;re Looking For (Qualifications)</b></p><p><b>
Educational background</b></p><ul><li>
Currently pursuing or recently completed a bachelor&rsquo;s, master&rsquo;s, or PhD in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Engineering, or a related quantitative field; or equivalent early-career industry experience.</li><li>
Strong academic performance or demonstrated excellence through research, projects, internships, competitions, or open-source contributions.</li></ul><p><b>
Core engineering + AI application skills</b></p><ul><li>
0&ndash;2+ years of professional AI / ML engineering experience or equivalent experience through internships, research, academic projects, or industry roles, with strong backend engineering fundamentals.</li><li>
Proficiency in Python and some experience building APIs/services (e.g., REST/gRPC) or comparable project-based experience.</li><li>
Hands-on experience (academic, internship, or industry) building ML or LLM-powered applications, preferably with exposure to delivery considerations such as latency, cost, reliability, or security.</li><li>
Exposure to retrieval/search systems (e.g., hybrid retrieval, vector search, reranking), and familiarity working with structured and unstructured data stores.</li><li>
Exposure to agentic patterns (context management, tool integration, orchestration, memory/state handling); hands-on experience (academic, internship, or industry) preferred.</li><li>
Strong engineering practices: testing, code review, version control, CI/CD, and performance profiling.</li></ul><p></p><p><b><br>
Breadth of knowledge across data science and machine learning</b></p><ul><li>
Experience training, validating, and testing ML models; strong understanding of overfitting, generalization, and evaluation methodology.</li><li>
Practical experience with feature engineering and data preprocessing.</li><li>
Familiarity with a broad set of ML algorithms (classical ML and deep learning), and the ability to choose methods that match constraints.</li></ul><p><b>
Delivery mindset and consulting skills</b></p><ul><li>
Excellent interpersonal and communication skills, able to explain technical decisions, tradeoffs, and results to mixed audiences.</li><li>
Interest in applying AI to solve real-world business problems in a client-facing environment.</li></ul><p></p><p><b><br>
Working Model &amp; Travel</b></p><ul><li>
This role requires a minimum of three days per week working together in person, either at a client location or at your Bain home office.</li><li>
Travel may be required beyond your home office / primary working location. Frequency and destination vary by project needs.</li></ul><p><b>
U.S. Compensation Information</b></p><p>
For all locations, the good-faith reasonable annualized full-time compensation for this role is commensurate with competitive geographic market rates. Additionally, in some locations compensation may vary based on several factors including, but not limited to relevant experience, education, licensure/certifications, training and skill level.</p><p>
Compensation for this role in the United States includes a monthly base salary of $9,000 and Bain's best-in-class benefits package (details listed below).
</p><ul><li>Bain &amp; Company's comprehensive benefits and wellness program is designed to help employees achieve personal independence, protection and stability in the areas most important to you and your family.</li><li>
Bain pays 100% individual employee premiums for medical, dental and vision programs, offering one of the most comprehensive medical plans for employees without impacting your paycheck</li></ul><p></p><p><i><br>
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.</i></p></div>

ML-AI Engineering Intern

Vertex Pharmaceuticals

<div><p><b>Job Description
</b></p><p>We are seeking a unique individual with a passion for data and AI technology who can help our team drive forward enterprise-scale AI adoption of our data &amp; AI platform. Vertex Pharmaceuticals is in a transformational period where we are accelerating our capabilities, technologies, and data to augment our scientific mission, enable Vertex to grow in scale, and continue to be on the forefront of science, medicine, and technology. As part of this effort, it is critical that users have data &ldquo;at their fingertips&rdquo;, with the patterns and technology to support AI adoption at scale.
</p><p>As a Senior Principal AI Engineer in the Data Platforms team, you will unlock and accelerate AI by designing the reusable components, starter kits, and opinionated developer first patterns for our Data Platform. This person will be a key contributing member of the Data Platform, driving forward the deployment and management of Vertex&rsquo;s enterprise platform for data &amp; AI. This role operates at the platform level, shaping how teams build AI on the platform by turning best practices into predictable and repeatable patterns embedding governance into code, and reducing time to value for new use cases. You will work horizontally across platform engineering, governance, and product teams to act as a technical multiplier, enabling dozens of teams across Vertex to ship safely, consistently, and securely. To support enablement, you will advise and influence high-impact initiatives across the organization and lead a scaled enablement program to deliver AI on the Vertex Data Platform.
</p><p></p><p><b>Key Duties &amp; Responsibilities:
</b></p><ul><li><p><b>Platform Management </b>&ndash; Design, support, and continuously enhance Vertex&rsquo;s enterprise data and AI platform to enable scalable and efficient AI adoption across the organization.
</p></li></ul><ul><li><p><b>Design and Build </b>&ndash; Design, build, and maintain production-grade AI starter kits to streamline the development and deployment of AI solutions.
</p></li></ul><ul><li><p><b>Reference Implementations </b>&ndash; Deliver end-to-end reference implementations for AI workloads, providing teams with clear examples and best practices for leveraging the platform.
</p></li></ul><ul><li><p><b>Delivery Management </b>&ndash; Partner with platform engineering, governance, and product teams to deliver data solutions that accelerate AI development and adoption, reducing time-to-value for new use cases.
</p></li></ul><ul><li><p><b>Solution Packaging </b>&ndash; Package solutions as templated, internal tooling, and opinionated defaults that align with enterprise standards, ensuring consistency and ease of use across teams.
</p></li></ul><ul><li><p><b>Team Enablement </b>&ndash; Act as a technical multiplier by providing reusable architecture patterns, starter kits, and developer-first abstractions to empower teams across Vertex to build and deploy AI solutions effectively.
</p></li></ul><ul><li><p><b>Technology Champion </b>&ndash; Advocate for innovative, cloud-first approaches to data and AI platform development, driving forward Vertex&rsquo;s technological capabilities to support future growth.
</p></li></ul><ul><li><p><b>Innovation Champion </b>&ndash; Identify and implement process enhancements and opportunities to improve Vertex&rsquo;s data and AI platform, ensuring alignment with best practices and industry standards.
</p></li></ul><ul><li><p><b>Enablement Champion </b>&ndash; Lead scalable enablement programs across the Vertex Data Platform to drive consistency, quality, and reuse of AI platform patterns.
</p></li></ul><p></p><p><b>Knowledge and Skills:
</b></p><ul><li><p><b>Technical Expertise </b>&ndash; Hands-on experience with AI technologies, particularly GenAI, LLM, Agentic AI, Dev/DataOps, or MLOps, and a solid understanding of enterprise software development tools and methodologies. Experience with Snowflake or Databricks is required.
</p></li><li><p><b>Workflow Tools </b>&ndash; Working knowledge of key workflow tools, including JIRA and Confluence, to support project management and collaboration.
</p></li><li><p><b>Product and Solution Management </b>&ndash; Experience managing the creation and grooming of epics and themes for product and Solution roadmaps.
</p></li></ul><ul><li><p><b>Project Management </b>&ndash; Demonstrated ability to work independently and manage multiple projects requiring collaboration across functional areas.
</p></li></ul><ul><li><p><b>Team Collaboration </b>&ndash; Skillful and collaborative team player with the ability to develop rapport and credibility with stakeholders.
</p></li></ul><ul><li><p><b>Mentorship and Support </b>&ndash; Proven ability and willingness to teach, engage, and support others as they learn new technologies and concepts.
</p></li></ul><ul><li><p><b>Adaptability and Problem-Solving </b>&ndash; Enthusiasm for and ability to quickly learn new technologies and tackle complex challenges.
</p></li></ul><ul><li><p><b>Communication Skills </b>&ndash; Strong presentation, verbal, and written communication skills to effectively convey technical concepts to diverse audiences.
</p></li></ul><p></p><p><b>Education and Experience:
</b></p><ul><li><p>Successful track record with 10+ years of experience working with data, advanced analytics, and 5+ years of developing cloud-based data products.
</p></li><li><p>Strong experience working with AI/ML and GenAI products
</p></li></ul><ul><li><p>5+ years of experience delivering projects in agile environments, with a strong understanding of agile methodologies and practices.
</p></li><li><p>Bachelor's degree in computer science, Software Engineering, or related field, or relevant years of experience
</p></li></ul><p></p><p><b>Pay Range:
</b></p>$188,000 - $282,000
<p></p><p><b>Disclosure Statement:
</b></p><div><div><div><div><div><div><div><div><p>The range provided is based on what we believe is a reasonable estimate for the base salary pay range for this job at the time of posting. This role is eligible for an annual bonus and annual equity awards. Some roles may also be eligible for overtime pay, in accordance with federal and state requirements. Actual base salary pay will be based on a number of factors, including skills, competencies, experience, and other job-related factors permitted by law.
</p><p></p><p>At Vertex, our Total Rewards offerings also include inclusive market-leading benefits to meet our employees wherever they are in their career, financial, family and wellbeing journey while providing flexibility and resources to support their growth and aspirations. From medical, dental and vision benefits to generous paid time off (including a week-long company shutdown in the Summer and the Winter), educational assistance programs including student loan repayment, a generous commuting subsidy, matching charitable donations, 401(k) and so much more.
</p></div></div></div></div></div></div></div></div><p></p><p><b>Company Information
</b></p><p>Vertex is a global biotechnology company that invests in scientific innovation.
</p><p></p><p>Vertex is committed to equal employment opportunity and non-discrimination for all employees and qualified applicants without regard to a person's race, color, sex, gender identity or expression, age, religion, national origin, ancestry, ethnicity, disability, veteran status, genetic information, sexual orientation, marital status, or any characteristic protected under applicable law. Vertex is an E-Verify Employer in the United States. Vertex will make reasonable accommodations for qualified individuals with known disabilities, in accordance with applicable law.
</p><p></p><p>Any applicant requiring an accommodation in connection with the hiring process and/or to perform the essential functions of the position for which the applicant has applied should make a request to the recruiter or hiring manager, or contact Talent Acquisition at ApplicationAssistance@vrtx.com</p></div>

Senior Principal AI Platform Engineer, Data & AI Platform

Edison Smart®

<div>
 <p>
  <strong>
   Senior Robotics ML Engineer | Autonomous Systems | Boston, MA (Hybrid) | 6-12 Month Contract
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Location:
  </strong>
  Boston, MA (Hybrid)
 </p>
 <p>
  <strong>
   Contract Type:
  </strong>
  Contract
 </p>
 <p>
  <strong>
   Rate:
  </strong>
  $90&ndash;$130/hr (W2 or 1099)
 </p>
 <p>
  <strong>
   Duration:
  </strong>
  6-12 months (extension possible)
 </p>
 <p>
  <strong>
   Right to Work:
  </strong>
  Must be authorized to work in the United States
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Project Overview
  </strong>
 </p>
 <p>
  Join a cutting-edge robotics and autonomous systems program focused on deploying ML-driven perception and decision-making models on real-world robotic platforms operating in production environments.
 </p>
 <p>
  <br/>
 </p>
 <p>
  This program is moving beyond experimentation and PoCs, with a strong emphasis on
  <strong>
   production-grade ML
  </strong>
  ,
  <strong>
   edge deployment
  </strong>
  , and
  <strong>
   robustness under real-world constraints
  </strong>
  such as latency, compute limits, and sensor noise.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Key Responsibilities
  </strong>
 </p>
 <ul>
  <li>
   Lead design, training, and iteration of ML models for robotic perception, including object detection, segmentation, and tracking
  </li>
  <li>
   Deploy and maintain ML models on edge devices within robotic or autonomous platforms
  </li>
  <li>
   Optimize inference performance to meet latency, throughput, and compute constraints
  </li>
  <li>
   Evaluate and improve model performance using real-world sensor data
  </li>
  <li>
   Collaborate with autonomy, robotics, and systems teams to integrate ML outputs into downstream decision-making pipelines
  </li>
  <li>
   Mentor junior engineers and support post-deployment monitoring and continuous model improvement
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Required Skills &amp; Experience
  </strong>
 </p>
 <ul>
  <li>
   Extensive hands-on experience with Python and PyTorch or TensorFlow
  </li>
  <li>
   Proven track record deploying ML models into production environments (beyond research or notebooks)
  </li>
  <li>
   Strong practical experience applying computer vision or multimodal ML to real-world robotic or sensor data
  </li>
  <li>
   Deep understanding of ML trade-offs, including accuracy vs latency, model size, and compute efficiency
  </li>
  <li>
   Experience working where ML models interact with physical systems in real-time
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Tech Stack:
  </strong>
  Python, PyTorch or TensorFlow, Computer Vision, Edge ML, ROS2 , Sim2Real
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Senior Robotics ML Engineer | Autonomous Systems | Boston, MA (Hybrid) | 6-12 Month Contract
  </strong>
 </p>
</div>


Senior Robotics ML Engineer | Boston, MA

QuEra Computing

<div><p><b>Summary</b></p>
<p>
QuEra Computing is seeking a mission-driven Quantum Scientist focused on designing and delivering specialized content for community engagement, customer training, and AI-enabled learning experiences. You'll use your expertise in quantum information, machine learning, and modern AI systems to create learning pathways that bridge users to the latest outcomes in quantum computing research &ndash; particularly that by QuEra's team &ndash; via our Bloqade software suite and our current and upcoming neutral-atom hardware.</p>
<p>
In addition to building world-class educational content, you will help integrate AI agents, ML-driven analytics, and scalable infrastructure into our enablement ecosystem&mdash;enhancing user onboarding, experimentation workflows, and community engagement.</p>
<p>
Deliverables include lessons, tutorials, notebooks, AI-assisted labs, hackathon challenges, interactive agents, and technical presentations tailored to users across academia, national labs, and industry.</p>
<p>
Some travel may be required for workshops, conferences, and hackathons.</p>
<p><b>
Responsibilities</b></p>
<ul>
<li>Design, author, and maintain structured, hands-on learning materials (courses, tutorials, notebooks, problem sets, quizzes, and challenge tracks) aligned with neutral-atom quantum computing capabilities.</li>
<li>Develop AI-enhanced learning tools, including intelligent tutoring workflows, agent-assisted coding labs, and automated feedback systems.</li>
<li>Build and deploy lightweight ML or AI agent systems that support user onboarding, documentation navigation, experiment configuration, or workflow optimization.</li>
<li>Produce clear, accurate technical presentations and demos for customers, partners, conferences, and hackathons; deliver live and online instruction.</li>
<li>Closely collaborate with the applications research, product and solutions teams to ensure content reflects the latest algorithms, applications, APIs, and hardware constraints.</li>
<li>Translate new R&amp;D into learner pathways; define objectives and assessments; use learner data, ML-driven insights, and feedback to iterate and improve.</li>
<li>Contribute to scalable infrastructure for educational environments (cloud-based notebooks, APIs, model endpoints, experiment orchestration systems).</li>
<li>Drive customer/partner enablement and community programs (hackathons, forums, workshops) and expand documentation to lower adoption barriers.</li>
<li>Explore and prototype AI agents that assist users in quantum program synthesis, parameter optimization, debugging, or workflow automation.</li>
</ul>
<p><b>Qualifications</b></p>
<ul>
<li>Advanced experience in Physics, Computer Science, Electrical Engineering, Machine Learning, or a related field (PhD preferred; MSc/BSc with equivalent experience considered).</li>
<li>Strong foundation in quantum information/science, spanning algorithmic building blocks, software tooling, data analysis, and hardware limitations.</li>
<li>Experience using machine learning systems, AI agents, LLM-based tooling, or intelligent automation frameworks.</li>
<li>Familiarity with modern ML/AI infrastructure (e.g., model APIs, vector databases, agent orchestration frameworks, cloud deployment, experiment tracking).</li>
<li>Experience creating technical education materials or teaching (tutorials, workshops, developer content), with demonstrated interest in research-informed pedagogy.</li>
<li>Excellent written and verbal communication skills; ability to explain complex topics to diverse technical audiences.</li>
<li>Collaborative mindset and the discipline to independently deliver high-quality technical and educational artifacts.</li>
</ul>
<p><b>Preferred Qualifications</b></p>
<ul>
<li>Experience with Bloqade or other QIS tooling for scientific computing.</li>
<li>Familiarity with neutral-atom quantum computing platforms.</li>
<li>Experience applying ML techniques and agents to scientific and quantum computing, particularly in the context of education.</li>
<li>Experience building or deploying AI agents (e.g., retrieval-augmented systems, code assistants, orchestration frameworks).</li>
<li>Experience with MLOps, infrastructure-as-code, containerization, or cloud-based notebook environments.</li>
<li>Track record presenting at conferences, running hackathons, or building developer communities.</li>
<li>Comfort using data analytics and ML methods to evaluate learning outcomes and improve curricula.</li>
</ul>
<p>The approximate base salary range for this position is $130,400 - $211,900.</p>
<p>
We consistently monitor external market data and update base salary ranges accordingly. We determine base compensation decisions on several factors, including as geographic placement, role-specific knowledge, skills, and/or experience. In addition to our base salary offerings, we also provide equity grants for all new hires.</p>
<p>
QuEra is committed to cultivating a diverse work environment and is proud to be an equal opportunity employer. We highly value diversity in our current and future employees and do not discriminate (including in our hiring and promotion practices) based on race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law.</p>
<p>
#LI-NB1</p></div>

Quantum Scientist – Community, AI & Learning Systems

Red Hat

<div><div><div>Job Summary</div></div><div></div><div><div><div><div><div><div><div><div><div><div>
At Red Hat we believe the future of AI is open and we are on a mission to bring the power of open-source LLMs and vLLM to every enterprise. The Red Hat AI Inference Engineering team accelerates AI for the enterprise and brings operational simplicity to GenAI deployments. As leading developers, maintainers of the vLLM and LLM-D projects, and inventors of state-of-the-art techniques for model quantization and sparsification, our team provides a stable platform for enterprises to build, optimize, and scale LLM deployments.</div><div></div><div>
As a Senior Machine Learning Engineer focused on distributed vLLM infrastructure in the llm-d project, you will be at the forefront of innovation, collaborating with our team to tackle the most pressing challenges in scalable inference systems and Kubernetes-native deployments. Your work with machine learning, distributed systems, high performance computing, and cloud infrastructure will directly impact the development of our cutting-edge software platform, helping to shape the future of AI deployment and utilization. If you want to solve cutting edge problems at the intersection of deep learning, distributed systems, and cloud-native infrastructure the open-source way, this is the role for you.</div><div></div><div>
Join us in shaping the future of AI!</div><div></div><div>
What you will do</div><ul><li><div>
Contribute to the design, development, and testing of new features and solutions for Red Hat AI Inference</div></li><li><div>
Innovate in the inference domain by participating in upstream communities</div></li><li><div>
Design, develop, and maintain distributed inference infrastructure leveraging Kubernetes APIs, operators, and the Gateway Inference Extension API for scalable LLM deployments.</div></li><li><div>
Develop and maintain system components in Go and/or Rust to integrate with the vLLM project and manage distributed inference workloads.</div></li><li><div>
Develop and maintain KV cache-aware routing and scoring algorithms to optimize memory utilization and request distribution in large-scale inference deployments.</div></li><li><div>
Enhance the resource utilization, fault tolerance, and stability of the inference stack.</div></li><li><div>
Develop and test various inference optimization algorithms.</div></li><li><div>
Actively participate in technical design discussions and propose innovative solutions to complex challenges for high impact projects</div></li><li><div>
Contribute to a culture of continuous improvement by sharing recommendations and technical knowledge with team members</div></li><li><div>
Collaborate with product management, other engineering and cross-functional teams to analyze and clarify business requirements</div></li><li><div>
Communicate effectively to stakeholders and team members to ensure proper visibility of development efforts</div></li><li><div>
Mentor and coach a distributed team of engineers</div></li><li><div>
Provide timely and constructive code reviews</div></li><li><div>
Represent RHAI in external engagements including industry events, customer meetings, and open source communities</div></li></ul><div><br>
</div><div>What you will bring</div><ul><li><div>
Strong proficiency in Python and GoLang or similar</div></li><li><div>
Experience with cloud-native Kubernetes service mesh technologies/stacks such as Istio, Cilium, Envoy (WASM filters), and CNI.</div></li><li><div>
A solid understanding of Layer 7 networking, HTTP/2, gRPC, and the fundamentals of API gateways and reverse proxies.</div></li><li><div>
Knowledge of serving runtime technologies for hosting LLMs, such as vLLM, SGLang, TensorRT-LLM, etc.</div></li><li><div>
Excellent written and verbal communication skills, capable of interacting effectively with both technical and non-technical team members.</div></li><li><div>
Experience providing technical leadership in a global team</div></li><li><div>
Autonomous work ethic and the ability to thrive in a dynamic, fast-paced environment</div></li></ul><div><br>
</div><div>Following is considered a plus</div><ul><li><div>
Strong proficiency in Rust, C, or C++</div></li><li><div>
Working knowledge of high-performance networking protocols and technologies including UCX, RoCE, InfiniBand, and RDMA is a plus.</div></li><li><div>
Deep experience with the Kubernetes ecosystem, including core concepts, custom APIs, operators, and the Gateway API inference extension for GenAI workloads.</div></li><li><div>
Experience with GPU performance benchmarking and profiling tools like NVIDIA Nsight or distributed tracing libraries/techniques like OpenTelemetry.</div></li><li><div>
Experience in writing high performance code for GPUs and deep knowledge of GPU hardware</div></li><li><div>
Strong understanding of computer architecture, parallel processing, and distributed computing concepts</div></li><li><div>
Bachelor's degree in computer science or related field is an advantage, though we prioritize hands-on experience</div></li><li><div>
Active engagement in the ML research community (publications, conference participation, or open source contributions) is a significant advantage</div></li></ul><div></div><div>
#AI-HIRING</div><div>
#LI-MD2</div></div></div></div></div></div></div></div></div></div><div></div>
The salary range for this position is $170,770.00 - $281,770.00. Actual offer will be based on your qualifications.<div></div><div>
Pay Transparency</div><div>
Red Hat determines compensation based on several factors including but not limited to job location, experience, applicable skills and training, external market value, and internal pay equity. Annual salary is one component of Red Hat&rsquo;s compensation package. This position may also be eligible for bonus, commission, and/or equity. For positions with Remote-US locations, the actual salary range for the position may differ based on location but will be commensurate with job duties and relevant work experience.</div><div></div><div>
About Red Hat</div><div><div>
Red Hat</div> is the world&rsquo;s leading provider of enterprise<div> open source</div> software solutions, using a community-powered approach to deliver high-performing Linux, cloud, container, and Kubernetes technologies. Spread across 40+ countries, our associates work flexibly across work environments, from in-office, to office-flex, to fully remote, depending on the requirements of their role. Red Hatters are encouraged to bring their best ideas, no matter their title or tenure. We're a leader in open source because of our open and inclusive environment. We hire creative, passionate people ready to contribute their ideas, help solve complex problems, and make an impact.</div><div></div><div>
Benefits<br>
<ul><li>Comprehensive medical, dental, and vision coverage</li><li>Flexible Spending Account - healthcare and dependent care</li><li>Health Savings Account - high deductible medical plan</li><li>Retirement 401(k) with employer match</li><li>Paid time off and holidays</li><li>Paid parental leave plans for all new parents</li><li>Leave benefits including disability, paid family medical leave, and paid military leave</li><li>Additional benefits including employee stock purchase plan, family planning reimbursement, tuition reimbursement, transportation expense account, employee assistance program, and more!</li></ul></div><div></div><div>
Note: These benefits are only applicable to full time, permanent associates at Red Hat located in the United States.</div><div></div><div>
Inclusion at Red Hat<br>
Red Hat&rsquo;s culture is built on the open source principles of transparency, collaboration, and inclusion, where the best ideas can come from anywhere and anyone. When this is realized, it empowers people from different backgrounds, perspectives, and experiences to come together to share ideas, challenge the status quo, and drive innovation. Our aspiration is that everyone experiences this culture with equal opportunity and access, and that all voices are not only heard but also celebrated. We hope you will join our celebration, and we welcome and encourage applicants from all the beautiful dimensions that compose our global village.</div><div></div><div>
Equal Opportunity Policy (EEO)<br>
Red Hat is proud to be an equal opportunity workplace and an affirmative action employer. We review applications for employment without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, citizenship, age, veteran status, genetic information, physical or mental disability, medical condition, marital status, or any other basis prohibited by law.</div><div><br>
Red Hat does not seek or accept unsolicited resumes or CVs from recruitment agencies. We are not responsible for, and will not pay, any fees, commissions, or any other payment related to unsolicited resumes or CVs except as required in a written contract between Red Hat and the recruitment agency or party requesting payment of a fee.</div><div></div><div>
Red Hat supports individuals with disabilities and provides reasonable accommodations to job applicants. If you need assistance completing our online job application, email <div>application-assistance@redhat.com</div>. General inquiries, such as those regarding the status of a job application, will not receive a reply.</div></div>

Senior Machine Learning Engineer, Distributed vLLM (llm-d)

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