senior data engineer

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manager platform engineering

ml-ai engineering intern

principal machine learning engineer

Wiser Solutions

<div><b>Company Description</b><p><br>
Wiser Solutions is a suite of in-store and eCommerce intelligence and execution tools. We're on a mission to enable brands, retailers, and retail channel partners to gather intelligence and automate actions to optimize pricing, marketing, and operations initiatives, both in-store and online. Our Commerce Execution Suite is available globally.</p><b><br>
Job Description</b><p><b><br>
LOCATION</b>: This position can be based anywhere in Canada, with a preference for someone in the eastern or central time zone who can work with our teams in the US, Europe and India.</p><p><b><i>
ABOUT THE ROLE</i></b></p><p>
Wiser Solutions is seeking a Principal Machine Learning Engineer to shape and execute our AI and data science strategy. This is a senior technical leadership role for someone who combines deep expertise in machine learning, data science, and production engineering with the business acumen to translate complex capabilities into customer value.</p><p>
You will be the technical authority for AI at Wiser: defining architectural direction, representing our capabilities to customers and partners, and delivering production systems that drive measurable business outcomes. This role requires someone who operates fluidly between strategic planning and hands-on implementation, who can present to executives and debug production pipelines in the same week.</p><p>
We're building an AI-native engineering culture at Wiser, one where AI tools and techniques are woven into how we work, not just what we build. We need a Principal AI Engineer who doesn't just deliver AI products but models AI-augmented ways of working and helps the broader engineering organization adopt them. If you believe AI is fundamentally transforming software development and you're already living that transformation daily, we want to talk.</p><p><b><i>
What You Will Do</i></b></p><p><b>
Strategic Leadership</b></p><ul><li>
Define and evolve Wiser's AI and data science technical strategy in partnership with product and business leadership</li><li>
Represent Wiser's AI capabilities to customers, partners, and advisors&mdash;articulating our approach, roadmap, and differentiation</li><li>
Identify high-impact opportunities where AI can solve customer problems or create competitive advantage</li><li>
Establish technical standards, patterns, and best practices that influence engineering decisions across the organization</li></ul><p><b>
Technical Execution</b></p><ul><li>
Architect and build production AI systems including LLM applications, RAG pipelines, semantic search, and traditional ML models</li><li>
Design rigorous evaluation frameworks, experimentation methodologies, and monitoring systems that ensure AI solutions deliver reliable, measurable results</li><li>
Bridge classical data science approaches (statistical modeling, experimentation design, feature engineering) with modern generative AI techniques</li><li>
Own technical quality for AI systems end-to-end: from data pipelines through model deployment to production observability</li></ul><p><b>
Cross-Functional Impact</b></p><ul><li>
Partner with product management to translate business requirements into technical approaches and validate solutions against customer needs</li><li>
Mentor and elevate the AI/data science team (3-5 engineers), raising the technical bar through code review, architecture guidance, and knowledge transfer</li><li>
Collaborate across engineering teams to integrate AI capabilities into Wiser's broader platform architecture</li><li>
Drive build-vs-buy decisions and vendor evaluations for AI infrastructure and tooling</li><li>
Champion AI-native development practices across Wiser engineering&mdash;demonstrating how AI tools accelerate development, improve code quality, and change what's possible</li><li>
Help build an engineering culture where AI augmentation is the default, not the exception</li></ul><br>
<b>
Qualifications</b><br>
<ul><li>
15+ years of experience in data science, machine learning, or ML engineering, with demonstrated progression into technical leadership</li><li>
Deep expertise in statistical methods, experimental design, and classical ML (not just LLM integration)</li><li>
Proven ability to architect and deliver production ML/AI systems at scale on cloud platforms (AWS strongly preferred)</li><li>
Strong software engineering fundamentals: you write production-quality code, not just notebooks</li><li>
Track record of organization-wide technical influence&mdash;setting standards, driving architectural decisions, mentoring engineers</li><li>
Experience communicating technical strategy and capabilities to non-technical stakeholders, including customers and executives</li><li>
Demonstrated ability to operate autonomously, identify high-impact problems, and drive initiatives without close direction</li><li>
Active, daily use of AI coding assistants (Cursor, Claude Code, GitHub Copilot) and a demonstrated belief that AI fundamentally changes how software gets built</li></ul><p><b>
Technical Depth Expected:</b></p><ul><li>
NLP and text analytics: embeddings, semantic similarity, classification, entity extraction, and information retrieval</li><li>
LLM integration patterns: prompt engineering, RAG architectures, agent frameworks, evaluation methods</li><li>
Data engineering: pipeline design, data quality, feature stores, and working with large-scale datasets</li><li>
MLOps: model deployment, monitoring, A/B testing infrastructure, and production observability</li><li>
Python ecosystem mastery; SQL fluency; comfort with distributed systems concepts</li></ul><p><b>
Preferred:</b></p><ul><li>
Experience with transformer architectures, fine-tuning, or training custom models</li><li>
Background in retail, e-commerce, or product data domains</li><li>
Familiarity with LLM orchestration frameworks (LangChain, LlamaIndex) and vector databases</li><li>
Track record of introducing AI-augmented workflows to teams or organizations</li><li>
Opinions (and evidence) on how AI changes engineering practices, team structures, or development processes</li></ul><p><b><i>
What Success Looks Like</i></b></p><p><b>
First 90 Days:</b> Deep understanding of Wiser's data assets, current AI capabilities, and customer use cases. Identified 2-3 high-impact technical opportunities. Established credibility with the team through hands-on contribution and technical guidance.</p><p><b>
First 6 Months:</b> Delivered at least one significant AI capability to production with measurable customer impact. Documented and socialized architectural standards for AI development. Shaped our AI roadmap based on customer needs and technical opportunity.</p><p><b><i>
Supervisory Responsibility</i></b>: None</p><b><br>
Additional Information</b><p><b><br>
Other Duties</b></p><p>
This position may require being on call rotation to address critical production application issues outside of normal working hours.</p><p>
Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities that are required of the employee for this job. Duties, responsibilities, and activities may change at any time with or without notice.</p><p><b>
EEO STATEMENT</b> - Wiser Solutions, Inc. is an Equal Opportunity Employer and prohibits Discrimination, Harassment, and Retaliation of any kind. Wiser Solutions, Inc. is committed to the principle of equal employment opportunity for all employees and applicants, providing a work environment free of discrimination, harassment, and retaliation. All employment decisions at Wiser Solutions, Inc. are based on business needs, job requirements, and individual qualifications, without regard to race, color, religion, sex, national origin, family or parental status, disability, genetics, age, sexual orientation, veteran status, or any other status protected by the state, federal, or local law. Wiser Solutions, Inc. will not tolerate discrimination, harassment, or retaliation based on any of these characteristics.</p><p>
Base pay is one part of our total compensation package. Pay is established on an individual basis after considering multiple factors such as relevant experience, education, and other qualifications. In addition, we take into account geographical differentials and make sure pay is equitable with our current staff. For this position, our hiring range for base annual pay is estimated to be USD$155,000 to $170,000<b>,</b> at the time of this posting.</p><p>
Performance-based discretionary bonuses and variable pay plans are available for some positions.</p><p>
If you require accommodation to complete any part of the application process or need an alternative manner to apply, please contact us at USTalentAcquisition@wiser.com or call (855) 469-4737. &quot;</p><p>
#LI-Remote</p></div>

Principal Machine Learning 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

Canterr, Inc.

<div>
 <p>
  Position: Senior Data Scientist
 </p>
 <p>
  Location: Boston, MA
 </p>
 <p>
  Job Type: Full Time
 </p>
 <p>
  <br/>
 </p>
 <p>
  Responsibilities:
 </p>
 <p>
  Use data science and machine learning principles to develop effective predictive models
 </p>
 <p>
  Write software to prepare, clean, and sample data for use in developing predictive models
 </p>
 <p>
  Use cloud resources (e.g., Amazon Web Services) to prepare and process data
 </p>
 <p>
  Query and extract data from databases (Snowflake)
 </p>
 <p>
  Use data analysis and visualization tools (examples include SQL, Python, Jupyter Notebooks, and Looker) to inform the business strategy
 </p>
 <p>
  Relentlessly iterate solutions within a fast-paced environment where ambiguity is the norm
 </p>
 <p>
  Solve challenging, uncharted problems
 </p>
 <p>
  Work in an environment that thrives on teamwork and continuous learning opportunities
 </p>
 <p>
  <br/>
 </p>
 <p>
  Qualifications:
 </p>
 <p>
  Bachelor&rsquo;s degree required; degree in applied math, computer science, natural sciences or engineering preferred
 </p>
 <p>
  M.S. or Ph.D. in a related field highly desired
 </p>
 <p>
  5+ years of experience with machine learning, statistical modeling, and optimization techniques
 </p>
 <p>
  Fluent in Python (pandas, numpy, SciPy, and scikit-learn preferred)
 </p>
 <p>
  Proficient in linear algebra and statistics
 </p>
 <p>
  Familiar with scientific software principals, e.g. versioning systems, reproducibility
 </p>
 <p>
  Experience in the manufacturing industry is desired
 </p>
 <p>
  <br/>
 </p>
 <p>
  If interested share your updated resume to karun.mabbu@canterr.in
 </p>
</div>


Senior Data Scientist

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

Boston Children's Hospital

<div><div><div><div>84026BR</div>
</div><div><div>Pediatrics-GI/Nutrition
</div></div><div><p><b>Job Posting Description</b></p>
<div><b>Position Summary:</b><br>
The Cell Discovery Network (CDN) at Boston Children&rsquo;s Hospital is seeking a Research Data Scientist to lead the development of a framework for modeling and reprogramming rare disease-relevant cell types. This new initiative, inspired by recent progress in human organoid systems, iPSC-based modeling, and single-cell technologies, aims to unlock therapeutic strategies for neglected pediatric diseases by computationally guiding the discovery and modeling of disease-critical cell states.<br>
<br>
This position offers the opportunity to integrate cutting-edge computational methods (e.g., generative models, foundation models, optimal transport) with a deep understanding of human developmental biology and stem cell differentiation.<br>
<br>
<b>Key Responsibilities:</b><ul><li>
Curate and analyze public and in-house datasets (including organoid and iPSC-derived models).</li><li>
Identify rare or disease-associated cell types from large-scale single-cell and organoid datasets.</li><li>
Determine where similar phenotypes have been observed across developmental systems or model organisms.</li><li>
Build and apply machine learning and AI models (e.g., generative or trajectory-based models, foundation models) to identify and compare rare cell states.</li><li>
Model cellular trajectories and propose reprogramming strategies to reach desired cell phenotypes from accessible starting points (e.g., iPSCs).</li><li>
Prioritize therapeutic strategies for disease modeling and compound screening.</li></ul><br>
<b>
Minimum Qualifications</b><br>
<b>Education:</b><ul><li>
Master's Degree in biology, bioinformatics, computer science or related study is required.</li></ul><b>
Experience</b>:<ul><li>
2 years of experience in computational biology, bioinformatics, machine learning, systems biology, or a related field is required.</li><li>
Minimum of two years of position-specific experience might potentially include experience acquired through MSc or Ph.D. studies.</li></ul></div>
</div><div><p><b>Status</b></p>
<div>Full-Time</div>
</div><div><p><b>Regular, Temporary, Per Diem</b></p>
<div>Regular
</div></div><div><p><b>Standard Hours per Week</b></p>
<div>40
</div></div><div><p><b>Pay Range</b></p>
<div>$83241.60-$133192.80 Annual</div>
</div><div><p><b>Office/Site Location</b></p>
<div>Boston</div>
</div><div><p><b>Job Posting Category</b></p>
<div>Research</div>
</div><div><p><b>Remote Eligibility
</b></p><div>Part Remote/Hybrid</div></div></div></div><div></div>

Senior Research Data Scientist

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)

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

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