ai decision science manager - life sciences

data engineer

software engineer

data engineer ii

senior data engineer

.net full stack engineer

ml engineer

CloudZero

<div><h2 class="jobSectionHeader"><b>About the Role:</b></h2><p>
The ML problems that define the future of cloud cost-per-anything CloudZero is the cost-per-anything model for cloud and Al - for humans and the agents spend</p><p>
they deploy. We're inverting cost intelligence: from billing-first to telemetry-first. Every</p><p>
CloudZero is inverting the traditional cost intelligence model. Engineering decision is a buying decision - Instead of starting from the monthly bill, we're building toward</p><p>
and we're building the platform that proves it in a telemetry-first platform &mdash; lightweight collection agents real time.inside customer environments, capturing every Al inference</p><p>
event, cloud resource usage, and product telemetry signal in Telemetry-FirstCost-to-Produce Al Inference Agentic Governance ML-Powered real time. That data is reconciled against billing to produce total cost-to-produce intelligence. Not just COGS. The full picture.</p><p>
Al is making every company look like a multi-tenant SaaS. Every enterprise now has per-model, per-token, per-customer Al inference complexity &mdash; and no one has a</p><p>
real-time answer for how to measure, govern, and optimize it. CloudZero is building that answer: a multi-tier architecture spanning real-time streaming (Kafka, Flink/KStreams), batch billing reconciliation, and an intelligent governance layer for both human engineers and the autonomous agents they deploy. Most of what makes this role extraordinary is what</p><p>
we're building next. This is a founding technical engineer role. You won't be managing a team on day one &mdash; you'll be anchoring one. You'll set the technical patterns, solve the hardest data science problems in the product, and help build the team around you. The vision: CloudZero becomes the cost-per-anything model for cloud and Al &mdash; for humans and</p><p>
the agents they deploy.</p><p></p><p>
6 hard ML problems. They sit at the intersection of financial telemetry, cloud infrastructure, Al inference, and massive scale. Some are live in product today; several are what we're building next.</p><p></p><ul><li><p><b>
Real-time Unit Economics</b>: Calculate per-unit costs across millions of transactions with dynamic efficiency management</p></li><li><p><b>
Predictive Cost Intelligence</b>: Predict and prevent cost efficiency breaches before they impact business</p></li><li><p><b>
Multi-Cloud Attribution</b>: Accurately attribute cloud spend across complex systems using probabilistic modeling</p></li><li><p><b>
Autonomous Optimization</b>: Build AI agents that make safe infrastructure changes within business constraints</p></li></ul><h2 class="jobSectionHeader"><b>
Responsibilities:</b></h2><ul><li><p>
Lead by example: spend 60-70% of your time building, architecting, and solving technical problems</p></li><li><p>
Prototype novel ML/AI research ideas, and help translate them into production-ready systems that handle enterprise scale</p></li><li><p>
Build AI-powered features (in partnership with product/engineering teams) for cost optimization, anomaly detection, and predictive analytics</p></li><li><p>
Establish technical standards and development processes for AI/ML systems</p></li><li><p>
Build and develop a small team of AI/ML specialists</p></li><li><p>
Provide hands-on coaching and technical guidance to team members</p></li><li><p>
Foster a culture of innovation, continuous learning, and customer focus</p></li><li><p>
Lead by example in technical decision-making and problem-solving approach</p></li><li><p>
Partner closely with engineering teams to embed AI throughout the platform</p></li><li><p>
Translate complex AI concepts into business value for executives and customers</p></li><li><p>
Drive AI strategy alignment with company vision and product roadmap</p></li><li><p>
Represent CloudZero's AI capabilities in customer conversations and industry events</p></li></ul><h2 class="jobSectionHeader"><b>
Qualifications:</b></h2><ul><li><p>
6+ years in ML engineering and/or data science disciplines, with meaningful time in production systems at scale</p></li><li><p>
Deep time-series fluency &mdash; you've built forecasting and anomaly detection systems that made it to production and earned customer trust</p></li><li><p>
Classical ML foundations &mdash; graphs, clustering, probabilistic modeling, data structures. You reach for the right tool, not the trendiest one</p></li><li><p>
Production ML engineering &mdash; you've owned the full stack: feature engineering, model serving, monitoring, retraining pipelines, feedback loops</p></li><li><p>
Python fluency and data warehouse experience (Snowflake, BigQuery, or equivalent)</p></li><li><p>
Formal background &mdash; in Computer Science, Statistics, Mathematics, or a related quantitative field</p></li><li><p>
GenAI/LLM experience &mdash; you've integrated LLMs, seen their failure modes, and know when to use them vs.traditional ML</p></li><li><p>
Cloud ML infrastructure &mdash; AWS SageMaker, Bedrock, or equivalent. Building systems at enterprise scale in AWS/GCP</p></li><li><p>
FinOps or cost intelligence domain nice to have - understanding of cloud billing, infrastructure cost models, or related financial data</p></li><li><p>
Founding IC experience &mdash; you've been the first or second data scientist and know what it takes to build from scratch</p></li><li><p>
Graph modeling and semantic layers &mdash; knowledge graphs, entity resolution, or semantic modeling in production contexts</p></li><li><p>
Bias toward correctness &mdash; you care whether models are actually right, not just accurate on a validation set</p></li></ul><p></p><h2 class="jobSectionHeader"><b>
About CloudZero:</b></h2><p>
Cloud cost management is one of the biggest challenges organizations face today. As cloud adoption continues to accelerate, so do the complexities and costs associated with it &mdash; and macroeconomic conditions only increase pressure to prove cloud efficiency.</p><p></p><p>
CloudZero is a SaaS platform at the intersection of next-generation cloud cost management and FinOps. We ingest billing and usage data from all cloud, SaaS, and PaaS providers, organize it in real time according to our customers' business structures, and empower organizations to make more informed business decisions.</p><p></p><p>
Since our founding in 2016, our mission has been to make efficient innovation a reality for every cloud-driven organization. We believe every engineering decision is a buying decision, and we're applying proven reliability engineering principles to financial efficiency.</p><p></p><p>
We believe the best AI empowers users with clear insights and confident decisions, transforming complex cloud cost data into actionable intelligence that drives meaningful business outcomes.</p><p></p><p>
To date, we've raised over $56 million from leading venture capital firms. We're solving problems of massive scale, business importance, and complexity in a space that needs it more than ever.</p><p></p><h3 class="jobSectionHeader"><b>
Equal Opportunity Employer</b></h3><p>
CloudZero is an equal opportunity employer and values diversity. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status or disability status. All job offers are contingent upon the candidate passing background and reference checks.</p><p><b>
Please note</b>: <i>CloudZero is unable to sponsor employment visas. Candidates must have permanent authorization to work in the United States without the need for current or future sponsorship.</i></p><p>
Compensation Range: $220K - $250K</p></div>

Lead 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

Motional

<div><p><b>Mission Summary:</b><br>
<br>
We are looking for a Machine Learning Systems Engineer to join our ML Acceleration team. In this role, you will be responsible for the core systems that enable our researchers to train frontier models at scale, focusing obsessively on speed, cost, reliability, and throughput. You will work at the intersection of machine learning research and high-performance systems engineering. Your work will directly impact our ability to scale large-scale distributed model training and reduce the time-to-convergence for our next generation of models.</p><br>
<p><b>
What you'll be doing:</b></p>
<ul>
<li><b>Performance Profiling &amp; Optimization</b>: Utilize profiling tools (e.g., Nsight, PyTorch Profiler) to identify bottlenecks in data loading, gradient computation, and communication. Implement optimizations like kernel fusion, sharding, and tiling to improve step time.</li>
<li><b>Distributed Training</b>: Optimize distributed training pipelines using frameworks such as PyTorch Distributed.</li>
<li><b>Kernel Development</b>: Design and maintain high-performance GPU kernels in Triton or CUDA for state-of-the-art ML workloads.</li>
<li><b>Data Pipeline Engineering</b>: Optimize robust data loading pipelines that maximize training throughput.</li><br>
</ul>
<p><b>What we're looking for</b>:</p>
<ul>
<li><b>Education</b>: Bachelor's, Master's degree, or PhD in Computer Science, Computer Engineering, or a related technical discipline.</li>
<li><b>Software Engineering</b>: Strong proficiency in Python.</li>
<li><b>ML Frameworks</b>: Extensive hands-on experience with PyTorch.</li>
<li><b>ML Knowledge</b>: Experience optimizing machine learning model execution during training and inference, alongside a strong understanding of fundamental machine learning concepts, architectures, and processes.</li>
<li><b>Problem Solving</b>: Exceptional analytical and problem-solving skills, with a bias for action and a data-driven approach to technical challenges.</li><br>
</ul>
<p><b>We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.</b></p><p></p><div><p><b><br>
Motional is a driverless technology company making autonomous vehicles a safe, reliable, and accessible reality. We're driven by something more.</b></p>
<p><b>
Our journey is always people first.</b></p>
<p>
We aren't just developing driverless cars; we're creating safer roadways, more equitable transportation options, and making our communities better places to live, work, and connect. Our team is made up of engineers, researchers, innovators, dreamers and doers, who are creating a technology with the potential to transform the way we move.</p>
<p><b>
Higher purpose, greater impact.</b></p>
<p>
We're creating first-of-its-kind technology that will transform transportation. To do so successfully, we must design for everyone in our cities and on our roads. We believe in building a great place to work through a progressive, global culture that is diverse, inclusive, and ensures people feel valued at every level of the organization. Diversity helps us to see the world differently; it's not only good for our business, it's the right thing to do.
</p><p><b>Scale up, not starting up.</b></p>
<p>
Our team is behind some of the industry's largest leaps forward, including the first fully-autonomous cross-country drive in the U.S, the launch of the world's first robotaxi pilot, and operation of the world's longest-standing public robotaxi fleet. We're driven to scale; we're moving towards commercialization of our technology, and we need team members who are ready to embrace change and challenges.</p>
<p>
Formed as a joint venture between Hyundai Motor Group and Aptiv, Motional is fundamentally changing how people move through their lives. Headquartered in Boston, Motional has operations in the U.S and Asia. For more information, visit www.Motional.com and follow us on Twitter, LinkedIn, Instagram and YouTube.</p>
<p><i>
Motional AD Inc. is an EOE. We celebrate diversity and are committed to creating an inclusive environment for all employees. To comply with Federal Law, we participate in E-Verify. All newly-hired employees are queried through this electronic system established by the DHS and the SSA to verify their identity and employment eligibility.</i></p></div></div>

ML Systems Engineer, ML Acceleration

Realign

<div>Boston, Massachusetts 02108 Posted March 12th, 2026
 <div></div><p>Looking for more job opportunities? Click here!</p></div>
<br><div><p>Job Type: Full Time</p>
<p>Job Category: IT</p>
<p>Job Description</p>
<p></p><div><b>Role - Agentic AI Engineer</b></div><div><b>
Location - Boston, MA (ONSITE)</b></div><div><b>
Experience &ndash; 5+</b></div><div><b>
Type - Full Time</b></div><div><b><br>
Job Description</b></div><div><b>
Must Have Technical/Functional Skills</b></div><ul><p>
Hands-on with agent frameworks such as Semantic Kernel, LangGraph, LangChain Agents or CrewAI</p><p>
Proficiency in Python and modern AI/ML frameworks, AWS</p><p>
Hands‑on with Bedrock AgentCore Memory, Gateway, and Runtime</p><p>
Strong experience with LLMs (fine-tuning, evaluation, prompt engineering)</p></ul><div></div><div><b><br>
Roles &amp; Responsibilities</b></div><ul><p>
Focus on designing, building, and optimizing AGENTC capabilities using large language models and advanced reasoning frameworks.</p></ul><div></div><div><b><br>
Key Responsibilities</b></div><ul><p>
Strong technical depth, a passion for building, workstreams in a fast-paced, innovation-driven environment</p><p>
Build serverless agentic product architectures and features on AWS using Bedrock AgentCore Memory, Gateway, Identity, Interpreter, Observability and Runtime</p><p>
Implement and operate A2A/MCP servers on AWS; integrate with Bedrock Agents/Converse APIs</p><p>
Orchestrate multi‑agent plans with Strands Agents; deploy to EKS or Lambda</p><p>
Instrument agents with CloudWatch metrics, spans, and traces; enable audits</p><p>
Collaborate with product managers, data engineers, and UX teams to deliver production-ready solutions</p><p>
Follow the roadmap for AI initiatives, ensuring they align with organizational goals and show measurable impact</p></ul><div></div><div><b><br>
Required Skills &amp; Experience</b></div><ul><p>
Strong problem-solving and strategic thinking abilities</p><p>
Strong collaboration skills in research-driven environments</p><p>
Hands‑on with Bedrock AgentCore Memory, Gateway, and Runtime</p><p>
Strong experience with LLMs (fine-tuning, evaluation, prompt engineering)</p><p>
Proficiency in Python and modern AI/ML frameworks</p><p>
Proficiency in vector databases and embedding models for retrieval tasks</p><p>
Experience with Data Connectors and API gateways that support seamless communication between systems</p><p>
Experience with generative AI concepts such as Retrieval-Augmented Generation (RAG), agentic workflows, training LLMs with structured and unstructured data sets</p><p>
Strong communicator with the ability to present ideas clearly and influence stakeholders - with a passion for enabling data-driven transformation</p><p>
Hands-on with agent frameworks such as Semantic Kernel, LangGraph, LangChain Agents or CrewAI</p><p>
Deep subject matter expertise in AI technologies, including but not limited to Copilot Studio, OpenAI, Semantic Kernels, Azure AI Foundry, Google Gemini, Microsoft 365, and M365 Copilot or Anthropic or AWS platforms</p></ul><br>
<p></p>
<p>Required Skills</p>
<p>DEVOPS ENGINEER</p> <p>SENIOR EMAIL SECURITY ENGINEER</p></div>

Agentic AI Engineer-7

Lendbuzz

<div><div>At Lendbuzz, we believe financial opportunity should be more personalized and fair. We develop innovative technologies that provide underserved and overlooked borrowers with better access to credit. From our employees to our dealers, partners, and borrowers, we&rsquo;ve built a company and a culture around a resolute belief in the promise and power of diversity. We value independent and critical thinking.<br>
</div><div></div><br>
<div>We are seeking a skilled and motivated <b>Software Engineer focused on Large Language Model (LLM) applications</b> to join our Machine Learning team. In this role, you will help design, build, and optimize next-generation conversational agent technologies. You will collaborate closely with ML researchers and product teams to ship high-impact features and own key components of our conversational AI stack. This position reports to the ML Research Scientist.</div><div></div><div><b><br>
This is a hybrid position based in Boston, MA and requires 3 days onsite.</b></div>
<h3 class="jobSectionHeader"><b>Key Responsibilities:</b></h3><ul><li>
Software engineering for LLM-powered conversational agents, with an emphasis on practical implementation, reliability, and user experience</li><li>
Evaluate, fine-tune, and deploy LLM-based models and pipelines using REST APIs and internal microservices</li><li>
Implement prompt engineering, retrieval-augmented generation (RAG), tool-use pipelines, and conversation orchestration logic</li><li>
Investigate and integrate emerging technologies, particularly in real-time voice, streaming, and multi-modal interaction</li><li>
Analyze model outputs, user interactions, and system performance to drive iterative improvements</li><li>
Build and maintain high-quality datasets, including data cleaning, preprocessing, labeling workflows, and benchmarking for NLP tasks</li><li>
Own data quality, ensuring accuracy, reproducibility, and reliability across the data lifecycle</li><li>
Collaborate with ML, backend, and product teams on deployment best practices, monitoring, and scalability of LLM-based services</li><li>
Contribute to internal documentation, experimentation processes, and model evaluation frameworks</li></ul><div></div><h3 class="jobSectionHeader"><b><br>
Key Requirements:</b></h3><ul><li>
Master&rsquo;s degree in Artificial Intelligence, Computer Science, or a related technical field</li><li>
Strong programming skills in Python, with experience in ML and data tooling (e.g., PyTorch, Pandas, NumPy, Scikit-learn)</li><li>
Preferred: 2+ years of professional software engineering experience, including scripting, data processing, or backend/ML pipelines</li><li>
Experience with NLP techniques, LLMs, or machine learning fundamentals</li><li>
Strong problem-solving ability and comfort working independently in a fast-moving environment</li><li>
Preferred: Experience deploying applications or models on cloud platforms, preferably AWS</li><li>
Bonus (not required): experience with real-time systems, WebSockets/streaming, RAG pipelines, vector databases, or ML evaluation frameworks, Genesys/Twilio</li></ul><div>
We believe:</div><div></div><div><b><br>
Diversity</b> <b>is a competitive advantage.</b> We celebrate our differences, and are better when we have a variety of experiences, viewpoints, and backgrounds.</div><div></div><div><b><br>
Compassion</b> <b>is a strength.</b> We care about our customers and look to build long-term relationships with them.</div><div></div><div><b><br>
Simplicity</b> <b>is a key feature.</b> We work hard to make our forms and processes as painless and intuitive as possible.</div><div></div><div><b><br>
Honesty and transparency are non negotiable.</b> We incorporate these traits in all of our interactions.</div><div></div><div><b><br>
Financial opportunity belongs to everyone. </b>We work every day to improve lives by extending this opportunity.</div><div></div><div><br>
If you believe these things too then we would love to hear from you!</div><div></div><div><b><i><br>
A Note on Recruiting Outreach</i></b></div><div><i>
We&rsquo;ve been made aware of individuals falsely claiming to represent Lendbuzz using lookalike email addresses (eg @</i><i>lendbuzzcareers.com</i><i>). Please note that all legitimate emails from our team come from @</i><i>lendbuzz.com</i><i>. We will never ask for sensitive information or conduct interviews via messaging apps.</i></div></div>

Software Engineer (Machine Learning - LLMs)

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

Suffolk Construction

<div>Overview:
<p><b>
About Suffolk</b></p><div><div>
Suffolk is a national enterprise that builds, innovates, and invests. We provide value across the entire project lifecycle through our core construction management services and complementary business lines in real estate investment, design, self-perform construction, and technology start-up investment (Suffolk Technologies). By integrating data, artificial intelligence, and advanced technology through our Seamless Platform, we connect design, construction, and operations to deliver smarter, more predictable results and redefine how America builds.</div></div><div></div><div><div><br>
Suffolk &ndash; America&rsquo;s Contractor &ndash; is a national company with more than $9 billion in annual revenue, 3,000 employees, and 17 offices, including Boston (headquarters), New York City, Miami, West Palm Beach, Tampa, Estero, Dallas, Los Angeles, San Francisco, San Diego, Las Vegas, Herndon, U.S. Virgin Islands, and other key markets. Suffolk manages some of the most complex and transformative projects in the country, serving clients across healthcare, life sciences, education, gaming, aviation, transportation, government, mission critical, and commercial sectors. Suffolk is privately held and is led by founder, chairman and CEO John Fish. Suffolk is ranked #8 on ENR&rsquo;s list of &ldquo;Top CM-at-Risk Contractors.&rdquo; For more information, visit www.suffolk.com and follow Suffolk on Facebook, Twitter, LinkedIn, YouTube, and Instagram.</div></div><div></div><div><div><br>
At Suffolk, we believe that our total rewards program should offer you and your family the support you need when it matters most. That&rsquo;s why we have created a program that provides employees with access to a wide variety of options that can be personalized to support you and your loved ones physically, emotionally, and financially.</div></div><div></div><div><div><br>
Benefits include, competitive salaries, auto allowances and gas cards for certain roles, access to market leading medical and emotional and mental health benefits, dental, and vision insurance plans, virtual care options for physical therapy and primary care, generous paid time off, 401k plan with employer match and access to expert financial resources, company paid and voluntary life insurance, tax deferred savings accounts, 10 backup daycare days each year, short- and long-term disability, commuter benefits and more. For more information, click here.</div></div><p></p><div><b><br>
Role</b></div><div>
Join Suffolk&rsquo;s AI Studio in Boston as a core engineer transforming how AI powers construction management. Partnering with Product Managers, Site AI Engineers and Data Engineers, you&rsquo;ll solve pain points, redesign workflows, and deploy AI agents that cut down reporting, accelerate RFIs, simplify lookahead planning, progress updates, materials tracking, and more. You&rsquo;ll focus on building secure, scalable, and high-performance AI agents using modern technologies including AWS Bedrock, and Databricks &mdash; shaping the backbone of Suffolk&rsquo;s &ldquo;Construction Site of the Future.&rdquo;</div><div></div><div><br>
The AI Engineer (AI Studio) builds the foundation that enables jobsite AI to scale. You&rsquo;ll focus on technical excellence, platform reliability, and scalable agent frameworks, enabling field teams to transform how construction projects are executed.</div>
Responsibilities:
<div><b>
AI Product Engineering &amp; Deployment</b></div><ul><li>
Translate product requirements and user stories into production-grade AI solutions using AWS Bedrock, Lambda, ECS/EKS, and Databricks.</li><li>
Implement RAG pipelines with Delta tables, Unity Catalog, and Vector Search.</li><li>
Design and deploy multi-model agents that dynamically select between LLMs (Claude, GPT, Llama, Titan, etc.) based on task context, cost, and latency.</li><li>
Implement multi-agent orchestration frameworks enabling collaboration among specialized agents (e.g., data retriever, planner, summarizer, and action executor) for complex construction workflows.</li><li>
Own full lifecycle delivery &mdash; design, development, testing, deployment, monitoring, and maintenance.</li></ul><div><b>
Full-Stack &amp; Backend Development</b></div><ul><li>
Build APIs, backend services, and agentic workflows using Python, FastAPI, LangChain, and AWS SDKs.</li><li>
Create reusable connectors and orchestration layers for multi-model agents (Claude, GPT, Llama, etc.).</li><li>
Develop front-end integrations for Teams and web SPAs via REST or GraphQL endpoints.</li></ul><div><b>
Data Engineering &amp; Integration</b></div><ul><li>
Partner with Data Engineering to design robust ETL/ELT pipelines from enterprise systems to the Databricks Lakehouse.</li><li>
Ensure efficient data access, caching, and vectorization for low-latency AI response.</li><li>
Build tools to monitor and improve data quality, latency, and observability.</li></ul><div><b>
DevOps &amp; Platform Automation</b></div><ul><li>
Use Terraform, AWS CDK, and GitHub Actions to automate infrastructure and deployments.</li><li>
Implement LLMOps: cost monitoring, latency optimization, usage analytics, and model versioning.</li><li>
Enforce security, governance, and access standards in line with enterprise policies.</li></ul><div><b>
Collaboration &amp; Communication</b></div><ul><li>
Work closely with product managers, site AI engineers, and data scientists to iterate rapidly in Agile sprints.</li><li>
Communicate technical progress clearly to non-technical stakeholders; contribute to internal AI playbooks and templates.</li></ul><br>
<div></div>
Qualifications:
<ul><li>4-6 years of professional software development experience on AWS, with 2+ years focused on AI/ML engineering (LLMs, RAG, Bedrock, or similar). Strong coding proficiency in Python (LangChain, FastAPI, boto3) and solid experience with SQL, Databricks, and vector databases.</li><li>
Experience designing and deploying production systems using AWS Lambda, ECS/EKS, API Gateway, Step Functions, S3, CloudFront, and KMS.</li><li>
Strong foundation in CI/CD, IaC (Terraform/CDK), and GitHub Actions</li><li>
Experience training, retraining and performing transfer learning on ML models desirable.</li><li>
Bachelor&rsquo;s in Computer Science, Engineering, Physics, or a related field; Master&rsquo;s preferred.</li><li>
Prior hands-on work in construction or heavy process industries (manufacturing, oil &amp; gas, chemicals) is a significant plus.</li><li>
Excellent collaboration and communication skills &mdash; able to work cross-functionally but not dependent on business-side facilitation.</li><li>
Integration &amp; ETL skills: Foundational understanding of ETL/ELT design, Airflow or Databricks Workflows, and REST/GraphQL API development; proven collaboration with Data Engineering on source-to-lake and lake-to-agent pipelines.</li></ul><div>
#SUFFOLKHIRING</div>
Working Conditions:
<div>
While performing the duties of this job, the employee is regularly required to sit for long periods of time; talk or hear; perform fine motor, hand and finger skills in the use of a keyboard, telephone, or writing. The employee is frequently required to stands; walk; and reach with arms and/or hands. Specific vision abilities include close vision, distance vision, depth perception and the ability to adjust focus. The employee will spend their time in an office environment with a quiet to moderate noise level. Job site walking.</div>
EEO Statement:
<div>
Suffolk provides equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, sexual orientation, pregnancy or maternity, national origin, citizenship, genetic information, disability, protected veteran, gender identity, age or any other status protected by law. This policy applies to recruiting, hiring, transfers, promotions, terminations, compensation, benefits, and all other terms and conditions of employment. Suffolk will not tolerate any unlawful discrimination toward, or harassment of, applicants or employees by anyone at Suffolk, or anyone working on behalf of Suffolk.</div>
Compensation Information:
<div>
The expected salary range for this position (AI Engineer) in Boston, MA is between $133,000 and $186,000 USD. This represents the typical salary range for this position and is just one component of Suffolk&rsquo;s total compensation package. Actual salaries may be based on several factors including, but not limited to, skill set, experience, education and other qualifications. Suffolk offers a comprehensive benefits package as part of its overall compensation strategy. Salary ranges may differ by geography and are reviewed regularly to reflect market trends.</div></div>

AI Engineer

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