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intelligence engineer

Foundation Robotics Labs Inc.

<div>About Us 
Foundation is developing the future of general purpose robotics with the goal to address the labor shortage. 
Our mission is to create advanced robots that can operate in complex environments, reducing human risk in conflict zones and enhancing efficiency in labor-intensive industries. 
We are on the lookout for extraordinary engineers and scientists to join our team. 
Your previous experience in robotics isn't a prerequisite &mdash; it's your talent and determination that truly count. 
We expect that many of our team members will bring diverse perspectives from various industries and fields. We are looking for individuals with a proven record of exceptional ability and a history of creating things that work. 
Our Culture 
We like to be frank and honest about who we are, so that people can decide for themselves if this is a culture they resonate with. Please read more about our culture here https://foundation.bot/culture.
About This Role 
 
Why You Should Join 
<ul><li>
You are highly technical - regardless of the role you are in. We are building technology; you need to understand technology well.</li><li>
You care about aesthetics and design inside out. If it's not the best product ever, it bothers you, and you need to &ldquo;fix&rdquo; it.</li><li>
You don't need someone to motivate you; you get things done.</li></ul> 
Why are We Hiring for this Role: 
<ul><li>
We are building a general-purpose humanoid that must understand and navigate the physical world &mdash; and that requires a dedicated engineer to architect the internal models that make that possible</li><li>
World models are the cognitive backbone of our robot; without them, the humanoid cannot plan, predict, or adapt to novel environments</li><li>
We are at an inflection point where our hardware is ready &mdash; now we need the intelligence layer to match it</li><li>
The gap between a robot that executes fixed commands and one that truly reasons about its environment is a world model; we are hiring to close that gap</li><li>
As we scale to real-world deployment, our humanoid needs to generalize across unstructured, unpredictable settings &mdash; something only a robust world model can enable</li><li>
This hire will directly shape the core intelligence architecture of our platform before it becomes locked in at scale</li></ul> 
What Kind of person are we looking for 
<ul><li>
Hands-on experience building world models, model-based RL, or predictive world simulators using frameworks like PyTorch or JAX &mdash; you have shipped these systems, not just studied them</li><li>
Strong foundation in deep learning architectures relevant to world modeling: transformers, diffusion models, neural radiance fields (NeRF), and variational recurrent state-space models</li><li>
Proficient in Python as a primary research and development language, with production-level familiarity in C++ for latency-sensitive inference and real-time robotics integration</li><li>
Experience with robotics middleware and simulation environments &mdash; ROS2, Isaac Sim, MuJoCo &mdash; and the ability to close the sim-to-real gap in learned representations</li><li>
Experience with video prediction or future-frame generation models (e.g., RSSM, DreamerV3, UniSim, Genie) is a strong plus</li><li>
Able to read and implement from recent arXiv papers with minimal overhead &mdash; you are comfortable turning a research prototype into a tested, integrated system</li></ul></div>

AI Engineer (World Models)

Sutter Health

We are so glad you are interested in joining Sutter Health!
 
Organization: 
SHSO-Sutter Health System Office-Bay
 
Position Overview: 
****Please Note: While this position is listed as hybrid, regular in-office attendance is required. Candidates should be prepared to commute to the San Francisco office on a consistent basis to support team collaboration and business needs.****
 
Responsible for leading the design, build, and maintain Sutter Health’s artificial intelligence (AI) and machine learning (ML) infrastructure, including end to end pipelines for ML and large language models (LLM) that support analytics, data science, and enterprise AI use cases. This includes how AI models and associated data are ingested, processed, trained, deployed, monitored, governed, and secured across cloud and on premises environments. Ensures that AI systems meet high standards of performance, reliability, and compliance, enabling the organization to safely and effectively integrate AI capabilities into clinical, operational, and strategic workflows.
 
Utilizing modern artificial intelligence operations (AIOps) and machine learning operations (MLOps) practices, leads the operationalization of models, maintenance of scalable AI services, monitoring of system behavior, and automation of deployment workflows. Works with structured and unstructured data, clinical data models, healthcare data standards, and modern cloud platforms. Develops and validates ML models, integrates researcher built or vendor provided algorithms, and contributes to AI platform architecture and tooling. Sets standards for high quality, secure, and well governed AI systems that support advanced analytics, automation, and intelligent applications.
 
Job Description: 
****Please Note: While this position is listed as hybrid, regular in-office attendance is required. Candidates should be prepared to commute to the San Francisco office on a consistent basis to support team collaboration and business needs.****
 
EDUCATION: 
<ul>
<li>Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or related field; or equivalent combination of education and experience.</li>
</ul>
TYPICAL EXPERIENCE: 
<ul>
<li>5 years recent relevant experience</li>
</ul>
PREFERRED EXPERIENCE: 
<ul>
<li>Advanced experience with Azure cloud services, including containerized model hosting, Azure ML, secure environment management, and cloud‑native deployment patterns.</li>
<li>Strong proficiency in MLOps / LLMOps practices, including CI/CD pipelines, automated testing, observability, model versioning, canary/AB rollouts, and drift monitoring.</li>
<li>Hands‑on expertise in building and operating AI/ML/LLM pipelines (ingestion → preprocessing → training → inference → monitoring), using Python, modern container frameworks, and orchestration systems.</li>
<li>Experience integrating researcher‑built or vendor‑provided ML/LLM models into production workflows, including API‑based embedding, performance tuning, and secure data handling.</li>
<li>Familiarity with healthcare data standards and environments, such as clinical data models, unstructured clinical text, and privacy/security expectations in regulated domains.</li>
</ul>
SKILLS AND KNOWLEDGE: 
<ul>
<li>Relevant experience in AI/ML Engineering and LLM/MLOps</li>
<li>Experience building end to end AI/ML pipelines, including training, deployment, monitoring, and retraining loops.</li>
<li>Strong programming skills in Python and familiarity with modern ML/LLM frameworks and libraries.</li>
<li>Hands on experience with Azure, including services such as Fabric, Foundry, Container Apps, AKS, and Application Insights.</li>
<li>Experience implementing AIOps / MLOps / LLM Ops practices, including CI/CD pipelines, automated testing, observability, and versioned deployments.</li>
<li>Experience with GitOps principles using GitHub Actions or similar tools.</li>
<li>Understanding of healthcare data models, including Epic Clarity, FHIR, HL7, and Caboodle.</li>
<li>Ability to evaluate and integrate machine learning models into production systems and support model lifecycle management.</li>
<li>Strong debugging skills across distributed systems, containerized environments, and cloud platforms.</li>
<li>Ability to work in cross functional environments with clinicians, data scientists, architects, and engineering teams.</li>
<li>Ability to produce high quality documentation and communicate complex technical concepts to diverse stakeholders.</li>
<li>Detail oriented, organized, and effective at prioritizing multiple concurrent initiatives.</li>
<li>Familiarity with HIPAA, PHI/PII security requirements, and regulatory considerations for healthcare AI.</li>
</ul>
These Principal Accountabilities, Requirements and Qualifications are not exhaustive but are merely the most descriptive of the current job. Management reserves the right to revise the job description or require that other tasks be performed when the circumstances of the job change (for example, emergencies, staff changes, workload, or technical development).
 
Job Shift: 
Days
 
Schedule: 
Full Time
 
Days of the Week: 
Monday - Friday
 
Weekend Requirements: 
As Needed
 
Benefits: 
Yes
 
Unions: 
No
 
Position Status: 
Exempt
 
Weekly Hours: 
40
 
Employee Status: 
Regular
 Sutter Health is an equal opportunity employerEOE/M/F/Disability/Veterans.
 
Pay Range is $168,958.40 to $270,337.60 / annual salary
 The compensation range may vary based on the geographic location where the position is filled. Total compensation considers multiple factors, including, but not limited to a candidate’s experience, education, skills, licensure, certifications, departmental equity, training, and organizational needs. Base pay is only one component of Sutter Health’s comprehensive total rewards program. Eligible positions also include a comprehensive benefits package.

AI Engineer

OpenAI

<div>
 
 About The Team
 
 
 
 The Integrity team at OpenAI is dedicated to ensuring that our cutting-edge technology is not only revolutionary, but also secure from a myriad of adversarial threats. We strive to maintain the integrity of our platforms as they scale.
 
 
 The Integrity team is at the front lines of defending against misuse in all its forms: content abuse, scaled attacks, and other actions that could undermine the user experience or harm our operational stability.
 
 
 
 About The Role
 
 
 
 As a Machine Learning Engineer in OpenAI's Integrity team, you will have the opportunity to work with some of the brightest minds in AI. You&rsquo;ll work on state-of-the-art models and classifiers, experiment with new architecture and approaches, and push forward our abilities in content and user understanding. You&rsquo;ll help turn research breakthroughs into tangible solutions that improve the trust and safety of our platform. If you're excited about training LLMs and building ML models, this role is your chance to make a significant mark.
 
 
 
 In This Role, You Will
 
 
 
 <ul>
 <li>
 Innovate and Deploy: Design and deploy advanced machine learning models that solve real-world problems. Bring OpenAI's research from concept to implementation, creating AI-driven applications with a direct impact.
 </li>
 <li>
 Collaborate with the Best: Work closely with researchers, software engineers, and product managers to understand complex business challenges and deliver AI-powered solutions. Be part of a dynamic team where ideas flow freely and creativity thrives.
 </li>
 <li>
 Optimize and Scale: Implement scalable data pipelines, optimize models for performance and accuracy, and ensure they are production-ready. Contribute to projects that require cutting-edge technology and innovative approaches.
 </li>
 <li>
 Learn and Lead: Stay ahead of the curve by engaging with the latest developments in machine learning and AI. Take part in code reviews, share knowledge, and lead by example to maintain high-quality engineering practices.
 </li>
 <li>
 Make a Difference: Monitor and maintain deployed models to ensure they continue delivering value. Your work will directly influence how AI benefits individuals, businesses, and society at large.
 
 
 </li>
 </ul>
 
 You Might Thrive In This Role If You
 
 
 
 <ul>
 <li>
 Master's/ PhD degree in Computer Science, Machine Learning, Data Science, or a related field.
 </li>
 <li>
 Demonstrated experience in deep learning and transformers models
 </li>
 <li>
 Experience with content understanding or abuse prevention with LLMs is a plus
 </li>
 <li>
 Proficiency in frameworks like PyTorch or Tensorflow
 </li>
 <li>
 Strong foundation in data structures, algorithms, and software engineering principles.
 </li>
 <li>
 Are familiar with methods of training and fine-tuning large language models, such as distillation, supervised fine-tuning, and policy optimization
 </li>
 <li>
 Excellent problem-solving and analytical skills, with a proactive approach to challenges.
 </li>
 <li>
 Ability to work collaboratively with cross-functional teams.
 </li>
 <li>
 Ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines
 </li>
 <li>
 Enjoy owning the problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done
 
 
 </li>
 </ul>
 
 About OpenAI
 
 
 
 OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
 
 
 We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
 
 
 For additional information, please see OpenAI&rsquo;s Affirmative Action and Equal Employment Opportunity Policy Statement.
 
 
 Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
 
 
 To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance.
 
 
 We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.
 
 
 OpenAI Global Applicant Privacy Policy
 
 
 At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.
 
 
 Compensation Range: $266K - $555K
</div>

Machine Learning Engineer, Integrity

Apple

The people here at Apple don't just create products - they create the kind of wonder that's revolutionized entire industries. It's the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it. 
 
Data Services provides Data Solutions across Apple is looking for a creative and motivated AI Engineer with excellent problem solving and interpersonal skills. We are seeking a hands-on AI Solutions Engineer with deep technical expertise in Artificial Intelligence and Generative AI who can design, prototype, and implement scalable, high-impact AI solutions. This role blends system architecture, rapid prototyping, and cross-functional execution to shape the next generation of intelligent products. We would love to hear from you if you're enthusiastic, highly motivated, and want to make an impact! 
 
Description 
 
As a strategic and technical leader, you will partner closely with engineers, product managers, product designers, data scientists, and business stakeholders to identify high-value AI opportunities, translate them into scalable architectures, and ensure successful deployment into production environments. The ideal candidate is a proactive self-starter who excels in cross-functional collaboration to empower our Sales and Sales Finance organizations. In addition to deep technical expertise, this role demands exceptional communication and presentation skills. You&rsquo;ll need to translate complex AI concepts for senior leadership, cross-functional peers, and non-technical partners- ensuring clear alignment, strong collaboration, and high-impact delivery. It&rsquo;s a unique opportunity for someone who thrives in a fast-paced environment and takes full ownership of their work from idea to execution.&quot;,&quot;responsibilities&quot;:&quot;Design end-to-end AI solutions with clear business impact, from concept to deployment, with a strong focus on feasibility, scalability, and performance. 
 
Build prototypes to evaluate architecture and modeling approaches, working closely with engineering teams to transition concepts into robust systems. 
 
Guide architectural decisions across LLM applications, multimodal systems, and agentic frameworks. 
 
Assess and integrate open-source tools, orchestration frameworks, context management solutions, and vector databases. 
 
Collaborate with product and business teams to define KPIs and ensure alignment of AI initiatives with strategic goals. 
 
Stay on top of emerging trends in Generative AI, prompt engineering, and agent-based architectures. 
 
Define and advocate for best practices in model serving, integration patterns, and runtime efficiency-not just training ops. 
 
Preferred Qualifications 
 
Experience leading zero-to-one initiatives for AI product development 
 
Familiarity with synthetic data generation, fine-tuning techniques, and performance optimization for large models 
 
Understanding of agent-based interaction patterns, multi-agent communication protocols (e.g., MCP, A2A), and AG-UI concepts 
 
Prior work in designing user-facing AI features or tools; familiarity with frontend integration is a plus 
 
Excellent communication skills with the ability to translate technical concepts into business terms 
 
Ph.D. in Computer Science, AI, or related field, or equivalent industry experience with demonstrated impact in real-world applications 
 
Minimum Qualifications 
 
2+ years industry experience with a Master&rsquo;s in Computer Science, AI, or related field, or equivalent industry experience with demonstrated impact in real-world applications 
 
1+ years plus of experience building AI-powered systems with a strong fundamental grasp of architecture and deployment 
 
Experience designing and delivering NLP or LLM-based systems, with knowledge of multi-modal and agentic AI frameworks 
 
Proficiency with Python to use ML libraries (e.g., PyTorch, TensorFlow, Transformers), vector search engines (e.g., FAISS, Weaviate or similar) and vector databases (e.g., Milvus, Vespa, or similar) 
 
Experience developing cloud platforms solutions (e.g., AWS, GCP, or public cloud, etc.) 
 
Hands-on experience with LLM orchestration frameworks (e.g., LangChain, LlamaIndex or similar), containerization (Docker, Kubernetes or similar), and scalable APIs 
 
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant . 
 
Pay &amp; Benefits 
 
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $147,400 and $272,100, and your base pay will depend on your skills, qualifications, experience, and location. 
 
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits. 
 
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

AI Engineer, AI & Data Platforms (AiDP)

Bain & Company

What Makes Us A Great Place To Work
 
We are proud to be consistently recognized as one of the world’s best places to work. We are currently the top ranked consulting firm on [1] Glassdoor’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’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.
 
About Bain AI, Insights & Solutions (AIS)
 
Bain’s [2] AI, Insights & Solutions (AIS) team works with clients to design and deliver AI-powered solutions that create measurable business impact. You’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.
 
The Impact You’ll Have
 
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.
 
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.
 
The Role
 
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.
 
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.
 
You will have opportunities to work with major AI ecosystem partners through Bain’s partnerships, collaborating on real client deployments and helping shape how emerging capabilities are applied in enterprise settings.
 
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.
 
What You’ll Do
 
Build AI applications that drive real business outcomes
 <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>
Build and apply data science and machine learning capabilities
 <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>
Engineer for real delivery: POC → MVP → production
 <ul>
<li>Write clean, testable, maintainable code and ship AI services through the full SDLC: build → test → deploy → monitor → 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>
Thrive in a consulting environment
 <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>
What We’re Looking For (Qualifications)
 
Educational background
 <ul>
<li>Currently pursuing or recently completed a bachelor’s, master’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>
Core engineering + AI application skills
 <ul>
<li>0-2+ 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>
Breadth of knowledge across data science and machine learning
 <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>
Delivery mindset and consulting skills
 <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>
Working Model & Travel
 <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>
U.S. Compensation Information
 
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.
 
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).
 
<ul>
<li>Bain & 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>
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.
 References
 
Visible links
 1. https://www.bain.com/about/awards-recognition/
 2. https://www.bain.com/careers/work-with-us/integrated-teams/ai-insights-solutions-ais/

ML-AI Engineering Intern

Airtable

<div><div>Airtable is the no-code app platform that empowers people closest to the work to accelerate their most critical business processes. More than 500,000 organizations, including 80% of the Fortune 100, rely on Airtable to transform how work gets done.</div>
Airtable's Data Science &amp; Analytics team seeks an Analytics Engineer to embed within our Marketing organization. This is a high-impact, early-career role. You will be responsible for building the canonical data infrastructure, owning critical dashboards, and enabling Marketing stakeholders to execute faster, more confident, data-driven decisions.

We require a genuinely AI-native professional&mdash;not merely familiar with tools like Claude, Cursor, or ChatGPT, but one who integrates them as a core part of their daily workflow. The successful candidate will possess a full-stack mindset, a bias for action, and deep curiosity about how marketing data drives tangible business outcomes.

About the Team

The Data Science &amp; Analytics team at Airtable is the company-wide partner for building data infrastructure, metrics, and insights that directly inform decision-making. The Marketing Analytics Engineer will embed with the Marketing organization, working directly with GTM data and Airtable's core data stack: dbt, Databricks, Looker, and Omni.
<h1 class="jobSectionHeader">
What you'll do</h1>
<ul><li>Canonical Marketing Data Sources
<ul><li>Design and maintain trustworthy data models for core marketing metrics, managing the full lifecycle from prototyping through production.</li>
<li>Develop and govern dbt data pipelines, establishing data integrity standards and SLAs for timely, accurate delivery across the Marketing organization.</li>
</ul></li><li>Critical Dashboards and Self-Serve Tooling
<ul><li>Build and optimize dashboards that deliver real-time, self-serve insights across high-priority marketing areas: campaign performance, funnel conversion, pipeline contribution, and lead scoring.</li>
<li>Drive data independence for Marketing stakeholders, eliminating reliance on ad-hoc data requests and manual reporting.</li>
</ul></li><li>AI-Native Data Infrastructure
<ul><li>Collaborate with the Marketing team and data partners to establish the AI Business Context layer for marketing use cases.</li>
<li>Lead the development of tools that facilitate natural language data access and AI-assisted reporting for non-technical stakeholders.</li>
</ul></li><li>Trusted Partnership
<ul><li>Serve as the primary data partner for marketing managers, demand generation teams, and leadership.</li>
<li>Translate complex data insights into clear business recommendations via dashboards, memos, and presentations.</li>
</ul></li><li>Domain Expertise
<ul><li>Achieve a comprehensive mastery of Airtable's marketing data models, existing pipelines, and BI tools (dbt / Looker / Omni) within the first 6 months, becoming the definitive internal expert.</li>
</ul></li></ul><h1 class="jobSectionHeader">Who you are</h1>
<ul><li>Must-Have
<ul><li>Expert-level SQL: Proven ability to write complex queries involving joins, aggregations, and window functions.</li>
<li>Proficiency with dbt or equivalent data transformation tools.</li>
<li>Experience with BI and visualization platforms (Looker, Omni, Tableau, Hex, or similar).</li>
<li>Active, demonstrated daily use of AI coding tools (Cursor, Claude, ChatGPT, Gemini). Candidates must provide specific, concrete examples of how these tools are integral to their work, moving beyond simple familiarity.</li>
<li>Mandatory use of GitHub for version control in a standard development workflow.</li>
<li>Exceptional communication skills: the ability to translate technical data findings into compelling business narratives for non-technical leadership.</li>
</ul></li><li>Nice-to-Have
<ul><li>Python for data work (pandas, ETL scripting, or analysis).</li>
<li>Prior exposure to marketing data concepts: attribution, funnel metrics, lead scoring, or campaign performance.</li>
<li>Familiarity with CRM (Salesforce) or marketing automation platforms (Marketo).</li>
<li>Experience with Databricks or cloud data warehouses.</li>
<li>A public portfolio showcasing data or AI-assisted engineering work (GitHub, personal projects, Kaggle).</li>
</ul></li><li>Full-stack Mindset: You own problems end-to-end and drive the solution, even if it requires expanding the original scope.</li>
<li>Bias for Action: You prioritize effective delivery over perfection, operating with a 'ship, learn, and iterate' mentality.</li>
<li>Genuinely AI-Native: AI tools are fundamental to your work process. You leverage them to write cleaner SQL, debug models faster, generate documentation, and prototype solutions, and can articulate your specific usage.</li>
<li>Data Storyteller: You provide definitive business closure&mdash;framing findings as actionable recommendations, not just delivering technically correct output.</li>
<li>Thrives in Ambiguity: You proactively create clarity and forward momentum, even when requirements are incomplete or rapidly changing.</li>
</ul>
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AI Analytics Engineer (Marketing Analytics)

Baselayer

<div>
 
 
 About Baselayer:
 
 
 
 Trusted by 2,200+ financial institutions, Baselayer is the intelligent business identity platform that helps verify any business, automate KYB, and monitor real-time risk. Baselayer&rsquo;s B2B risk solutions &amp; identity graph network leverage state &amp; federal government filings and proprietary data sources to prevent fraud, accelerate onboarding, and lower credit losses.&nbsp;
 
 
 &nbsp;
 
 
 
 About You:
 
 
 
 You want to learn from the best of the best, get your hands dirty, and put in the work to hit your full potential. You're not just doing it for the win&mdash;you're doing it because you have something to prove and want to be great. You are looking to be an impeccable machine learning engineer working on cutting-edge AI solutions.
 
 <ul>
 <li>
 You have 4-8 years of experience in machine learning development, working with Python and building ML models
 </li>
 <li>
 You're comfortable working with large-scale data and enjoy optimizing performance for computationally intensive ML systems
 </li>
 <li>
 You have a strong foundation in AI/ML fundamentals, particularly with LLMs, and are eager to experiment with emerging techniques
 </li>
 <li>
 You prioritize responsible AI practices and model governance, especially in regulated environments like KYC/KYB
 </li>
 <li>
 You have a keen eye for detail and take pride in writing clean, maintainable code while optimizing for model performance
 </li>
 <li>
 You thrive in a high-trust, ownership-focused environment and are comfortable working across different levels of abstraction
 </li>
 <li>
 Problem-solver who navigates the unknown confidently
 </li>
 <li>
 Proactive self-starter who thrives in dynamic settings
 </li>
 <li>
 Incredibly intelligent and clever. You take pride in your models
 </li>
 <li>
 Highly feedback-oriented. We believe in radical candor and using feedback to get to the next level
 </li>
 </ul>
 
 
 Responsibilities:
 
 
 <ul>
 <li>
 Model Development &amp; Integration: Build and maintain ML models and integrate them with various data sources, ensuring scalability, high performance, and adaptability for autonomous agents in the GTM space
 </li>
 <li>
 ML System Design: Architect and design core ML services that support KYC/KYB processes, leveraging knowledge graphs and LLMs for dynamic use cases
 </li>
 <li>
 Data Processing &amp; Feature Engineering: Develop and maintain robust data pipelines for feature extraction and transformation, focusing on scalability and performance when handling large-scale, high-dimensional data
 </li>
 <li>
 Advanced ML Techniques: Implement and experiment with state-of-the-art techniques including reinforcement learning from human feedback (RLHF) and parameter-efficient fine-tuning methods (e.g., LoRA) to improve LLMs for specific use cases within the identity space
 </li>
 <li>
 ML Infrastructure: Build and maintain infrastructure for model training, evaluation, and deployment, creating a scalable platform foundation for continued innovation
 </li>
 <li>
 Model Governance &amp; Compliance: Ensure ML systems meet industry standards for fairness, explainability, and compliance, particularly around KYC/KYB regulations
 </li>
 <li>
 Performance Optimization: Implement optimizations for model inference and training, ensuring ML services can efficiently process identity data while maintaining reliability
 </li>
 <li>
 Experimentation &amp; Evaluation: Design and conduct experiments to evaluate model performance, debug issues, and monitor ML services, while continuously improving architectures to handle diverse data and use cases
 </li>
 </ul>
 
 &nbsp;
 
 
 
 Work Location
 
 
 <ul>
 <li>
 Hybrid in SF. In office 3 days/week
 </li>
 </ul>
 
 &nbsp;
 
 
 &nbsp;
 
 
 
 Compensation and Benefits
 
 
 <ul>
 <li>
 
 Salary range of $145,000 to $225,000
 
 </li>
 <li>
 
 Equity package
 
 </li>
 <li>
 
 Unlimited vacation
 
 </li>
 <li>
 
 Fully paid health insurance, dental, and vision
 
 </li>
 <li>
 
 401(k) with company match
 
 </li>
 </ul>
 
 &nbsp;
 
</div>

Machine Learning Engineer

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