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Plaid

<div><div><h2 class="jobSectionHeader">Location</h2>
San Francisco HQ
</div><div><h2 class="jobSectionHeader">Address</h2>
1098 Harrison Street, San Francisco, California, 94103
</div><div><h2 class="jobSectionHeader">Employment Type</h2>
Full time
</div><div><h2 class="jobSectionHeader">Location Type</h2>
Hybrid
</div><div><h2 class="jobSectionHeader">Department</h2>
All DepartmentsEPD in S&amp;M
</div><div><h2 class="jobSectionHeader">Compensation</h2>
<ul><li>Zone 1The target base salary for this position ranges from $216,000/year to $367,200/year in Zone 1. The target base salary will vary based on the job's location. Our geographic zones are as follows: Zone 1 - San Francisco / New York City / Seattle. The base salary range listed for this full-time position excludes commission (if applicable), equity and benefits. The pay range shown on each job posting is the minimum and maximum target for new-hire salaries. Actual pay may be higher or lower depending on factors like skills, experience, and relevant education or training. $216K &ndash; $367.2K</li>
</ul><div>Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.</div></div></div>
 <div><div>We believe that the way people interact with their finances will drastically improve in the next few years. We&rsquo;re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid&rsquo;s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam.
AI and intelligent systems are driving the fifth paradigm shift, following previous technological revolutions like mainframes, personal computers, the internet, and mobile devices.
We believe, in the foreseeable future, AI will revolutionize the FinTech industry - from how consumers understand and manage their finances, to how developers build applications and how all companies operate. The fintech industry landscape will undergo a fundamental reshape. 
 
Plaid in the FinTech AI Ecosystem
Plaid is uniquely positioned to become the financial data and insights backbone for AI applications and platforms in this evolving ecosystem.
We believe consumers should be able to understand and manage their financial life through conversational AI interfaces using natural language. We believe consumers should have peace of mind with a trustworthy consent and authorization manager when agents shop for them. We believe identity verification and financial fraud prevention in AI-powered products should feel seamless and embedded for the end users. The list goes on.
The most important AI companies, major fintechs, and customer agent platforms are actively trying to integrate Plaid into AI-powered products and solutions they build. 
 
Plaid&rsquo;s AI-powered Experiences for Customers
The explosive growth in model intelligence and increasing relevance for enterprise tasks offer a unique opportunity for Plaid to rethink from the ground up how we operate and interact with our customers for the most optimal experience.
Our early experiments in building AI agents for customer experience are paying off with huge business value. For example, our homegrown AI-powered customer support agents resolve a significant proportion of customer questions and issues within seconds, which would otherwise have required humans hours to work on.
This is just the beginning and there is so much more we could do. 
 
The AI Applications Team
You will have the opportunity to lead this newly formed team that is dedicated to rapidly scaling our successful bets so far, and grow with the team in our quest to accelerate Plaid&rsquo;s transformation into an AI-first company.
Responsibilities
<ul><li>You will lead a team of 4 engineers, ranging from junior to staff, developing them through clear goal setting, coaching, and feedback
</li><li>You will define and drive the long-term strategy and manage execution on the team, in close partnership with technical and product leaders across Plaid
</li><li>Have a front row seat to all the latest industry developments. Over time, with the skills and experience you develop and hone on this team, you can become an influential voice in defining where AI &lt;&gt; Fintech will be heading longer term.
</li></ul>You will work with your team to:
<ul><li>Lead projects that enable and scale our business with our largest AI customers and partners, starting with personal finance use cases and expanding into many others; examples include:
<ul><li>Develop and evolve the preferred integration pattern for Plaid with AI providers - from API adaptations to building the official Plaid MCP Servers, and beyond
</li><li>Redefine how Plaid&rsquo;s consumer link experience embed into conversational interfaces in the most seamless way
</li><li>Architect the trust layer for the future of agentic commerce that will become the industry standard
</li></ul></li><li>Scale and extend our existing successful bets on AI-powered customer experience; examples include:
<ul><li>Make the next step-function improvement in our homegrown customer support agent - land our multi-turn and multi-agent system that powers a truly delightful experience for our customers; define how to scalably run offline evaluation for complex multi-turn open-ended tasks; research and prototype how Human-In-The-Loop Reinforcement Learning (RLHF) can power an insights flywheel; pioneer the architecture for customer-specific long-term memory, etc.
</li><li>Extend our agentic system to support other critical parts of the customer journey, starting with areas with the highest ROI - top-of-funnel product recommendation, customer onboarding and risk diligence, customer activation and assistance for faster productionization, as well as upselling and cross-selling of Plaid products.
</li></ul></li></ul>Qualifications
Must-haves:
<ul><li>8+ years of industry experience, that includes time as a Staff-level engineer before transitioning into management.
</li><li>1+ years of engineering management experience
</li><li>Hands-on experience working with LLMs to build products and shipping them to product with iterating with real user feedback - including but not limited to:
<ul><li>Prompt engineering
</li><li>Fine-tuning
</li><li>Retrieval augmented generation (RAG)
</li><li>Semantic search - vector database and embedding models
</li><li>Agent orchestration framework
</li><li>Evaluation and monitoring framework of open-ended tasks
</li><li>Streaming and SSE
</li><li>Common UX and design patterns for GenAI-powered products
</li></ul></li><li>Ability to deeply understand customer and user needs through user research and rapid experimentation - be your own technical PM
</li><li>Track record of building and growing high performing engineering teams (either as an engineer or a manager).
</li><li>Ability to balance divergent thinking (exploring possibilities) with convergent thinking (evaluating feasibility), which is critical for driving 0 -&gt;1 projects
</li><li>Extremely curious and passionate about working in GenAI applications space
</li></ul>Nice to have:
<ul><li>Experience training and/or serving ML models in production, or fine-tuning LLMs for domain-specific use cases
</li><li>Comfortable operating in privacy/PII-sensitive environments and applying compliance mitigations
</li></ul>Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!
Plaid is proud to be an equal opportunity employer and values diversity at our company. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws. Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at accommodations@plaid.com. 

Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.</div></div>

Engineering Manager, AI Applications

Eli Lilly

At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We’re looking for people who are determined to make life better for people around the world.
 
Locations: San Diego, CA; San Francisco, CA; Boston, MA; Louisville, CO; Indianapolis, IN
 
Lilly Small Molecule Discovery is purpose-built to create molecules that make life better for people. Discovery Technology and Platforms (DTP) accelerates molecule discovery by building optimized foundational platforms, streamlining lab operations through advanced technologies and data connectivity, and investing in novel capabilities.
 Data Foundry is a multidisciplinary team within DTP that enables AI-native drug discovery through four integrated pillars: Architecture4Insight (data infrastructure and scientific software), Methods4Insight (analytical and computational methods), Automation & Scale4Insight (lab automation and agentic workflows), and Preparedness4Insight (data governance and readiness). These pillars empower every Lilly scientist to make optimal decisions by providing seamless access to data, insights, and AI-driven capabilities—serving both human scientists and autonomous AI agents.
 
Position Summary
 
We are seeking an Engineer - MLOps & Scientific Platforms - Data Foundry to operationalize Data Foundry’s scientific tools and analytical methods into actionable-prototypes. You will build the ML deployment pipelines, model serving infrastructure, API layers, and observability guardrails that make our scientific discovery methods and tools reliable, scalable, and consumable, both by discovery scientists and by the Frontier AI group’s autonomous agents.
 This role sits at the interface between Methods4Insight (which develops analytical methods) and Architecture4Insight(which provides the agile data infrastructure). Your job is to ensure every scientific tool Data Foundry produces are analytics-ready, well-monitored, and exposed through APIs with the response-time guarantees and error handling that both human users and AI agents require.
 
Responsibilities
 
MLOps & Model Lifecycle Management
 
<ul>
<li>Build and maintain end-to-end ML deployment pipelines: experiment tracking, model versioning (MLflow, Weights & Biases), containerized model serving, and automated retraining triggers.</li>
<li>Develop model registry infrastructure and feature engineering pipelines that enable computational scientists to access models.</li>
<li>Implement monitoring and alerting for data pipelines, APIs, ML models, and agentic systems (LLMOps) to ensure system reliability and performance at scale.</li>
<li>Build dashboards and metrics tracking for pipeline execution, API latency, token usage, model prediction quality, and system health</li>
<li>Establish structured logging and tracing infrastructure for debugging and performance optimization across scientific data systems</li>
</ul>
Scientific Tool Agile Deployment
 
<ul>
<li>Deploy predictive and analytical methods from Methods4Insight (e.g. cheminformatics, structural biology, bioinformatics, reaction informatics) with versioning, structured error handling, and response-time guarantees that enable insight generation in agile manner. Productionize when and where needed in partnerships with Tech@Lilly.</li>
<li>Build serving infrastructure supporting both synchronous (interactive scientist queries) and asynchronous (batch and agent-invoked) workloads in partnership with Tech@Lilly and Frontier AI.</li>
<li>Define and implement API contracts, documentation standards, and testing frameworks that ensure scientific tools are analysis ready, robust and consumable by external teams including Frontier AI.</li>
</ul>
Platform Engineering & Integration
 
<ul>
<li>Build and operate cloud-native model serving infrastructure (AWS, Azure, or GCP) using containers, Kubernetes, and infrastructure-as-code.</li>
<li>Develop CI/CD pipelines for ML models: automated validation, A/B testing, canary deployments, and rollback procedures.</li>
<li>Integrate model serving with Data Foundry’s data pipelines, ensuring models have access to properly formatted, versioned training and inference data.</li>
</ul>
Frontier AI Interface & Collaboration
 
<ul>
<li>Partner with the Frontier AI team and Tech@Lilly to ensure Data Foundry’s scientific tools are exposed via well-defined interfaces (REST APIs, MCP-compatible endpoints) that agents can invoke programmatically.</li>
<li>Collaborate on API performance requirements: latency targets, throughput guarantees, and graceful degradation under load.</li>
<li>Work with Methods4Insight scientists to ensure deployed models include appropriate uncertainty quantification and confidence metrics.</li>
</ul>
Basic Requirements
 
<ul>
<li>B.S. or M.S. in Computer Science, Data Science, Machine Learning, Bioinformatics, Computational Biology, or related field.</li>
<li>3+ years of experience in MLOps, ML engineering, or scientific platform development</li>
<li>Qualified applicants must be authorized to work in the United States on a full-time basis. Lilly will not provide support for or sponsor work authorization or visas for this role, including but not limited to F-1 CPT, F-1 OPT, F-1 STEM OPT, J-1, H-1B, TN, O-1, E-3, H-1B1, or L-1.</li>
</ul>
Preferred Qualifications
 
<ul>
<li>Pharmaceutical or biotech research industry experience.</li>
<li>Strong Python skills; experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and ML lifecycle tools (MLflow, W&B, Kubeflow, or similar).</li>
<li>Proven track record building and deploying production model serving infrastructure — containerized endpoints, RESTful/gRPC APIs, and operational monitoring</li>
<li>Working knowledge of cloud platforms (AWS, Azure, or GCP), Kubernetes, and CI/CD automation.</li>
<li>Strong communication skills with ability to collaborate across computational scientists, software engineers, and partner teams.</li>
<li>Experience operationalizing scientific or computational models (cheminformatics, bioinformatics, structural biology, QSAR, molecular simulations, PK/PD, systems biology, or ODE-based models).</li>
<li>Hands-on experience with model monitoring, drift detection, and automated retraining systems.</li>
<li>Familiarity with API gateway patterns, event-driven architectures, and service mesh technologies.</li>
<li>Experience with feature stores, data versioning (DVC), or experiment tracking at scale.</li>
<li>Exposure to AI agent frameworks (MCP, LangChain) or building APIs that AI systems invoke programmatically.</li>
<li>Experience with C, C++, CUDA, or GPU-accelerated computing for optimizing model training/inference performance; familiarity with containerizing HPC workloads (Singularity/Apptainer).</li>
</ul>
Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form (https://careers.lilly.com/us/en/workplace-accommodation) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.
 
Lilly is proud to be an EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status.
 
Our employee resource groups (ERGs) offer strong support networks for their members and are open to all employees. Our current groups include: Africa, Middle East, Central Asia Network, Black Employees at Lilly, Chinese Culture Network, Japanese International Leadership Network (JILN), Lilly India Network, Organization of Latinx at Lilly (OLA), PRIDE (LGBTQ+ Allies), Veterans Leadership Network (VLN), Women’s Initiative for Leading at Lilly (WILL), enAble (for people with disabilities). Learn more about all of our groups.
 
Actual compensation will depend on a candidate’s education, experience, skills, and geographic location. The anticipated wage for this position is
 $66,000 - $165,000
 
Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly’s compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.
 
#WeAreLilly

Engineer - MLOps & Scientific Platforms - Data Foundry

OpenAI

About the Team
 
The Intelligence and Investigations team seeks to rapidly identify and mitigate abuse and strategic risks to ensure a safe online ecosystem. We are dedicated to identifying emerging abuse trends, analyzing risks, and working with our internal and external partners to implement effective mitigation strategies to protect against misuse. Our efforts contribute to OpenAI's overarching goal of developing AI that benefits humanity.
 
The Strategic Intelligence & Analysis (SIA) team provides safety intelligence for OpenAI’s products by monitoring, analyzing, and forecasting real-world abuse, geopolitical risks, and strategic threats. Our work informs safety mitigations, product decisions, and partnerships, ensuring OpenAI’s tools are deployed securely and responsibly across critical sectors.
 
About the Role
 
As an Agentic Risk Analyst, you will shape OpenAI’s operating picture for current agentic risk across products and platforms. You will bring a strategic, system-level perspective to current risks, connecting individual incidents, technical findings, abuse patterns, and external developments to relevant workstreams, mitigations, owners, dependencies, and residual gaps. You will analyze how risks emerge through autonomy, multi-step task execution, tool use, memory, retrieval, connectors, computer-use capabilities, and multi-agent workflows, with a particular focus on both adversarial misuse and unintended system behavior. By synthesizing signals from investigations, evaluations, red teaming, security reviews, product launches, external research, and real-world incidents, you will maintain a current view of material risks and evolving threat patterns. Your work will help turn complex and often ambiguous signals into coordinated decisions and measurable follow-through across product, safety, security, policy, and governance teams.
 
You will work closely with investigators, engineers, product, policy, safety, and security teams, and measurement and forecasting experts who lead longer-horizon risk discovery and scenario work to maintain a shared operating picture of current risks, mitigation priorities, owners, and dependencies. This is an opportunity to help shape how OpenAI coordinates decisions and follow-through across the evolving risk landscape of increasingly capable agentic systems, ensuring that safety decisions keep pace with rapidly advancing technology.
 
In this role, you will: 
Build and maintain a current, company-wide portfolio of material agentic risks across OpenAI’s products, platforms, and emerging capabilities, mapping each risk to relevant workstreams, owners, mitigations, dependencies, decisions, and residual gaps.
 
Run a cross-functional intake and review cadence for signals from across OpenAI and the broader ecosystem to identify emerging risks, evolving threat patterns, and important shifts in the agentic risk landscape, routing findings to the right owners and decision-makers
 
Connect individual incidents, technical findings, evaluations, and weak signals to broader system-level trends, producing clear assessments of impact, severity, evidence, uncertainty, priority, and recommended action.
 
Assess how emerging capabilities, product changes, ecosystem developments, and adversary adaptation may affect current risk priorities and launch readiness, surfacing risks that are unowned, stalled, or under-mitigated for decision and escalation, and tracking residual risk after launch.
 
In partnership with colleagues who lead horizon scanning, use relevant external developments across AI safety and security research, public incidents, adversarial activity, industry standards, emerging technologies, and competitor products, as inputs to current risk prioritization and mitigation decisions at OpenAI.
 
Apply and refine practical frameworks and taxonomies for current agentic failure modes, control gaps, abuse patterns, and potential downstream harms across products, deployment environments, and user workflows, integrating them into the broader risk program.
 
Produce concise, decision-ready assessments that communicate key findings, assumptions, confidence levels, competing hypotheses, and prioritized recommendations for product, safety, security, policy, and leadership stakeholders, with accountable owners and next review points.
 
Define and track operating metrics for the agentic-risk program, including coverage, ownership, decision latency, mitigation status, and closure and partner with measurement experts on risk indicators and monitoring capabilities.
 
Partner with colleagues who lead evaluations, scenario analyses, tabletop exercises, red-team campaigns, and investigations focused on agentic systems, using their findings to drive actionable mitigations, accountable ownership, and measurable follow-through.
 
You might thrive in this role if you
 
Have significant experience—typically 7+ years—in trust and safety, integrity, security, cyber threat intelligence, AI safety, product risk, strategic intelligence, abuse investigations, or a related field.
 
Have a strong understanding of modern AI systems and agentic architectures, including hands-on experience using, evaluating, or building agentic systems. You are familiar with AI safety concepts, agentic failure modes, and misalignment risks, and can reason about how agent objectives, incentives, environments, and system design choices may produce unintended or harmful outcomes.
 
Have demonstrated experience analyzing how harmful outcomes emerge from interactions between users, products, and technical systems, and can assess how increasingly capable agentic systems may amplify, automate, or transform existing abuse vectors such as fraud, scams, social engineering, coordinated influence operations, cyber abuse, or other forms of platform misuse. You have also helped operate a cross-functional risk, safety, security, or integrity program, clarifying ownership, resolving dependencies, escalating gaps, and driving follow-through without direct authority.
 
Have exceptional analytical judgment and experience producing assessments under uncertainty. You can identify weak signals, develop and test hypotheses, distinguish signal from noise, communicate assumptions and confidence levels clearly, and update your conclusions as new evidence becomes available.
 
Possess strong technical fluency and are comfortable engaging deeply with engineers, researchers, and security practitioners. You do not need to be an engineer, but you should be able to translate technical findings into clear risk assessments.
 
Are comfortable synthesizing data and evidence from multiple sources to manage a current risk portfolio, including investigations, telemetry, evaluations, experiments, dashboards, and external research. Experience using SQL, Python, and analytical tools is a plus.
 
Have experience applying risk frameworks, threat models, severity assessments, taxonomies, or prioritization frameworks to structure current operational ambiguity and support decision-making.
 
Communicate complex topics clearly and effectively, producing concise, executive-ready analysis and influencing stakeholders across technical and non-technical teams.
 
Thrive in fast-moving environments, balancing rigor with pragmatism while building lightweight operating processes and tools that improve organizational coordination and follow-through on material risks.
 
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’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.
 
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
 
$288K – $425K + Offers Equity

Agentic Risk Analyst

Dnata

dnata is more than a global air and travel services provider. We connect people with possibilities and opportunities across 130+ airports in 34 countries through ground handling, cargo, travel, and catering & retail services. Our purpose is clear: to deliver on the promises our customers make. We are powered by a bold vision to be the world’s most admired air and travel services provider, which guides our strategy, culture, and every role across our teams. Our core values fuel everything we do: we are safe, we play to win, we respect everyone and work together as a team, and we are future thinking. At dnata, we foster diversity, nurture ambition, and celebrate achievement. If you're inspired by purpose and motivated to be part of a global team shaping the future of aviation and travel, we’d love to have you onboard.
 On the ground, dnata USA keeps 27 of the country’s major airports connected – both with each other and the world - including John F. Kennedy Airport in New York City. Handling 200,000 tonnes of cargo, guiding 15,000,000 passengers and keeping 50,000 flights in the air each year, the work that the dnata USA team complete on the ground is crucial to keeping the country in the air.
 
As a dnata Cabin Cleaning Supervisor, you will play an important part in supervising and performing the safety and efficient cleaning of the aircraft cabin.
 
Essential Duties and Responsibilities
 
Ensure thorough aircraft cleaning following company training guidelines and specifications.
 
Ensure cleaning supplies are ready for reach arrival, following EPA and company procedures.
 
Ensure compliance with TSA and FAA regulations and policies.
 
Consistently delivers om the company’s and the customer’s standards and expectations.
 
Train Cabin Service personnel and assist in their scheduling.
 
Monitor and request all supplies needed in operation.
 
Perform the same or similar work as assigned to the group being supervised.
 
Establishes a culture that promotes safety through daily observation, shift briefings, routine audits, training, and documented safety procedures.
 
Carries out supervisory responsibilities in accordance with the organization’s policies and applicable laws.
 
Schedule personnel as per operational needs
 
Job Qualifications
 
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
 
Must be physically able to perform the essential functions of the job, with or without accommodations including but not limited to standing, lifting, bending, stooping, walking, crawling, pushing, and pulling for extended periods of time.
 
Push, pull, lift, and load up to 70 pounds (32 kg) on a regular basis.
 
Ascend/descend jet-way stairs.
 
Ability to understand and react to verbal and visual commands and safety warnings, with and without assistance of sight and visual aids.
 
Work outside in all types of weather and around jet and machinery noises
 
Wear proper Personal Protective Equipment
 
Prior supervisor experience in Cabin Cleaning preferred
 
Possess/maintain FAA/Airport required identification/seals or authorizations.
 
Full time
 $28.50

Cabin Service Supervisor

Explore 4+ Agent in San Francisco this June. Find roles across top companies and apply now to boost your career.

4 agent jobs In San Francisco

Engineering Manager, AI Applications

Engineer - MLOps & Scientific Platforms - Data Foundry

Agentic Risk Analyst

Cabin Service Supervisor