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Cloud Support Engineer

AI Summary
AlphaSense is seeking a Cloud Support Engineer to provide second-line technical support for customer issues across SaaS and private-cloud environments. The role owns assigned cases from investigation through resolution, using logs, traces, reproducible test cases, and collaboration with frontline support and engineering teams. Candidates need 2–4 years of relevant support or cloud operations experience, practical AWS, Kubernetes, API, and scripting capabilities, and strong customer communication skills. The position also emphasizes AI-assisted troubleshooting, documentation, proactive risk identification, and a potential growth path into senior support or SRE roles.
Remote from
USA
Salary
USD 100k–111k / yr
Employment
Full Time
Experience
Senior
Published
Apply before
5 Dec 2026
Listing views
35
Application actions
1
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Opportunity details

About this role.

About AlphaSense:

AlphaSense is the AI platform redefining market intelligence and workflow orchestration, trusted by thousands of leading organizations to drive faster, more confident decisions in business and finance. The platform combines domain-specific AI with a vast content universe of over 500 million premium business documents — including equity research, earnings calls, expert interviews, filings, news, and internal proprietary content. Purpose-built for speed, accuracy, and enterprise-grade security, AlphaSense helps teams extract critical insights, uncover market-moving trends, and automate complex workflows with high-quality outputs. With AI solutions like Generative Search, Generative Grid, and Deep Research, AlphaSense delivers the clarity and depth professionals need to navigate complexity and obtain accurate, real-time information quickly. For more information, visit www.alpha-sense.com. Come join us.

About the Team

Customer & Product Support (C&PS) sits at the intersection of sales, customer success and R&D teams. Through close teamwork and collaboration, we drive positive customer outcomes and ensure seamless access and optimal utilization of AlphaSense’s market-leading platform and products. We are committed to enhancing every user’s experience through consistent delivery of prompt and knowledgeable responses. The Customer & Product Support team is based globally across the US, Canada, UK, India and Singapore.

About the Role

We are looking for a proactive Cloud Support Engineer to join our dynamic team. In this role, you will own technical problems end-to-end within your scope including investigating, diagnosing and resolving customer issues across SaaS and private cloud environments. In your role day-to-day, you will collaborate closely with senior engineers and technical customer contacts on more complex cases.

You will be the second line of defense for technical queries, working at the intersection of customers, frontline support and Engineering. This is a strong foundation role for someone early in their cloud support career with some experience, who has demonstrated relevant technical skills, contributing to a high-trust team and the ability to apply AI tools to scale their impact.

Growth paths from this role include progression with the Support function to Senior Cloud Support Engineer and beyond, or a move into our Site Reliability Engineering (SRE) team, supported by structured training and collaboration with Engineering.

Who You Are

  • A technically curious engineer with 2 – 4 years in technical support, IT operations, cloud operations or a related field.
  • A clear communicator in writing and on customer calls, with the ability to explain technical concepts with appropriate clarity.
  • A problem-solver with a proactive, customer-first mindset and an eye for surfacing risks early.
  • Adaptable, eager to continuously learn, and willing to ask for help when blocked.
  • Energized by being part of an entrepreneurial team and willing to pull your own weight to help the team succeed.

What You’ll Do

  • Deliver exceptional technical support: Act as the second line of defense for technical queries, ensuring timely and effective resolution of customer issues through close collaboration between L1 support and R&D teams. Own the customer-facing loop on your assigned tickets, driving them to resolution with urgency.
  • Troubleshoot and resolve technical challenges: Diagnose issues using logs, traces, and reproducible test cases; work confidently with kubectl, awscli, and at least one scripting language. Resolve in-scope issues completely and escalate out-of-scope issues to the correct engineering domain with high-quality artifacts.
  • Collaborate cross-functionally: Partner effectively within the team and across regions and time zones; share knowledge proactively and close loops on commitments. Build working relationships with both account teams and R&D as you grow your technical credibility.
  • Documentation: Contribute to runbooks, KEDB articles, and FAQs. Maintain documentation hygiene on the issues you own.
  • Be an advocate for customers: Represent the voice of the customer in internal discussions and initiatives to maximize the value of our platform and products.
  • Build working knowledge of the AlphaSense platform and core products. Identify your own knowledge gaps and build a plan to close them.
  • Use AI tools in your daily work for technical investigations, log analysis, customer communication and documentation. Build repeatable prompts and workflows rather than one-off use; share what works with the team.

Minimum Qualifications:

  • Bachelor’s degree in Computer Science, Information Technology, or a related field (or equivalent practical experience).
  • 2 – 4 years in technical support, IT operations, cloud operations or a related field.
  • Working proficiency with CLI tools (e.g., kubectl, awscli) and scripting skills in Python, JavaScript or similar.
  • Working knowledge of GraphQL, REST APIs and MCP-related troubleshooting.
  • Familiarity with connecting LLMs to external tools, including multi-agent systems.
  • Exposure to alerting and logging systems (e.g., Prometheus, Grafana, FireHydrant).
  • Demonstrated AI fluency in day-to-day work: can speak to examples of how AI has changed the way they work.
  • Experience managing customer support cases through their lifecycle from inquiry, triage, bug reporting, through to resolution.
  • Ability to communicate complex technical concepts clearly to customers and team members.

Preferred Qualifications:

  • Experience in networking and troubleshooting complex network issues
  • Experience troubleshooting in at least one major cloud platform (preferably AWS) and containerized environments using Kubernetes or Docker.
  • Extensive experience working with GraphQL and other web APIs
  • Hands-on experience with Infrastructure as Code tools, such as Crossplane, and related troubleshooting
  • Experience with Search Technologies and Data Storages (e.g. Vespa, ElasticSearch, MongoDB, MySQL)
  • Experience or familiarity with the Java programming language
  • Experience with standard software release lifecycles

For base compensation, we set standard ranges for all roles based on function and level benchmarked against similar stage growth companies and internal comparables. In order to be compliant with local legislation, as well as to provide greater transparency to candidates, we share salary ranges on all job postings regardless of desired hiring location. Final offer amounts are determined by multiple factors including candidate experience/expertise and may vary from the amounts listed below.

You may also be offered a performance-based bonus, equity, and a generous benefits program.

Base Compensation Range

$100,000—$111,000 USD

AlphaSense is an equal-opportunity employer. We are committed to a work environment that supports, inspires, and respects all individuals. All employees share in the responsibility for fulfilling AlphaSense’s commitment to equal employment opportunity. AlphaSense does not discriminate against any employee or applicant on the basis of race, color, sex (including pregnancy), national origin, age, religion, marital status, sexual orientation, gender identity, gender expression, military or veteran status, disability, or any other non-merit factor. This policy applies to every aspect of employment at AlphaSense, including recruitment, hiring, training, advancement, and termination.

In addition, it is the policy of AlphaSense to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws, regulations, and ordinances where a particular employee works.

Recruiting Scams and Fraud

We at AlphaSense have been made aware of fraudulent job postings and individuals impersonating AlphaSense recruiters. These scams may involve fake job offers, requests for sensitive personal information, or demands for payment. Please note:

  • AlphaSense never asks candidates to pay for job applications, equipment, or training.
  • All official communications will come from an @alpha-sense.com email address.
  • If you’re unsure about a job posting or recruiter, verify it on our Careers page.

If you believe you’ve been targeted by a scam or have any doubts regarding the authenticity of any job listing purportedly from or on behalf of AlphaSense please contact us. Your security and trust matter to us.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

4/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

4/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThe role requires hands-on diagnosis across cloud, Kubernetes, APIs, logging, and customer-facing incident workflows. Engineers must independently drive cases while producing high-quality escalation artifacts and communicating clearly with technical and non-technical stakeholders.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$105,500
US market range (per year)$90k–$125k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses a base compensation range of $100,000–$111,000 USD. The annual cadence is inferred from the range magnitude and the US full-time cloud support context. Estimated US market pay for a Cloud Support Engineer with 2–4 years of experience is approximately $90,000–$125,000 USD per year; bonus and equity are excluded from these base-pay figures.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you investigate a customer-reported issue in a Kubernetes-hosted SaaS application?

I would first clarify the user impact, timing, affected tenant, and reproducible steps. I would review relevant application and Kubernetes logs, pod health, events, traces, and recent deployments, then compare the findings with expected behavior. If the issue is outside my ownership scope, I would create an escalation with evidence, impact, reproduction details, timestamps, and my initial hypothesis.

Describe how you have used scripting to improve a support workflow.

I have used scripts to collect and normalize diagnostic data, query APIs, parse logs, and reduce repetitive investigation steps. A useful script should produce consistent output, handle errors safely, and be documented so other support engineers can reuse it. I would also identify whether the workflow can be incorporated into a runbook or automated alert response.

How do you explain a complex technical issue to a customer who is not deeply technical?

I begin with the customer impact and the current status in plain language, avoiding unnecessary internal details. I explain what we know, what actions are underway, and when they can expect the next update. I tailor the depth of technical detail to the audience while remaining transparent about limitations and dependencies.

What information makes an escalation to an engineering team effective?

An effective escalation includes the customer impact and priority, a concise issue statement, exact timestamps, environment and tenant context, reproduction steps, relevant logs or traces, recent changes, and troubleshooting already completed. I also state a clear question or requested action so the receiving team can begin work without repeating basic discovery.

How would you apply AI tools responsibly during a support investigation?

I would use AI to summarize sanitized logs, suggest investigation paths, draft customer updates, and create repeatable troubleshooting prompts. I would validate all outputs against source evidence, avoid entering sensitive customer data into unapproved tools, and document successful workflows so they can be reviewed and reused by the team.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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