SonusLabs LLC
Training-first internships

Careers at SonusLabs LLC

These internships are training-first and designed to produce a portfolio of practical artifacts (datasets, scoring rubrics, experiment plans, analytics artifacts, runbooks, demo kits). You’ll work on practice-based projects and simulations with weekly feedback.

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Open Internships

Type: Unpaid Internship (Training-first) · Location: Remote/Hybrid · Start: ASAP · Duration: 10–12 weeks (extendable) · Time: 10–15 hrs/week (flexible)

Training-first boundary: you will not be expected to replace employees, carry a quota, or perform essential business operations.

Internship Growth Analytics Competitive Intelligence

Growth Analytics & Competitive Intelligence Intern (Training Program)

SonusLabs LLC · Remote/Hybrid · Unpaid (Training-first) · 10–12 weeks (extendable) · 10–15 hrs/week
Alignment: Computer Science / Information Systems / Data Analytics / Business Analytics

What this internship is

A training-focused internship where you learn how to apply data, systems thinking, and experimentation to growth and go-to-market analysis. You will complete practice-based projects and simulations that produce a portfolio of analytical artifacts (funnels, experiment plans, competitive datasets).

What you’ll learn (outcomes)

  • Build a trial funnel model (activation → time-to-value → conversion) using structured data
  • Convert qualitative market research into structured datasets and insights
  • Design experiments (hypothesis, metrics, success criteria, instrumentation plan)
  • Apply basic analytics (cohorting, conversion rates, drop-off analysis) to sample data
  • Draft messaging frameworks as systems outputs (simulated, not executed independently)

Training activities (what you’ll do)

  • Competitive Intelligence as Data (primary): create a competitor dataset (pricing, features, positioning, pros/cons) in Google Sheets
  • Feature taxonomy + scoring rubric: define a taxonomy and scoring approach (where each solution wins/loses)
  • Weekly competitive change log: learning exercise to track updates and summarize implications
  • Funnel analytics practice projects: review sample journeys (provided) and propose improvements
  • Experiment design: propose 2 experiments/week (hypothesis + metric plan + measurement approach)
  • Copy variants: draft onboarding email sequence variants (not sent without review)
  • Conversion knowledge assets: draft FAQ copy from a “user objections → answers” knowledge map
  • Portfolio artifacts: mini-demo talk tracks + objection-handling scripts
  • Lead magnet concepts: draft 1–2 concepts (e.g., “missed-call calculator”) with inputs/outputs defined

Clear boundaries

  • No independent outreach to real prospects/customers
  • No ownership of revenue KPIs (e.g., “book X demos/week”)
  • No direct selling quotas
  • Any real-world usage of your drafts is optional and only after heavy supervision and revision

How you’ll be evaluated

  • Quality/clarity of competitor dataset + insights
  • Quality of experiment designs (hypothesis, metrics, instrumentation)
  • Strength of analytical thinking + iteration speed
  • Consistency: weekly deliverables, feedback incorporation, improvement

Requirements

  • Student in CS/IS/Analytics (or equivalent), or strong interest
  • Comfortable with spreadsheets + basic metrics
  • Strong written English and structured thinking

AI usage policy (allowed + expected)

  • You may use AI tools (ChatGPT, Claude, etc.) to brainstorm, outline, rewrite, and generate draft artifacts
  • You must verify claims and avoid inventing competitor facts
  • You must keep a short “AI usage note” for each deliverable (tool + what it helped with)
  • You must not paste confidential/internal data into public AI tools

What you’ll get

  • Portfolio: competitor dataset, scoring rubric, funnel experiment plans, copy variants
  • Mentorship + weekly feedback
  • Potential paid extension if a paid role becomes available
Apply by Email How we work
Internship Systems Integration Technical Enablement

Systems Integration & Technical Enablement Intern (Training Program)

SonusLabs LLC · Remote/Hybrid · Unpaid (Training-first) · 10–12 weeks (extendable) · 10–15 hrs/week
Alignment: Computer Science / Information Systems / Software Engineering / Cloud / IT

What this internship is

A training-first technical internship where you learn implementation workflows using sandbox environments, mock scenarios, and guided labs. You’ll create technical documentation and demo assets as a portfolio.

What you’ll learn (outcomes)

  • APIs, webhooks, and JSON debugging using Postman/cURL
  • Trial onboarding workflows and “time-to-first-success” design patterns
  • Demo scenario preparation and technical communication
  • Optional: cloud/serverless fundamentals (auth, env vars, basic monitoring)

Training activities (what you’ll do)

  • Sandbox integration labs (primary): practice webhook testing, API calls, and calendar integration in sandbox
  • Guided labs: complete a setup checklist lab (step-by-step)
  • Debugging drills: debug pre-made issues (provided logs/broken configs) and write fixes
  • Demo kit development: build demo scripts and mock flows for common scenarios
  • Test assets: create test datasets and “demo reset” checklists
  • Technical FAQ: draft common errors + resolutions (practice artifacts)
  • Documentation portfolio: runbooks like “How to test a webhook,” “How to connect calendar,” “Common API errors”
  • Templates: create Postman collections + cURL examples (learning templates)
  • Optional: small helper utilities (Python/JS) in a sandbox context
  • Competitive technical research (learning exercise): feature comparison table + tradeoffs (no claims you can’t verify)

Clear boundaries

  • No direct handling of real customer tickets
  • No production credentials / prod access by default
  • Work is primarily in sandbox/mock environments
  • Any real demo participation is observational (shadowing) unless supervised

How you’ll be evaluated

  • Debugging approach + ability to explain issues clearly
  • Quality of runbooks and troubleshooting steps
  • Completeness and clarity of demo kit artifacts
  • Demonstrated improvement over 10–12 weeks

Requirements

  • Student in CS/IS/Software/IT (or equivalent)
  • Comfortable with APIs, JSON, and basic scripting (Python/JS)
  • Strong communication

AI usage policy (allowed + expected)

  • You may use AI tools to draft documentation/runbooks, generate example requests/responses, and brainstorm troubleshooting steps
  • You must validate outputs by testing in sandbox
  • You must avoid copying secrets/credentials into AI tools
  • You must include a brief “AI usage note” per artifact

What you’ll get

  • Portfolio: onboarding playbooks, troubleshooting guides, demo kit, templates
  • Mentorship + weekly review
  • Potential paid extension if a paid role becomes available
Apply by Email How we work

How we work

Weekly iteration
You’ll set weekly learning goals, ship portfolio artifacts, and review outcomes.
Training-first boundaries
Practice-based work with supervision. No quotas, no essential business operations.
Clear deliverables
Each week ends with concrete outputs: datasets, rubrics, experiment plans, runbooks, demo kits.
Portfolio-friendly
You leave with artifacts you can show publicly (sanitized if needed).

Apply

Apply by email. Please include the role title in your subject line and share links to your resume/portfolio.

Email: it@sonuslabs.tech

Email Application Back to roles

Subject format: Internship Application — [Role] — [Name]

FAQ

Is this paid?

These roles are listed as unpaid training-first internships. If a paid extension becomes available, it will be discussed separately.

What’s the time commitment?

10–15 hours/week (flexible). Weekly deliverables are aligned to your availability and time zone.

Do I need to be in a specific location?

Remote or hybrid (your choice).

Can I use AI tools?

Yes — AI tools are allowed and expected. You must verify claims, avoid inventing facts, keep an AI usage note per deliverable, and never paste confidential/internal data or secrets into public AI tools.

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