<h3>Who We Are</h3>
Rockerbox with Double Verify empowers marketing executives to confidently make data-driven decisions, helping brands such as Tula, Figs, and Burton with the strategic decision-making that drives growth. To do so, Rockerbox offers a unique suite of product lines that centralize data and offer diversified measurement methodologies. The foundation of Rockerbox's solution is data centralization. Atop this foundation, the platform enables marketers to choose from a range of measurement methodologies, giving customers the flexibility to choose the most appropriate approach for their specific needs and questions. We are looking for a Senior Software Engineer II to design and build the next generation of AI tooling and infrastructure. This role focuses on developing Model Context Protocol (MCP) integrations, and enabling workflow automation with tools like n8n. You’ll be at the intersection of backend engineering, AI systems, and developer tooling—helping shape the foundation for our AI-first platform.
<h3>What You’ll Do</h3>
<ul>
<li style="font-family: arial, helvetica, sans-serif; font-size: 12pt;">Design, implement, and maintain MCP servers and connectors to integrate LLMs with internal and external systems.</li>
<li style="font-family: arial, helvetica, sans-serif; font-size: 12pt;">Extend and integrate n8n workflows (or equivalent orchestration tools) for automation of AI-powered tasks.</li>
<li style="font-family: arial, helvetica, sans-serif; font-size: 12pt;">Collaborate with product, data, and AI teams to deliver developer-facing tools and SDKs.</li>
<li style="font-family: arial, helvetica, sans-serif; font-size: 12pt;">Ensure security, observability, and reliability in AI-enabled services.</li>
<li style="font-family: arial, helvetica, sans-serif; font-size: 12pt;">Mentor other engineers and contribute to team-wide best practices in AI-first software engineering. </li>
</ul>
<h3>Who You Are</h3>
<ul>
<li style="font-family: arial, helvetica, sans-serif; font-size: 12pt;">5–8+ years of professional software engineering experience.</li>
<li style="font-family: arial, helvetica, sans-serif; font-size: 12pt;">Hands-on experience with MCP (Model Context Protocol) or similar AI-agent frameworks.</li>
<li style="font-family: arial, helvetica, sans-serif; font-size: 12pt;">Familiarity with n8n, Langchain or similar.</li>
<li style="font-family: arial, helvetica, sans-serif; font-size: 12pt;">Strong expertise in Python and in Semantic data definitions (CubeJS etc)</li>
<li style="font-family: arial, helvetica, sans-serif; font-size: 12pt;">Proven experience designing and deploying APIs with strong contracts/interfaces.</li>
<li style="font-family: arial, helvetica, sans-serif; font-size: 12pt;">for orchestration.</li>
<li style="font-family: arial, helvetica, sans-serif; font-size: 12pt;">Knowledge of data pipelines (Snowflake, DuckDB, dbt, Kafka) and secure API integrations.</li>
<li style="font-family: arial, helvetica, sans-serif; font-size: 12pt;">Solid grounding in security practices: secrets management, data redaction, rate limiting</li>
<li style="font-family: arial, helvetica, sans-serif; font-size: 12pt;">Excellent communication skills and ability to work across cross-functional teams</li>
</ul>
&nbsp;
<h3>Nice to Haves</h3>
<ul>
<li style="font-family: arial, helvetica, sans-serif; font-size: 12pt;">Strong observability and monitoring skills (OpenTelemetry, Prometheus, Grafana, Sentry).</li>
<li style="font-family: arial, helvetica, sans-serif; font-size: 12pt;">Experience scaling LLM-driven systems from prototype to production. </li>
</ul>
<h3>What Success Looks Like</h3>
<ul>
<li style="font-family: arial, helvetica, sans-serif; font-size: 12pt;">Within the first 3 months, you’ve shipped internal automation workflows in n8n (or similar) that remove manual processes and save engineering, professional services, or customer success teams time.</li>
<li style="font-family: arial, helvetica, sans-serif; font-size: 12pt;">By 6 months, you’ve built reliable APIs and MCP connectors that make it easy for LLMs to interface with internal systems.</li>
<li style="font-family: arial, helvetica, sans-serif; font-size: 12pt;">You’re recognized by peers as a go-to engineer for AI-powered automation, helping other teams design and implement their own integrations.</li>
<li style="font-family: arial, helvetica, sans-serif; font-size: 12pt;">Within a year, your tools are embedded in day-to-day workflows across the company, improving velocity, data access, and operational efficiency across engineering, professional services, and support teams.</li>
</ul>
 The successful candidate’s starting salary will be determined based on a number of non-discriminating factors, including qualifications for the role, level, skills, experience, location, and balancing internal equity relative to peers at DV. The estimated salary range for this role based on the qualifications set forth in the job description is between [$107,000 - $193,000]. This role will also be eligible for bonus/commission (as applicable), equity, and benefits. The range above is for the expectations as laid out in the job description; however, we are often open to a wide variety of profiles, and recognize that the person we hire may be more or less experienced than this job description as posted. 
Not-so-fun fact:&nbsp;<a href="https://urldefense.com/v3/__https://hbr.org/2014/08/why-women-dont-apply-for-jobs-unless-theyre-100-qualified__;!!P6nVbRxDCw3cNFlf!EJszlPs7CwYzNT-NJOg9EZZm8SLk_u9r0_XmHjJQZBRBzKMxMlLzRyQb5e9Gb6uO0F2xTBQvUt-QaHkxF-J6DnOrpLeuZQc$" target="_blank" data-saferedirecturl="https://www.google.com/url?q=https://urldefense.com/v3/__https://hbr.org/2014/08/why-women-dont-apply-for-jobs-unless-theyre-100-qualified__;!!P6nVbRxDCw3cNFlf!EJszlPs7CwYzNT-NJOg9EZZm8SLk_u9r0_XmHjJQZBRBzKMxMlLzRyQb5e9Gb6uO0F2xTBQvUt-QaHkxF-J6DnOrpLeuZQc$&amp;source=gmail&amp;ust=1684938412437000&amp;usg=AOvVaw0CniBQNf70G5TxCR0QyED2">Research</a>&nbsp;shows that while men apply to jobs when they meet an average of 60% of job criteria, women and other marginalized groups tend to only apply when they check every box. So if you think you have what it takes but you’re not sure that you check every box, apply anyway!
&nbsp;

### **Who We Are**

Rockerbox with Double Verify empowers marketing executives to confidently make
data-driven decisions, helping brands such as Tula, Figs, and Burton with the
strategic decision-making that drives growth. To do so, Rockerbox offers a
unique suite of product lines that centralize data and offer diversified
measurement methodologies. The foundation of Rockerbox's solution is data
centralization. Atop this foundation, the platform enables marketers to choose
from a range of measurement methodologies, giving customers the flexibility to
choose the most appropriate approach for their specific needs and questions.  
  
We are looking for a Senior Software Engineer II to design and build the next
generation of AI tooling and infrastructure. This role focuses on developing
Model Context Protocol (MCP) integrations, and enabling workflow automation
with tools like n8n. You’ll be at the intersection of backend engineering, AI
systems, and developer tooling—helping shape the foundation for our AI-first
platform.

### **What You’ll Do**

  * Design, implement, and maintain **MCP servers and connectors** to integrate LLMs with internal and external systems.
  * Extend and integrate **n8n workflows** (or equivalent orchestration tools) for automation of AI-powered tasks.
  * Collaborate with product, data, and AI teams to deliver developer-facing tools and SDKs.
  * Ensure security, observability, and reliability in AI-enabled services.
  * Mentor other engineers and contribute to team-wide best practices in **AI-first software engineering**.  
  

### **Who You Are**

  * 5–8+ years of professional software engineering experience.
  * Hands-on experience with MCP (Model Context Protocol) or similar AI-agent frameworks.
  * Familiarity with n8n, Langchain or similar.
  * Strong expertise in Python and in Semantic data definitions (CubeJS etc)
  * Proven experience designing and deploying APIs with strong contracts/interfaces.
  * for orchestration.
  * Knowledge of data pipelines (Snowflake, DuckDB, dbt, Kafka) and secure API integrations.
  * Solid grounding in security practices: secrets management, data redaction, rate limiting
  * Excellent communication skills and ability to work across cross-functional teams



### **Nice to Haves**

  * Strong observability and monitoring skills (OpenTelemetry, Prometheus, Grafana, Sentry).
  * Experience scaling LLM-driven systems from prototype to production.  
  

### **What Success Looks Like**

  * Within the first 3 months, you’ve shipped internal automation workflows in n8n (or similar) that remove manual processes and save engineering, professional services, or customer success teams time.
  * By 6 months, you’ve built reliable APIs and MCP connectors that make it easy for LLMs to interface with internal systems.
  * You’re recognized by peers as a go-to engineer for AI-powered automation, helping other teams design and implement their own integrations.
  * Within a year, your tools are embedded in day-to-day workflows across the company, improving velocity, data access, and operational efficiency across engineering, professional services, and support teams.

  
The successful candidate’s starting salary will be determined based on a
number of non-discriminating factors, including qualifications for the role,
level, skills, experience, location, and balancing internal equity relative to
peers at DV.  
The estimated salary range for this role based on the qualifications set forth
in the job description is between [$107,000 - $193,000]. This role will also
be eligible for bonus/commission (as applicable), equity, and benefits.  
The range above is for the expectations as laid out in the job description;
however, we are often open to a wide variety of profiles, and recognize that
the person we hire may be more or less experienced than this job description
as posted.  
  

**Not-so-fun fact:**
[Research](https://urldefense.com/v3/__https://hbr.org/2014/08/why-women-dont-
apply-for-jobs-unless-
theyre-100-qualified__;!!P6nVbRxDCw3cNFlf!EJszlPs7CwYzNT-
NJOg9EZZm8SLk_u9r0_XmHjJQZBRBzKMxMlLzRyQb5e9Gb6uO0F2xTBQvUt-
QaHkxF-J6DnOrpLeuZQc$) shows that while men apply to jobs when they meet an
average of 60% of job criteria, women and other marginalized groups tend to
only apply when they check every box. So if you think you have what it takes
but you’re not sure that you check every box, apply anyway!



