Applied AI Engineer, Applied AI

Snowplow•,

About The Position

Snowplow is hiring an Applied AI Engineer to support its Growth team within Marketing. The role is embedded with Growth, while reporting into Applied AI for management, code review, career development, and engineering standards. The engineer will take ownership of existing go-to-market workflows that generate pipeline, make them reliable and production-grade, and improve them continuously. The role works closely with the Director of Growth, data engineers, a platform engineer, Growth Associates, and account executives. Priorities are set according to pipeline impact, while the Applied AI team’s standards guide implementation. The engineer owns production workflows including outbound orchestration, campaign building, intent-signal routines, enrichment and signal pipelines, and paid-media automation. They will add agent-based review and approval steps before outreach is sent, develop feedback loops using replies and booked meetings, instrument sends and test messaging, and maintain signals and enrichment data flowing into Snowflake. They will also build seller-facing Claude skills and routines, manage contact rules and deliverability safeguards, and progressively migrate workflows to the Firn factory framework. Operational ownership includes version control, alerting, tracing, runbooks, documented handovers, and a weekly planning and reporting cadence with the Director of Growth. The interview process includes a short coding exercise and a working session in which candidates demonstrate how they use AI on a real task with their own setup.

Requirements

Experience writing software that runs in production.
Python and SQL proficiency, daily use of git, and ability to learn and use APIs by reading their documentation.
Experience with workflow automation tools such as n8n, Clay, or Zapier, and ability to write code when those tools are insufficient.
Experience building with LLMs for practical work, including prompts, tools, structured outputs, evaluation, and designing for model failure modes.
Operational reliability practices, including alerting, error handling, retries, and recovery; ability to address root causes rather than isolated failures.
Experience building or maintaining data pipelines into Snowflake or an equivalent data warehouse.
Ability to model a table in dbt or learn to do so quickly.
Ability to work directly with sellers and marketers, understand their needs, and deliver solutions they will use.
Comfort working at a fast tempo, shipping incrementally, measuring outcomes, and communicating plainly about results and failures.
An AI-native working approach demonstrated through a workflow, automation, or agent that the candidate built and still uses; candidates should expect to demonstrate it during interviews.
Ability to work embedded in another team, follow its priorities, and uphold the engineering standards of the candidate’s own team.

Nice To Haves

Experience with Salesforce, Apollo, Smartlead, Lemlist, or similar go-to-market tools and their APIs.
Experience with email deliverability at volume.
Experience with paid-media platform APIs.

Responsibilities

Take ownership of the production go-to-market workflows, including outbound orchestration and sequences, the campaign builder, intent-signal routine, enrichment and signal pipelines, and paid-media loop.
Make workflows reliable, understand their operation and costs, and respond effectively when they break.
Build review steps and agents to check contact lists, validate signal freshness, review copy, apply holdback gates, and obtain sampled human sign-off for outreach to contacts who have not previously replied.
Instrument outreach, run A/B tests on messaging variants, and feed replies and meetings booked back into contact selection and messaging decisions.
Maintain pipelines that bring third-party signals and first-party behavioral data into Snowflake, and work with data engineers to expose Growth-required fields through MCP tools.
Build and maintain Claude skills, campaign tooling, and morning routines for Growth Associates and account executives, and conduct short working sessions to help new teams adopt them.
Maintain shared contact rules, warm-inbox discipline, and suppression windows to support email deliverability.
Apply engineering standards across owned workflows, including version control, alerting, tracing, runbooks, and documented handover; move workflows onto the Firn factory framework over time.
Maintain a standing backlog with the Director of Growth, prioritize work according to pipeline needs, and provide a concise weekly account of what shipped and its impact.
Collaborate with data engineers on scoring models and the semantic layer, and with the platform engineer on the framework used by workflows.
Translate Growth team requests into useful deliverables, identify underlying process problems, and communicate when an approach is not working.