Senior Applied ML Engineer, Marketing Systems

Senior Applied ML Engineer, Marketing Systems
About the role

We’re building a product that helps teams plan, launch, and improve marketing outcomes by combining automated decisioning with robust data + platform integrations . We’re looking for a Senior ML Engineer who is equally comfortable building applied ML systems and the backend infrastructure that makes those models reliable in production.

You’ll work on the full lifecycle: turning messy marketing data into usable signals, building models that make better decisions over time, and wiring everything into a platform that can execute safely, measure results, and learn continuously.

Compensation :

Founding Level Equity + Salary

What you’ll do

Applied ML / Decisioning

• Build systems that recommend and optimize marketing actions (e.g., budget allocation, pacing, creative/ad selection, audience targeting signals, channel mix).
• Develop learning loops from outcomes: experiment design, counterfactual/holdout analysis (when applicable), offline evaluation, and online monitoring.
• Implement ranking/bandits or constrained optimization approaches where they fit (ROI under guardrails, budget constraints, frequency caps, etc.).
• Work with noisy, delayed feedback signals (attribution limitations, conversion lag, partial observability).

Data + Measurement

• Design data models for campaigns, spend, conversions, events, and identity signals; unify across platforms.
• Build pipelines for ingestion, normalization, deduping, and reconciliation (spend/conversion mismatches, late-arriving data, API quirks).
• Improve measurement robustness (server-side events, event schemas, model features, privacy-aware aggregation).

Backend / Platform Engineering

• Build backend services/APIs that expose decisioning outputs and integrate with execution workflows.
• Implement orchestration primitives: queues, schedulers, state machines for “plan → launch → monitor → adjust.”
• Engineer for production realities: rate limits, retries, idempotency, backfills, observability, and SLAs.
• Create internal tooling for debugging decisions (why the system did X), data QA, and replay.

What we’re looking for

• 5–10+ years of experience shipping production systems, with meaningful time in applied ML.
• Strong coding ability and software fundamentals (you can build real services, not just notebooks).
• Experience with at least some of:
• Applied ML for optimization, ranking, forecasting, or decisioning under constraints.
• Marketing/adtech data (campaign hierarchy, spend, ROAS, CPA, conversion lag, attribution caveats).
• Production ML: feature pipelines, model training/evaluation, deployment, monitoring, model/data drift.
• Building backend systems that handle messy external APIs and high data volume.
• Comfortable working in ambiguity and iterating quickly with product/customer feedback.

Nice to have

• Hands-on experience integrating or working with major ad APIs (Meta/Google/TikTok/etc.).
• Experience with experimentation platforms, bandits, uplift modeling, or causal inference in real systems.
• Familiarity with privacy/measurement shifts (CAPI, iOS changes, consent modes).
• Experience designing guardrails for automated systems (budget caps, safety checks, “do no harm” constraints).

Example projects you might work on

• A decision engine that recommends budget shifts daily under pacing + spend constraints.
• A “next best action” system for creative/ad set selection based on recent performance + fatigue signals.
• A cross-channel performance model that normalizes platform-specific metrics into comparable signals.
• A robust ingestion + reconciliation pipeline that can backfill and explain discrepancies.
• A service that produces explainable recommendations with audit logs (“what changed, when, and why”).

Tech stack

• ML: Python, PyTorch/TF, scikit-learn, XGBoost; orchestration (Airflow/Dagster); feature store optional
• Backend: Python/Node/Go; REST/gRPC; queues (Kafka/SQS/PubSub)
• Data: Postgres + warehouse (BigQuery/Snowflake); Redis
• Infra: AWS/GCP; Docker/Kubernetes; Terraform
• Observability: Datadog/Grafana/Sentry