Title
Feature Engineer
Quick Summary
HelioFrame AI is hiring a Feature Engineer to design, validate, and serve high-signal features that power ranking, personalization, and forecasting. You will build trustworthy offline datasets, ensure point-in-time correctness, and deliver low-latency online features through a feature store with strict SLAs. The role suits strong graduates and early-career engineers who enjoy turning messy data into measurable model lift.
Project Category or Industry
Applied machine learning for consumer and B2B SaaS.
Type
Full-time employment.
Experience Level
Entry to mid-level with structured mentorship; experienced candidates are welcome.
Duration
Permanent role.
Location
Remote-first with optional hybrid hubs in Amsterdam and Toronto. Maintain at least four hours of overlap with teams operating between UTC−5 and UTC+2.
Salary
USD 92,000–138,000 base depending on location and experience, plus benefits and an annual performance bonus.
Payment Mode
Monthly payroll where supported; compliant contractor arrangements available in select countries.
Hiring Company Name
HelioFrame AI
Required Skills or Tools
Strong SQL and Python, comfort with distributed data processing, practical experience with feature stores, and a solid grasp of data quality, backfills, and monitoring. Ability to translate product goals into defensible features with clear documentation and tests.
Project Description
HelioFrame AI builds the data and serving layers that make machine learning useful in production. As a Feature Engineer, you will partner with product, data, and platform teams to discover predictive signals, formalize them into reusable feature definitions, and serve them consistently across offline training and online inference. You will measure feature impact through offline metrics and online experiments while keeping latency, freshness, and cost within targets.
Core Responsibilities and Expected Deliverables
Design, implement, and document reusable feature definitions with clear ownership, lineage, and data contracts.
Ensure online/offline parity and strict point-in-time correctness; implement backfills and replay workflows.
Build batch and streaming pipelines for feature computation with SLAs for freshness, accuracy, and availability.
Establish validation and anomaly detection for drift, null spikes, distribution shifts, and leakage.
Collaborate with modelers to prioritize features, generate ablations, and quantify lift; support launches with guardrails and dashboards.
Deliver production-grade code, tests, deployment manifests, and concise runbooks; participate in on-call as needed.
Required Experience and Preferred Qualifications
Proficiency in SQL and Python with sound engineering practices (testing, code review, CI/CD).
Hands-on experience with a warehouse or lakehouse (Snowflake, BigQuery, or Redshift; Delta Lake/Iceberg/Hudi a plus) and distributed processing (Spark or Flink).
Familiarity with feature stores and online serving patterns, including TTL, deduplication, and late-arriving data handling.
Preferred: experience with real-time streams (Kafka/Kinesis), metrics layers, experiment platforms, and cost/performance tuning in cloud environments.
Evidence of impact via internships, open-source contributions, coursework, or shipped ML features will be valued.
Tools or Platforms to Be Used
Feature management: Feast or Tecton (or equivalent), metrics/semantic layer where applicable.
Data processing and storage: Spark or Flink; S3 or GCS; Delta Lake/Iceberg/Hudi; Snowflake or BigQuery.
Streaming and messaging: Kafka with Schema Registry or Kinesis.
Orchestration and CI/CD: Airflow or Dagster; GitHub Actions.
Observability and quality: Great Expectations or Soda, Prometheus, Grafana, OpenLineage/Marquez, DataHub.
Language Requirement
Professional English is required. Additional languages are welcome for cross-regional collaboration.
Communication Style
Written-first culture using design docs and pull requests on GitHub, Slack for daily coordination, and Zoom for stand-ups, design reviews, and experiment readouts. Clear, actionable documentation is expected for all changes.
Time Commitment or Working Window
Standard 40 hours per week with flexible scheduling. Maintain a predictable daily block that overlaps at least four hours with the core team between 09:00 and 17:00 in your local time.
Payment Terms
Salary paid monthly via payroll. For contractors, invoices are processed on net-30 terms upon acceptance of deliverables and timesheets.
Evaluation Criteria
Portfolio or code samples demonstrating feature design, point-in-time correctness, and measurable model lift.
Practical exercise implementing a feature pipeline with validation, backfill, and an A/B test plan.
Technical interview covering streaming semantics, online/offline parity, drift detection, and cost governance.
Final conversation on collaboration, product sense, and communication.
References may be requested.
Other Requirements
New hires sign a confidentiality agreement and comply with security and data-handling policies. Light time-tracking may be used for distributed coordination. Occasional on-call for feature serving incidents is shared across the team.
About HelioFrame AI
HelioFrame AI is a privately held software company that helps product teams deploy reliable machine learning in production. Headquartered in Amsterdam with a distributed team across Europe and North America, we combine rigorous data engineering with pragmatic MLOps to deliver features that move core business metrics. Learn more at https://www.helioframe.ai and reach our hiring team at [email protected].