Jobs at Fusemachines

Lead design, build, and support of scalable data pipelines and integrations using Microsoft Fabric, ADF, PySpark, SQL, ADLS Gen2, and Databricks Unity Catalog. Drive Azure-to-Fabric migration, stabilize pipelines, tune performance, manage data governance, troubleshoot issues, document designs, mentor team members, and collaborate with stakeholders in Life Sciences regulatory environments during US Eastern hours.

Design, build, and harden Android VPN functionality: implement VpnService/TUN-based tunnels, integrate VPN protocols (WireGuard/OpenVPN/IKEv2), optimize native libraries via NDK/JNI, ensure security/privacy, prevent leaks, and support release and Play Console workflows. Deliver stable, low-battery-impact connections and production-grade deployments.

Design, build, and maintain scalable AWS-based data enrichment pipelines (Spark) and orchestrations (Airflow); use S3/Glue/Athena for storage, processing and querying; perform deep data quality analysis, implement validation/observability, troubleshoot tracking and delivery issues across hybrid systems, and collaborate on governance, lineage, and schema design to support media measurement and audience intelligence.

Design, build, and maintain AWS-based data enrichment pipelines (Spark/PySpark). Manage S3/Glue/Athena data platform, orchestrate with Airflow, perform deep data quality analysis, troubleshoot tracking and delivery issues, implement observability, and collaborate with global teams on data governance and pipeline optimization.

The Machine Learning Engineer role involves owning ML projects end-to-end, designing production ML systems, and leveraging AI tools to automate decision-making. Responsibilities include building robust pipelines, communicating with stakeholders, and mentoring team members, especially for Senior candidates.

Build and operate production-grade ML services and large-scale feature pipelines (Databricks/Spark, Snowflake, Azure). Own model lifecycle (MLflow, CI/CD, retraining, monitoring), activation/integration to DSPs/CDPs/clean rooms, and privacy-by-design patterns. Mentor engineers and set engineering standards.

Build and validate statistical and ML models to create, expand, and score audience segments from multi-source data; perform data fusion, propensity/lookalike modeling, measurement (reach, overlap, lift), and partner with ML Engineering to productionize privacy-aware models.

Convert data‑science prototypes into production ML services; build and operate Databricks/Spark pipelines reading/writing Snowflake; manage full model lifecycle (MLflow, CI/CD, retraining, drift monitoring); deliver/version audience segments to ad‑tech partners; ensure scalability, cost efficiency, privacy, and mentor engineers.

Build and validate statistical and ML models to create, expand, and score audience segments from survey, purchase, and media data. Perform data fusion, propensity/lookalike modeling, audience measurement (reach, overlap, lift), and partner with ML engineering to productionize privacy-safe models. Propose product improvements and collaborate with US product and analytics teams.

Administer and maintain RHEL and OpenShift clusters on bare-metal, manage node lifecycle and storage, apply patches/upgrades, monitor cluster and hardware health, support IBM Watson/Cloud Pak deployments, coordinate vendor and colo support, implement backup/DR, security hardening, incident response, and produce documentation and runbooks.

Administer and maintain RHEL and OpenShift clusters on bare-metal, perform patching/upgrades, manage node lifecycles and storage (Ceph/ODF, NFS, SAN), monitor cluster and hardware health (Prometheus/Grafana, iDRAC), coordinate colocation/vendor support (Dell, IBM), support IBM Cloud Pak/Watson deployments, implement backups/DR, apply security hardening, troubleshoot incidents, and document runbooks and procedures.

Administer and maintain RHEL and OpenShift clusters on bare-metal, manage node lifecycle and hardware (Dell), apply patches/upgrades, monitor cluster and hardware health, support IBM Watson/Cloud Pak deployments, manage storage and backups, implement security hardening, coordinate vendor and colocation support, document runbooks and perform incident response.

Lead data analysis, BI, and visualization efforts: collect, clean, model, analyze, and visualize large datasets. Build dashboards and reports, apply statistical methods and data mining, ensure data quality/governance, collaborate cross-functionally, and mentor junior analysts while implementing best practices for analytics and reporting.

The Lead Data Scientist will design and implement data-driven solutions, lead a team of data scientists, and collaborate across functions, while managing machine learning pipelines and mentoring junior staff.

Lead data analytics work: collect, clean, model, analyze, and visualize large datasets; build dashboards and reports (SnowSight, Power BI); design data models and implement Snowflake/dbt pipelines; ensure data governance and quality; mentor junior analysts and partner with cross-functional teams to deliver insights.

As a Lead Data Scientist, you will lead a team to develop AI solutions, collaborate with cross-functional teams, design predictive models, and ensure data quality in machine learning pipelines.

Lead collection, cleaning, modeling, analysis, and visualization of large datasets to deliver BI and advanced-analytics insights. Build and maintain Snowflake data pipelines, dbt transformations, dashboards (SnowSight/Power BI), ensure data governance and quality, mentor junior analysts, and collaborate with cross-functional teams to support data-driven decisions.

The Lead Data Scientist designs and implements data-driven solutions, leads a team, collaborates with cross-functional teams, and mentors junior scientists while staying updated with industry advancements.

Lead Data Scientist responsible for designing data-driven solutions, leading a team, and collaborating with cross-functional teams to utilize AI for business improvement.

Build and maintain forecasting, driver-based models, and analytics for FP&A. Analyze historical and operational drivers, improve forecast accuracy with statistical and ML methods, create dashboards and automated reports, integrate ERP/CRM data, perform variance and scenario analysis, and present insights to finance leaders.