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PAR Technology
PAR Technology
1.001 – 5.000 Angajați
Alimente și băuturiConsultanțăLogistică
PAR Technology dezvoltă tehnologii pentru restaurante, companii din sectorul ospitalității și alte medii de servicii cu volum ridicat de activitate. Platforma sa reunește sisteme de puncte de vânzare, comenzi digitale, plăți și instrumente de fidelizare a clienților, ajutând operatorii să coordoneze fluxurile de lucru din zona destinată clienților și din cea operațională. PAR Technology deservește organizații din domeniul restaurantelor cu servire rapidă și al restaurantelor fast-casual, precum și cinematografe, cazinouri, parcuri tematice, hoteluri și magazine de proximitate, oferind produse cloud concepute pentru operațiuni mai fluide și o experiență mai consecventă pentru oaspeți.
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Locurile de muncă ale companiei

MLOps Engineer, Identity Resolution

Bring Bridg’s identity-resolution models into production for PAR Technology’s restaurant software platform. Build AWS and Spark pipelines, CI/CD, monitoring, and agent-queryable data interfaces.

USStatele Unite ale AmericiiCOColoradoFLFloridaILIllinoisMNMinnesotaNYNew YorkNCCarolina de NordPAPennsylvaniaTXTexas
125.000 $ – 160.000 $ / an
RemoteNormă întreagăJuniorNivel mediuLimbă Obligatorie: englezăInginer de învățare automată
AirflowAmazon RedshiftAWSGraphQLPandasPySparkPythonScikit-LearnSpark

Descriere

  • Move Bridg’s identity-resolution model from notebooks into monitored, automated production.
  • Build and maintain reliable, scalable data and machine learning pipelines.
  • Write tests and add observability for match data.
  • Optimize Spark jobs and build CI/CD workflows.
  • Strengthen system testing and documentation.
  • Work with data science to deliver production machine learning systems.
  • Conduct exploratory data analysis and investigate data quality.
  • Maintain feature pipelines and consistency between model training and serving.
  • Evaluate champion and challenger models and flag anomalous results.
  • Create lightweight interfaces that let agent workflows query identity-resolution outputs.
  • Use AI for coding, refactoring, infrastructure as code, design documentation, log and run summaries, dataset exploration, and evaluation-case generation.
  • Report to Bridg’s Director of Product.

Cerințe

  • 2–4 years of experience building production data or machine learning systems.
  • Bachelor’s degree in computer science, data engineering, or a related field, or equivalent practical experience.
  • Experience building and maintaining automated, tested machine learning or data pipelines.
  • Experience improving CI/CD for data or machine learning workflows, including Git-based processes, automated unit and integration tests, and branch-based promotion through development, staging, and production.
  • Experience with workflow orchestration tools such as Airflow, Step Functions, or Databricks Workflows, including retries, backfills, SLA monitoring, and alerting.
  • Familiarity with experiment tracking and model registries such as MLflow.
  • Exposure to monitoring and observability, data-quality and schema-drift checks, and input and output distribution drift.
  • Experience with infrastructure as code, secrets management, and reproducible Python environments.
  • Production experience with AWS services including S3, EMR, Glue, Step Functions, Lambda, IAM, and CloudWatch.
  • Working knowledge of Spark performance topics including skew, shuffle, spill, partitioning, and join strategies.
  • Experience serving processed outputs to downstream systems such as Redshift.
  • Ability to conduct exploratory data analysis and data-quality investigations.
  • Experience building and maintaining feature pipelines and keeping training and serving consistent.
  • Foundation in applied statistics, including probability, hypothesis testing, sampling, and evaluation metrics.
  • Fluency with the Python data science stack, including pandas, scikit-learn, and PySpark.
  • Interest in LLM-based agent data integration and willingness to build interfaces such as MCP servers, REST or GraphQL endpoints, or structured feeds.
  • Good judgment when working with data contracts.
  • Comfort working with datasets that contain personally identifiable information.
  • Daily use of AI coding assistants, with accountability for reviewing and testing AI-generated code.
  • Must be legally authorized to work in the United States or Canada.
  • Must not have a restrictive covenant agreement that prevents employment at PAR.

Beneficii

  • Eligibility for a bonus.
  • Potential additional compensation, including commission and/or equity, as applicable.
  • Remote work arrangement in eligible locations.
  • Reasonable accommodations for individuals with disabilities.

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