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PAR Technology
PAR Technology
1001 – 5000 Zaposleni
Hrana in pijačaLogistikaSvetovanje
PAR Technology razvija tehnologijo za restavracije, gostinska podjetja in druga okolja z velikim obsegom storitev. Njena platforma združuje sisteme prodajnih mest, digitalno naročanje, plačila in orodja za zvestobo strank, da upravljavcem pomaga usklajevati procese v strežbi in zaledju. PAR Technology sodeluje z organizacijami na področju hitre in sproščene prehrane, kinematografov, igralnic, tematskih parkov, hotelov in trgovin z izdelki za vsakodnevno uporabo. Njeni izdelki v oblaku so zasnovani za učinkovitejše delovanje in bolj enotno izkušnjo gostov.
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Delovna mesta v podjetju

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.

USZdružene države AmerikeCOKoloradoFLFloridaILIllinoisMNMinnesotaNYNew YorkNCSeverna KarolinaPAPensilvanijaTXTeksas
125.000 $ – 160.000 $ / leto
Na daljavoPolni delovni časJuniorSrednja ravenZahtevani Jezik: angleščinaInženir strojnega učenja
AirflowAmazon RedshiftAWSGraphQLPandasPySparkPythonScikit-LearnSpark

Opis

  • 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.

Zahteve

  • 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.

Ugodnosti

  • 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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