Machine Learning Ops Engineer

For over four decades, PAR Technology Corporation (NYSE: PAR) has been a leader in restaurant technology, empowering brands worldwide to create lasting connections with their guests. Our innovative solutions and commitment to excellence provide comprehensive software and hardware that enable seamless experiences and drive growth for over 100,000 restaurants in more than 110 countries. Embracing our "Better Together" ethos, we offer Unified Customer Experience solutions, combining point-of-sale, digital ordering, loyalty and back-office software solutions as well as industry-leading hardware and drive-thru offerings. To learn more, visit partech.com or connect with us on LinkedIn , X (formerly Twitter) , Facebook , and Instagram . Position Description: As an ML Ops Engineer at Bridg, you will be the engineering partner to a data science team that has built Bridg’s next-generation identity resolution model. Your focus will be to help take their work from notebooks to monitored, automated production — building and maintaining the pipelines, tests, and observability that keep our match data reliable at scale. You will spend most of your week writing code: building pipelines, tuning Spark jobs, wiring up CI/CD, and leaving the system better tested and better documented than you found it. We are a growing SaaS organization building cloud-native, data-intensive applications on AWS and Snowflake, using Spark, Python, MLflow, and related tooling. The ideal candidate is curious, collaborative, and comfortable working in a fast-moving environment with a strong sense of ownership and accountability. Position Location: Remote - New York, Texas, Philadelphia, Florida, North Carolina, Minnesota, Colorado, Georgia, Illinois Reports To: Director of Product, Bridg Who You Are: 2–4 years of experience building production data or ML systems, with a Bachelor’s degree in Computer Science, Data Engineering, or a related field (or equivalent practical experience) A highly motivated, curious, and capable learner with a strong sense of ownership and accountability A strong communicator and collaborator who works well across cross-functional teams Comfortable in a fast-moving environment — able to shift priorities, navigate ambiguity, and support a customer-facing system What We’re Looking For: MLOps & Platform Engineering Experience building and maintaining automated, tested ML or data pipelines in partnership with other teams (you don’t need to be a from-scratch model builder) Experience optimizing CI/CD for data/ML: Git-based workflows, automated unit and integration tests (pytest or similar), and branch-based promotion through dev/stage/prod Workflow orchestration with Airflow, Step Functions, or Databricks Workflows, including retries, backfills, and basic SLA monitoring and alerting Familiarity with experiment tracking and model registries (MLflow or equivalent) for lineage and stage transitions Exposure to monitoring and observability — data-quality and schema-drift checks (such as Great Expectations, Deequ, or Datadog) and awareness of input/output distribution drift Infrastructure as code (Terraform preferred), secrets management, and reproducible Python environments (Docker, Poetry/uv/Conda) Cloud & Distributed Compute Production experience on AWS (S3, EMR, Glue, Step Functions, Lambda, IAM, CloudWatch) Working knowledge of Spark performance — comfortable diagnosing and addressing common issues like skew, shuffle, and spill, with sensible partitioning and join strategies Experience serving processed outputs to downstream systems (e.g., Redshift for BI, activation, or attribution) Data Science Partnership A strong technical partner to data science (you’re not the lead data scientist on IDR — that’s covered) Able to run EDA and data-quality investigations: profiling datasets, surfacing anomalies, and communicating findings clearly Experience building and maintaining feature pipelines and keeping training and serving consistent A solid applied-stats foundation (probability, hypothesis testing, sampling, evaluation metrics) — enough to support champion/challenger evaluation and flag when something looks off Comfortable reading ML code you didn’t write and asking good questions; fluent across the Python DS stack (pandas, scikit-learn, PySpark) AI Data Integration Interest in how LLM-based agents consume structured data — tool definitions, retrieval patterns, and pre-computed signals vs. raw data Willingness to build lightweight interfaces that make IDR outputs queryable by agent workflows (MCP servers, REST/GraphQL endpoints, or structured feeds) Good judgment about data contracts — what to expose, at what granularity, and how to keep it maintainable Experience with agentic frameworks (LangChain, LlamaIndex, or similar) is a plus; genuine interest is the baseline Additional Skills / Nice to Have: Exposure to PII-based record linkage, probabilistic matching methodology, or graph/clustering-based entity resolution (e.g., connected components) Awareness of blocking strategies and the recall/runtime tradeoffs they drive at large record volumes Comfort working carefully with PII-bearing datasets (our internal processes cover the privacy and compliance side) Cost-optimization wins on EMR, AWS, Databricks, or equivalent Dev environment AI in Your Workflow: Beginning in FY2026, all roles at PAR Technology are expected to incorporate AI into day-to-day responsibilities. For this role, the expectation is that you will adapt AI to help drive efficiencies in your workflow, including but not limited to: Daily, fluent use of AI coding assistants (Claude Code, Cursor, Copilot, or equivalent) as a core part of your workflow Good judgment about when to delegate to AI vs. write by hand — scaffolding tests, drafting refactors, generating boilerplate and IaC, and explaining unfamiliar code, while writing the tricky parts yourself Holding AI-generated code to the same review bar as your own: you read it, test it, and are accountable for it Using AI to accelerate non-coding work too — drafting design docs, summarizing logs and runs, exploring datasets, and generating eval cases Interview Process: Interview #1: Phone Screen with Talent Acquisition Team Interview #2: Video interview with the Hiring Manager (via MS Teams) Interview #3: Video interviews with the Team (via MS Teams) PAR is proud to provide equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. We also provide reasonable accommodations to individuals with disabilities in accordance with applicable laws. If you require reasonable accommodation to complete a job application, pre-employment testing, a job interview or to otherwise participate in the hiring process, or for your role at PAR, please contact accommodations@partech.com . If you’d like more information about your EEO rights as an applicant, please visit the US Department of Labor's website.