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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)
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