Machine Learning Ops Engineer

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)