Engineering Manager, Data Platform & Machine Learning

About Middesk

Middesk makes it easier for businesses to work together. Since 2018, we’ve been transforming business identity verification, replacing slow, manual processes with seamless access to complete, up-to-date data. Our platform helps companies across industries confidently verify business identities, onboard customers faster, and reduce risk at every stage of the customer lifecycle.

Middesk came out of Y Combinator, is backed by Sequoia Capital and Accel Partners, and was recently named to Forbes Fintech 50 List and cited as an industry leader in business verification by digital identity strategy firm, Liminal.

The Role:

We’re seeking an experienced Engineering Manager to lead Middesk’s data platform and machine learning infrastructure team. This team powers both our core identity platform and new AI-driven product experiences, sitting at the heart of our product foundation and growth strategy.

As we scale to hundreds of authoritative and alternative data sources, you’ll lead efforts to reimagine data acquisition and entity resolution with AI. We see massive opportunities to leverage AI agents to automate ETL development, monitoring, and schema adaptation—dramatically reducing the time and effort required to onboard and maintain new data pipelines. You’ll also drive the use of LLMs and AI-driven graph techniques to significantly improve the accuracy and efficiency of our business entity resolution process, a critical component of Middesk’s platform.

You’ll manage a team of data and ML engineers, balancing hands-on technical leadership with team scaling. You’ll work closely with Product, Design, and Go-to-Market teams to expand data coverage, accelerate ML model development, and operationalize AI/ML across the company.

We follow a hybrid work model, and for this role, there is an expectation of 2 days per week in our SF office. Candidates should be based within a commutable distance, as we believe in the value of in-person collaboration and building strong team connections while also supporting flexibility where possible.

What You’ll Do:

Team Leadership & Management

• Manage and grow a team of data and ML engineers; set goals, provide feedback, and build a culture of technical excellence.

• Partner with Recruiting to hire top talent as the team scales.

• Align engineering priorities with business goals, with near-term focus on scaling data acquisition, building ML infrastructure, and improving pipeline reliability.

Data Acquisition & Engineering

• Scale Middesk’s data acquisition framework—automate evaluation of new sources, build ingestion and ETL pipelines, and integrate into entity resolution.

• Partner with Product and Data Science to launch new data products and expand our business identity dataset.

• Improve reliability, observability, and cost-efficiency across the platform (e.g., storage, ETL, Elastic Search).

Machine Learning & AI Platform

• Build and scale ML infrastructure, including training pipelines, model serving, feature stores, and MLOps practices.

• Partner with Data Science to accelerate experimentation and deployment across fraud, risk, and identity use cases.

• Drive adoption of AI agents and LLM workflows to automate ingestion, orchestration, and AI-powered customer applications.

Cross-Functional Impact

• Collaborate with Product, GTM, and Data Science to shape the roadmap for ML- and data-driven initiatives.

• Champion AI/ML adoption across the company, setting technical direction and best practices.

• Partner with customers and stakeholders to ensure our data and AI infrastructure is reliable, trustworthy, and high-performing.

What We’re Looking For:

• 2+ years managing data engineering and/or ML engineering teams; 7+ years of overall data engineering and/or ML engineering experiences.

• Strong technical background in data infrastructure and distributed systems, with hands-on knowledge in SQL, Python, Spark, Airflow, dbt, or similar.

• Experience building and scaling ML infrastructure (model training pipelines, feature stores, model serving, monitoring).

• Proven track record of delivering reliable data platforms and ML systems in production.

• Ability to set technical direction, manage stakeholders, and operate with a balance of hands-on involvement and delegation.

• Strong communicator who can influence both technical and non-technical audiences.

Nice to Haves:

• Experience in fintech, fraud/risk, compliance, or B2B SaaS.

• Previous experience scaling data acquisition frameworks or integrating third-party data sources.

• Familiarity with graph database solutions, graph feature engineering, and AI agent services and deployment in production.

• Experiences with entity resolution processes.

• Startup experience building teams and systems from 0→1 and scaling early-stage infrastructure.

• Background collaborating with go-to-market and customer-facing teams on technical products.