Data Forward Deployed Engineer – Applied Data, ML Engineering

🔥 0 minutes ago

🇺🇸 United States – Remote

💵 $180k - $233k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 20%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.Why this score:

• Just posted.
• Jobs at this company usually fill in 43 days.
• 29 open roles for a team of 11.

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Tessera Labs

WebsiteLinkedInAll Job Openings

11 - 50 employees

🤖 Artificial Intelligence

🏢 Enterprise

☁️ SaaS

Artificial Intelligence • Enterprise • SaaS

Tessera Labs is a vendor-agnostic, multi-agent AI systems integration platform that gives enterprises a single, unified view across their IT architecture. Pre-trained on thousands of enterprise landscapes and hundreds of years of business process expertise, Tessera adapts to each customer environment to harmonize systems and data and to enable secure, governed execution across ERP and other critical systems. The company emphasizes enterprise-grade governance and controls for regulated industries, accelerating time-to-value while evolving with new data and workflows, and is built on published AI research and patented technology.

📋 Description

• Embed with client teams to scope data and ML problems, map source systems and deliver working outcomes in weeks • Own delivery quality, including validation, reconciliation to source and stakeholder sign-off • Turn repeated client patterns into reusable Tessera platform components • Design and develop scalable ETL/ELT pipelines using SQL, Python and dbt • Ingest and transform data from ERP, CRM, HCM and finance systems • Design canonical enterprise data models and cross-system mapping rules • Implement validation, reconciliation and monitoring to resolve duplicate, inconsistent and legacy data • Engineer features and build models for use cases such as forecasting and anomaly detection • Deploy ML models to production and monitor them • Collaborate with client teams, SMEs and Tessera product engineers • Translate complex business logic into data pipelines, canonical data models and applied ML solutions • Feed proven patterns back into the Tessera platform

🎯 Requirements

• 5+ years of experience in Data Engineering, ML Engineering or Applied Data Science • Enterprise data harmonization, migration or analytics experience across ERP, CRM or finance systems • Experience in client-facing, consulting or forward-deployed roles • Advanced SQL • Strong Python for data pipelines and ML workflows • Experience building ETL/ELT pipelines and processing large-scale enterprise datasets • Relational and dimensional data modeling • dbt for transformation, testing and documentation • Cloud platform experience with AWS, GCP or Azure • At least one ML model built, deployed and monitored in production on business data • Feature engineering from structured and time-series enterprise data • Model evaluation and monitoring • Working knowledge of enterprise data domains and business processes, including finance, supply chain and master data • Ability to understand complex enterprise systems and turn business rules into data and ML solutions across platforms • Comfortable working directly with client engineers, functional consultants and business stakeholders • Strong analytical skills for diagnosing enterprise data and model performance challenges • Ability to own complex problems and deliver production-grade solutions that scale across enterprise environments • Preferred: hands-on experience with Snowflake or Databricks • Preferred: distributed processing with Spark, PySpark, DuckDB or similar • Must be authorized to work in the U.S.; application asks whether immigration sponsorship is required

🏖️ Benefits

• Build at the frontier of applied AI • Work on a multi-agent AI platform automating complex enterprise workflows • Shape the Tessera platform through reusable pipelines, canonical models, mapping rules and ML components • Field input goes directly to product and R&D teams • Work across several enterprise customers and landscapes • Trusted with decisions and ownership of customer outcomes

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