IN
InPostHead of ML & MLOps Engineering - Fintech Engineering
LocationWarszawa, Województwo mazowieckie, pl
Work moderemote · Poland
Typecontract
SeniorityDirector
Experience7+ yrs
Company size10,000+ people
PostedOct 9, 2026 · 3d ago
Verified live23h ago
At a glanceSummarised by Seekless from the posting.
7+ years in ML
3+ years leading a team
Hands-on experience with ML lifecycle: development, validation, deployment, monitoring, retraining
Experience in credit-scoring or underwriting modelling, or comparable high-stakes ML
Skilled in model-risk management and responsible-AI governance
Experience building and leading a team from zero
Fluent in English (B2+)
CCD2 and consumer-credit regulation
DORA/ICT risk
IFRS 9 implications for model outputs
Fraud-detection ML
Databricks/Spark
Skills
Databricks
Spark
Company Description
Build ML that makes money and stands up to a regulator.
We are building a scale-up inside InPost, and AI will flow through it. You would join the core Data & AI leadership team and build the ML and MLOps function from zero, with the InPost executive team backing it. Our word is extreme ownership: you own a model from first idea to its profit and loss impact.
Job Description
The mission
Build a state-of-the-art ML platform and the discipline around it.
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Models with a price tag. Every model has a business case and a measurable monetary outcome.
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Credit and decisioning models built with rigorous validation, champion/challenger testing and explainability.
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A production ML platform. Serving, monitoring, reproducibility and retraining are engineered, not improvised.
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Responsible AI, built in. Model risk, bias and explainability checks, with an independent sign-off gate before anything reaches production.
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Agent-first systems. Agents are production components with orchestration, guardrails, evals and observability.
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◆ Models on governed data. You build on a point-in-time-correct feature store, not around it.
This is a business function. Every model carries monetary value, and you will run the function that way: compute budget, headcount and return on investment.
Qualifications
What you’ll own
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The ML & MLOps team, from your first hire onward.
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Model-development standards and the validation methodology that stands up to model-risk and regulatory scrutiny.
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The ML platform behind decisioning services.
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A clear ownership line between feature production (data engineering) and model consumption, set together with the Head of Data Engineering and the Director.
You are
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A leader who loves data and loves building systems around it.
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Hands-on when needed, especially with AI on board. You understand the model, the pipeline and the serving layer.
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Experienced across the full ML lifecycle: development, validation, deployment, monitoring and retraining.
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Experienced in credit-scoring or underwriting modelling, or comparable high-stakes ML.
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Skilled in model-risk management and responsible-AI governance.
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Experienced in building and leading a team from zero.
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Fluent in English (B2+). [add years of experience: suggest 7+ years in ML, 3+ leading]
Bonus
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CCD2 and consumer-credit regulation · DORA/ICT risk · IFRS 9 implications for model outputs · fraud-detection ML · Databricks/Spark.
Additional Information
Why this one
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Seat at the table on a core leadership team.
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Build it right the first time. No legacy ML estate.
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Models that matter. Your work decides real money, not a dashboard.
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Real pace. A lean, AI-native organisation.