Asks for

The ad says“Cloud platform experience: practical experience with GCP (our primary environment — Vertex AI, BigQuery, Dataflow) and AWS, both of which are in use across our platform.”
The ad says“Data fluency: strong working knowledge of data processing tools — BigQuery (SQL), pandas, Dataflow, or equivalents — and the ability to reason about data quality as a first-class concern.”
The ad says“ML engineering in production: demonstrated experience owning the full Data Science lifecycle — data, training, evaluation, deployment, serving, and ongoing operations — on production systems, not just prototypes. This includes monitoring for data drift, training/serving skew, and model degradation; the production discipline, not just the modelling.”
The ad says“Data fluency: strong working knowledge of data processing tools — BigQuery (SQL), pandas, Dataflow, or equivalents — and the ability to reason about data quality as a first-class concern.”
The ad says“ML fundamentals: fluency across supervised and unsupervised learning, ranking models, recommendation systems, evaluation metrics, overfitting, and model selection.”
The ad says“Cloud platform experience: practical experience with GCP (our primary environment — Vertex AI, BigQuery, Dataflow) and AWS, both of which are in use across our platform.”
The ad says“ML engineering in production: demonstrated experience owning the full Data Science lifecycle — data, training, evaluation, deployment, serving, and ongoing operations — on production systems, not just prototypes. This includes monitoring for data drift, training/serving skew, and model degradation; the production discipline, not just the modelling.”
The ad says“ML fundamentals: fluency across supervised and unsupervised learning, ranking models, recommendation systems, evaluation metrics, overfitting, and model selection.”
The ad says“ML engineering in production: demonstrated experience owning the full Data Science lifecycle — data, training, evaluation, deployment, serving, and ongoing operations — on production systems, not just prototypes. This includes monitoring for data drift, training/serving skew, and model degradation; the production discipline, not just the modelling.”
The ad says“ML engineering in production: demonstrated experience owning the full Data Science lifecycle — data, training, evaluation, deployment, serving, and ongoing operations — on production systems, not just prototypes. This includes monitoring for data drift, training/serving skew, and model degradation; the production discipline, not just the modelling.”
The ad says“ML engineering in production: demonstrated experience owning the full Data Science lifecycle — data, training, evaluation, deployment, serving, and ongoing operations — on production systems, not just prototypes. This includes monitoring for data drift, training/serving skew, and model degradation; the production discipline, not just the modelling.”
The ad says“ML fundamentals: fluency across supervised and unsupervised learning, ranking models, recommendation systems, evaluation metrics, overfitting, and model selection.”
The ad says“Data fluency: strong working knowledge of data processing tools — BigQuery (SQL), pandas, Dataflow, or equivalents — and the ability to reason about data quality as a first-class concern.”
The ad says“Python: strong production-grade Python; comfortable in a codebase where your models and services are consumed by other engineers.”
The ad says“ML fundamentals: fluency across supervised and unsupervised learning, ranking models, recommendation systems, evaluation metrics, overfitting, and model selection.”
The ad says“ML fundamentals: fluency across supervised and unsupervised learning, ranking models, recommendation systems, evaluation metrics, overfitting, and model selection.”
The ad says“Data fluency: strong working knowledge of data processing tools — BigQuery (SQL), pandas, Dataflow, or equivalents — and the ability to reason about data quality as a first-class concern.”
The ad says“ML fundamentals: fluency across supervised and unsupervised learning, ranking models, recommendation systems, evaluation metrics, overfitting, and model selection.”
The ad says“Technical leadership: experience leading the technical design of ML projects, including articulating trade-offs and driving decisions within a team.”
The ad says“ML fundamentals: fluency across supervised and unsupervised learning, ranking models, recommendation systems, evaluation metrics, overfitting, and model selection.”
The ad says“Cloud platform experience: practical experience with GCP (our primary environment — Vertex AI, BigQuery, Dataflow) and AWS, both of which are in use across our platform.”

About Us

We are Ocado Group, and we're bringing world-class automation to online grocery. Our Ocado Smart Platform (OSP) combines cutting-edge robotics, AI, and IoT within our advanced CFCs (Customer Fulfilment Centres). We've mastered the single pick, transforming online delivery for our global partners. Join us and be part of a team pushing the boundaries of retail technology.

About the team

Smarties is the Data Science and intelligent personalisation team within the Ecommerce stream at Ocado Technology — the part of Ocado Smart Platform (OSP) that powers the end-customer shopping experience. We build the prediction, ranking, and recommendation capabilities that power smart features across OSP and that product teams integrate into customer-facing journeys.

We are a cross-craft team combining data scientists, software engineers, and ML engineers — a mix of skills that lets us work across the full stack of a smart feature, from data and modelling to production systems.

Our mission is to make smart feature development scalable and self-serve. We turn the hard parts of "smarts" — prediction, ranking, personalisation — into reusable building blocks, proven on our own products first, so that over time any product team can build intelligent features using our platform and our support.

We own and operate a broad portfolio of Data Science systems in production: checkout walk personalisation, order prediction and recommendations, customer intelligence, search and browse ranking, recipe discovery, and the web analytics data pipeline that underpins measurement across Ecommerce. We are in an active phase of evolution — consolidating shared capabilities, reducing complexity, and opening space for new initiatives — while progressively shifting toward a platform model where product teams build on shared Data Science capabilities.

Underpinning all of this is data quality. Smart features are only as good as the data behind them. We treat reliable, well-understood data as a foundation, not a side task — and we actively contribute to raising that standard across Ecommerce.

Ecommerce is also a genuinely good place to be a technical engineer right now. Practically all the way up the reporting line you will find people with strong technical backgrounds who share a vision for what modern software development looks like — including how AI tools and coding assistants are changing what high-quality engineering means in practice. That perspective shapes how we work, what we invest in, and how we develop people.

Job Purpose

A Senior ML Engineer in Smarties brings the engineering perspective to a cross-craft team: raising the bar on how ML systems are built, not just how they perform. This means championing industry best practices across the full ML lifecycle — from experimentation and model design through to serving, monitoring, and cost management — and translating that expertise into systems that are reliable, testable, scalable, and maintainable in production.

The role carries real ownership: of the systems, of the decisions behind them, and of their long-term health. We expect someone who tracks how the field is moving, knows when to adopt and when to hold, and actively shapes how the team builds — not just what it builds.

Key responsibilities:

Influence - Be an individual with positive impact

Actively teach, coach, and create opportunities for others to share and develop expertise — both within the team and across the wider organisation.
Act as a technical leader and consultant on ML within the team's domain, raising the quality bar on how ML problems are defined and solved.
Work across craft boundaries: collaborate effectively with data scientists and software engineers, helping the team function as one cross-craft unit rather than parallel silos.
Promote realistic commitments at the team level; openly share trade-offs, uncertainties, and technical risks.
Represent ML thinking in conversations with product managers, data analysts, and consuming engineering teams.

Execution and Delivery - Get value out of the door

Take ownership of the full ML lifecycle — from problem framing and data exploration through training, evaluation, deployment, and monitoring (MLOps) — on production systems that directly affect revenue and customer experience.
Make key design decisions on ML/AI components within our portfolio, weighing technical, cost, and product implications explicitly.
Break work into incremental, reversible steps that deliver value early and reduce risk.
Handle production incidents and degradations effectively, diagnosing root causes and resolving them at the right speed.
Deliver with a high degree of autonomy; contribute to technical direction within the team.
Contribute to both the ongoing health of production systems and the emergence of reusable platform building blocks — extracting from real problems, not building speculatively.

Insights - Solve real problems

Analyse production ML systems, identify failure modes, and drive improvements grounded in data.
Design and implement well-defined experiments to validate hypotheses, from offline evaluation to live AB tests.
Seek the root of the problem being solved; translate business requirements into correctly scoped ML problems.
Proactively surface data-driven insights to technical and non-technical audiences.
Contribute to raising data quality standards across the team's pipelines — treating reliable, well-understood data as a foundation for all ML work.

Strategy - Keep an eye toward the future

Apply sufficient domain knowledge to make good design decisions across the team's ML portfolio.
Identify opportunities to simplify the live system — reducing support burden, compressing costs, and building leverage (for example, replacing several bespoke models with a single configurable engine).
Apply appropriate testing and de-risking principles, especially when working on systems with direct business impact.
Investigate and resolve production issues, raising awareness where these stretch beyond the team.
Collaborate with the Engineering Manager and Data Science leads on the technical direction of the platform.
Understand consuming teams' needs well enough to design building blocks that serve them without creating new dependencies.

Knowledge, skills and experience:

Required:

ML engineering in production: demonstrated experience owning the full Data Science lifecycle — data, training, evaluation, deployment, serving, and ongoing operations — on production systems, not just prototypes. This includes monitoring for data drift, training/serving skew, and model degradation; the production discipline, not just the modelling.
Data fluency: strong working knowledge of data processing tools — BigQuery (SQL), pandas, Dataflow, or equivalents — and the ability to reason about data quality as a first-class concern.
ML fundamentals: fluency across supervised and unsupervised learning, ranking models, recommendation systems, evaluation metrics, overfitting, and model selection.
Cloud platform experience: practical experience with GCP (our primary environment — Vertex AI, BigQuery, Dataflow) and AWS, both of which are in use across our platform.
Python: strong production-grade Python; comfortable in a codebase where your models and services are consumed by other engineers.
Technical leadership: experience leading the technical design of ML projects, including articulating trade-offs and driving decisions within a team.

Valued:

Experience with LLM-based systems (RAG, structured generation, evaluation, cost management), particularly on GCP/Vertex AI or similar.
Experience building or contributing to shared ML infrastructure or internal platforms (feature stores, serving layers, shared pipelines).
Familiarity with recommendation systems, personalisation, or search ranking at scale.
Experience working in a cross-craft team alongside software engineers and data scientists.
Understanding of AB testing methodology and experimentation platforms.
Familiarity with Java — our model serving layer is built on Java services.

What we are looking for beyond the CV

Smarties is a small team carrying a large portfolio. The ideal candidate is someone who:

Takes ownership seriously — not just of the model, but of the system, the data, and the outcome.
Thinks in leverage — prefers a solution that improves ten products at once over ten separate solutions.
Is honest about uncertainty — raises problems early, shares trade-offs openly, and does not treat "keeping the lights on" as someone else's job.
Works well across disciplines — can design with a data scientist, review a service with a software engineer, and explain an ML decision to a product manager.
Cares about data quality — understands that smart features are only as good as the data behind them, and acts accordingly.

This role description summarises the typical functions of the role. It is not an exhaustive list of all possible responsibilities, tasks, or duties.

BENEFITS: WHAT’S IN IT FOR YOU?

Work and life should fit together, so we offer a range of benefits focusing on well-being, development, and team spirit. The final package will depend on the contract type we agree on.

Standard benefits: life insurance, private health care [Luxmed], Multisport card, lunch vouchers, company share programs, and assistance for everyday worries and serious health problems.
Learning opportunities: access to the Learnebly platform and LinkedIn Learning, English classes, and a book library,
Parental support: additional +10 days maternity / +20 days paternity leave, funding for nurseries and kindergartens
Hybrid model: 2 days/week in the office and flexibility to work from almost any location for up to 30 days a year.
Office perks: centrally located offices with car and cycling parking, and home office equipment provided.
Career growth: a clear career path with opportunities to rotate between projects, teams, domains and roles under the guidance of highly skilled senior colleagues
High engineering culture: unique software engineering culture with a high level of test coverage and agile environment [read about our tech stack and engineering
practices across Ocado Technology]
Speaker community: public speaking training and support for preparing presentations at conferences or meetups, including our own Ocado Technology Meetups
Passions groups: running, cycling and more
Annual celebrations: carnival, summer parties, family picnics, or kid’s days.

At Ocado Technology, we're always exploring, learning, and implementing new initiatives, and we're eager to share stories, insights, and experiences with you. Meet our team members during meetups [Watch recordings here]

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Gross Monthly Pay range

Poland Monthly Pay Range

23 600 zł—29 900 zł PLN

Asks for

Values