Founded in 2021 with the ambition to make cities cleaner and quieter, Electra is accelerating the shift to electric mobility by tackling the main barrier: charging.
We are building and operating a network of fast-charging stations (20 minutes on average) with a seamless and intuitive user experience.
Based in Paris, Lyon, Bordeaux, Nantes, Brussels, Madrid, Zurich, Vienna, Munich, Milan and Amsterdam, our teams aim to deploy 15,000 fast-charging points by 2030, contributing to Europe’s energy transition.
Deployed +700 stations and 2,000+ fast-charging points
Opened offices in several European countries
Raised over €1B from leading investors and institutions
Joined French Tech Next40 and won European Scale-up of the Year 2024
Grown to a team of 250+ talented people… and we’re just getting started!
Electra is deploying thousands of fast-charging points across Europe, and ML & GenAI increasingly power the products behind them. Example ML projects currently in production:
Demand forecasting (for new and existing stations)
GenAI assistants (ElectraDrive, our custom route planner)
And many more to come (station recommendation, fraud detection, dynamic pricing etc). Today these run as isolated prototypes, with no shared infrastructure.
Your mission is to get our ML & GenAI use cases into production fast, and keep them performing there. You'll collaborate with our Data Scientist early on and model development and take the modelling and engineering work the rest of the way to a robust, shipped product. The goal is breadth and speed: many use cases live and reliable, supported by just enough shared tooling to make shipping repeatable.
Take models from prototype to production. Collaborate with the Data Scientist early on, then own the modelling that makes it production-ready: feature engineering, tuning, training pipelines, and performance optimization.
Own model performance in production: monitor drift, quality, and prediction-vs-actuals (Evidently or similar), set up automated retraining, and iterate on the models themselves when they degrade.
Build the evaluation framework that gates every model rollout, especially for our GenAI use cases (example: ElectraDrive).
Define a standard, repeatable path from prototype to production for batch and event-driven inference: the deployment contract (standard packaging + model registry) integrating with Dagster (batch) and Temporal (event-driven, backend-owned).
Set up just enough shared tooling to keep modelling consistent and repeatable: a feature store (dbt/Snowflake) for training/serving consistency and a model registry with versioning and lineage (MLFlow or similar).
Contribute to the parts of our data-platform infrastructure directly tied to ML workloads: primarily orchestration (Dagster) and, where it supports ML data needs, the lakehouse, alongside the Analytics Engineers and SRE team.
sorted by priority (high to low)
Strong applied-ML / modelling background: training, evaluating, and optimizing production ML models, with solid ML fundamentals.
Strong software engineering in Python, with production MLOps experience: taking models to production, deployment, monitoring, retraining, model registries.
Comfortable across the data stack: SQL, dbt, Snowflake (or equivalent).
You operate production systems and care about reliability, reproducibility, testing, CI/CD, and observability.
Evaluating GenAI / LLM systems (eval sets, guardrails).
Integrating ML into event-driven / workflow systems (Temporal or similar).
Data-infrastructure exposure: orchestration (Dagster or similar), cloud infra (AWS, EKS), infrastructure-as-code.
Lakehouse data engineering (S3 / Iceberg)
sorted by priority (high to low)
Bias for shipping. You'd rather have a simple model live and delivering value than a perfect one stuck in a notebook. You ship, measure, and iterate.
Versatile and autonomous. Comfortable taking a model from an early prototype through to production on your own, then moving on to the next use case.
Breadth over depth, by choice. Energised by covering many use cases rather than perfecting a single one. You context-switch well across problems and domains.
Pragmatic about tooling. You build just enough infrastructure to make the next model easier to ship, never a platform for its own sake.
Ownership beyond the model. You care what happens after deployment: reliability, monitoring, and the actual business outcome, not just offline metrics.
Collaborative. You partner closely with the Data Scientist, Analytics Engineers, SRE, and backend teams, and pick things up wherever they need to move.
A key role in a massive international rollout of Europe’s largest fast-charging network
Opportunities to grow across engineering, operations, and strategy
A collaborative and mission-driven environment
Real impact on the energy transition and the future of mobility
An attractive compensation package with fixed and variable components (evaluated and paid quarterly)
Brand-new offices in the 2nd arrondissement of Paris
2 days of remote work per week
Alan health insurance (50% covered by Electra)
Meal vouchers via Swile card (€10/day, 50% covered by Electra)
25 paid vacation days + 12 RTT (reduction of working time) days
SYNTEC collective agreement (including vacation bonus, additional leave for special occasions, etc.)
A quarterly company-wide event with the entire Electra team
Sustainable Mobility Package
Access to the company benefits platform (CSE) with numerous perks (culture, sports, etc.)
Access to a platform dedicated to mental health, supporting your well-being at work and beyond, including up to 4 sessions per year with certified practitioners, covered by Electra
Even if your profile doesn't match 100% of every criterion, we encourage you to apply. At Electra, we strive to create an environment where everyone feels comfortable, safe, and free to be themselves.
We are committed to equal opportunities and do not tolerate any discrimination based on religion, cultural background, nationality, gender, sexual orientation, age, or disability.
We value potential, motivation, and a learning mindset just as much as experience.
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