ML/Data Engineer
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1h ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 17h ago
The date the source published, not the day we noticed it (2026-10-16). Last seen at its source just now.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Romania, Bulgaria, Portugal, Poland, Slovakia, Spain
The description agrees: it names United States, Europe.
What the ad says
…location: USA…
…locations: EU ONLY…
Pay not stated
Similar roles pay €79.5k–99.5k/yr
Middle 50% of 11 listings that do state pay — Engineering · all levels · Bulgaria · EUR/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
Workable employer's own board first seen 1h ago · last seen just now
The listing
Intetics Inc., a global technology company providing custom software application development, distributed professional teams, software product quality assessment, and “all-things-digital” solutions, is seeking a highly skilled and experienced ML/Data Engineer to join our dynamic team on a full-time basis.
Customer location: USA
Candidate locations: EU ONLY
Working hours: 11.00-20.00 CET
Language: English B2+
Estimated start date: 16.11.2026
Duration: 12 months
Project Responsibilities
- Design and build production data infrastructure for high-volume athlete performance and sensor data.
- Build and operate real-time and near-real-time ingestion systems for streaming data.
- Design storage and data architectures for high-volume time-series data and longitudinal athlete records.
- Build infrastructure that makes production features and derived metrics available to machine learning systems and AI agents with low latency.
- Design and implement graph data models and schemas representing relationships between athletes, training loads, injuries, performance, and outcomes.
- Build data and ML evaluation infrastructure that helps measure model reliability, calibration, and performance across real-world cases.
- Design systems that maintain strong tenant-level data isolation across clubs and customers.
- Establish appropriate data provenance, lineage, auditability, and observability across the platform.
- Work with ML and AI engineers to provide reliable data foundations for model training, inference, and evaluation.
- Work with sport scientists and domain experts to translate complex requirements into durable production systems.
- Make pragmatic technology and architecture decisions as the platform evolves.
Requirements
- 5+ years of full-time professional software or data engineering experience, excluding internships, university placements, coursework, and academic projects.
- Proven experience designing, building, and operating production data infrastructure at scale.
- Strong experience working with time-series data or time-series databases, such as InfluxDB, TimescaleDB, Prometheus, ClickHouse, or equivalent technologies.
- Significant experience with real-time or streaming data ingestion, using technologies such as Kafka, Kinesis, Flink, Spark Streaming, Pulsar, or equivalent.
- Experience designing graph data models or graph database schemas, not simply querying or consuming an existing graph database.
- Experience designing or operating multi-tenant systems with tenant-level data isolation.
- Strong Python and SQL skills. Professional experience with Go is highly desirable.
- Experience working with production systems where reliability, scalability, observability, and data correctness matter.
- Ability to take ownership of ambiguous technical problems and turn them into practical production architectures.
- Experience working directly with data scientists, ML engineers, or other technical domain specialists.
- Experience building probabilistic evaluation, model calibration, or model monitoring infrastructure.
- Experience with causal inference, counterfactual modelling, or simulation.
- Experience working with wearable sensors, IoT data, biomechanics, sports technology, or other high-frequency telemetry.
- Experience building knowledge graphs or domain-specific ontologies.
- Experience with LLM or AI evaluation frameworks and an understanding of their limitations.
- Experience with AWS, including ECS, EC2, Lambda, SNS, SQS, or related services.
- Experience with GraphQL, REST, gRPC, Postgres, MongoDB, or similar technologies.