ML/Data Engineer

Intetics Inc.; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Intetics Inc. is seeking a highly skilled ML/Data Engineer to join our team on a full-time basis. The role focuses on building scalable data infrastructure for high-volume sports telemetry, with emphasis on real-time ingestion and ML evaluation.

You will design time-series storage, graph data models, and tenant-isolation strategies, collaborating with data scientists and domain experts to deliver reliable production systems.

Qualifications

• 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.

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.

Skills

Time-series data

Python

SQL

Go

Kafka

Kinesis

Flink

Spark Streaming

Graph data models

Multi-tenant systems

Data provenance

ML evaluation infrastructure

AWS

GraphQL

Tools

InfluxDB

TimescaleDB

Prometheus

ClickHouse

Job description

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 experiencedML/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.

• 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.