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Temporal Jobs in Texas (NOW HIRING)

ML Engineer

Plano, TX · On-site

$110K - $132K/yr

Advanced No-SQL (MongoDB) and SQL (Postgres) and data modeling for slowly changing dimensions / temporal mappings. * Experience building production-grade ML pipelines and data pipelines. * Solid ...

Advanced No-SQL (MongoDB) and SQL (Postgres) and data modeling for slowly changing dimensions / temporal mappings. Experience building production-grade ML pipelines, and data pipelines. Solid Python ...

Extensive Platform Engineering experience, including Platform Provisioning [i.e., Request Process Management, Provisioning Automation for Temporal (QA, research) and Permanent (prod, non-prod ...

Extensive Platform Engineering experience, including Platform Provisioning [i.e., Request Process Management, Provisioning Automation for Temporal (QA, research) and Permanent (prod, non-prod ...

Senior Perception Hardware Engineer

Austin, TX · On-site

$109K - $146K/yr

Temporal Synchronization: Work with electrical and embedded teams to validate hardware-level timing. Ensure sub-microsecond synchronization across all perception streams using PTP (Precision Time ...

Drive infrastructure decisions for scalable AI workloads: vector databases (Turbopuffer, pgvector, Milvus), workflow orchestration (Temporal, Airflow), and async compute patterns * Design and govern ...

Drive infrastructure decisions for scalable AI workloads: vector databases (Turbopuffer, pgvector, Milvus), workflow orchestration (Temporal, Airflow), and async compute patterns * Design and govern ...

Drive infrastructure decisions for scalable AI workloads: vector databases (Turbopuffer, pgvector, Milvus), workflow orchestration (Temporal, Airflow), and async compute patterns * Design and govern ...

Showing results 21-40

Temporal information

See Texas salary details

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How much do temporal jobs pay per hour?

As of Sep 14, 2026, the average hourly pay for temporal in Texas is $12.45, according to ZipRecruiter salary data. Most workers in this role earn between $8.94 and $14.57 per hour, depending on experience, location, and employer.

What is a Temporal engineer?

Temporal engineers are software professionals who specialize in using the Temporal platform to build, run, and scale reliable distributed applications. Temporal is an open-source workflow orchestration engine that helps developers manage complex workflows, handle failures, and ensure the consistency of long-running business processes. Temporal engineers design, implement, and maintain workflows that require coordination between multiple services, often in cloud-native environments. They use Temporal’s APIs and SDKs to build resilient applications that can recover from errors and interruptions automatically.

How does a Temporal engineer typically collaborate with cross-functional teams to deliver workflow solutions?

As a Temporal Engineer, collaboration with cross-functional teams is central to designing, implementing, and maintaining robust workflow solutions. You’ll frequently work alongside product managers, software engineers, and infrastructure teams to understand requirements and integrate Temporal's orchestration platform into broader system architectures. Regular meetings, code reviews, and design sessions are common, ensuring alignment on workflow logic and system reliability. This collaborative environment helps streamline communication, surface potential challenges early, and deliver scalable, dependable solutions.

What are the key skills and qualifications needed to thrive as a Temporal engineer, and why are they important?

To thrive as a Temporal Engineer, you need strong proficiency in distributed systems, software development (especially in Go or Java), and experience with workflow orchestration, ideally with a degree in computer science or a related field. Familiarity with Temporal's platform, cloud-native tools (like Kubernetes and Docker), and CI/CD systems is typically required. Excellent problem-solving, teamwork, and communication skills set top candidates apart in this role. These competencies ensure the reliable design, deployment, and operation of scalable workflow solutions, which are critical for modern software infrastructure.

What is the difference between Temporal vs Data Engineer?

AspectTemporalData Engineer
Required CredentialsTechnical knowledge of workflow orchestration, programming skillsDatabase, programming, and data pipeline skills
Work EnvironmentSoftware development, cloud-based systemsData processing, analytics, cloud platforms
Industry UsageWorkflow automation, microservices orchestrationData management, analytics, big data
Search & Comparison IntentUnderstanding workflow orchestration toolsData pipeline and infrastructure roles

Temporal is a workflow orchestration platform focused on managing complex application workflows, while Data Engineers build and maintain data pipelines and infrastructure for data processing. Both roles require technical skills but serve different purposes within software and data ecosystems.

Is Temporal a good company to work for?

Temporal is a technology company known for its open-source workflow orchestration platform. Employees often cite a collaborative environment, opportunities for growth, and a focus on innovation, though experiences can vary by role and team. As with any company, researching specific teams and roles can provide more insight into the work environment.

What job categories do people searching Temporal jobs in Texas look for?

The top searched job categories for Temporal jobs in Texas are:

What cities in Texas are hiring for Temporal jobs?

Cities in Texas with the most Temporal job openings:

Infographic showing various Temporal job openings in Texas as of September 2026, with employment types broken down into 78% Full Time, and 22% Temporary. Highlights an 80% In-person, and 20% Remote job distribution, with an average salary of $25,905 per year, or $12.5 per hour.

ML Engineer

Plano, TX • On-site

Promantus, Inc
11 - 50 employees

$110K - $132K/yr

Other

Re-posted 2 days ago


Job description

This role is ML Engineering with hands-on software engineering skills using Python, PySpark, and Databricks. 

  • Strong Python, Databricks, and PySpark experience with Spark, Kafka, Snowflake, MongoDB, PostgreSQL, Redis, Azure cloud.
  • Advanced No-SQL (MongoDB) and SQL (Postgres) and data modeling for slowly changing dimensions / temporal mappings.
  • Experience building production-grade ML pipelines and data pipelines.
  • Solid Python development skills with API development (FastAPI) and familiarity with ML workflows.
  • Delta Lake, MLflow, and workflow orchestration experience.
  • Agentic development and GenAI experience will be a big plus
  • Familiarity with ELK (Elastic Kibana)

Please note that DE with GenAI knowledge will not be shortlisted; the candidate must have classical ML/DS exposure with production pipeline design and development experience.