Sr. Software Engineer

HERE Technologies•Chicago, IL

27d•$130,000 - $140,000

About The Position

We are hiring a Senior Software Engineer to build AI platform and production systems that support advanced computer vision, perception, simulation, and mapping products. This person will own the pipelines, orchestration, APIs, deployment patterns, and operational tooling that turn technically ambitious ideas into durable internal and production systems. This is a senior role for someone who can build practical infrastructure today while laying foundations that remain useful across multiple AI-driven products over time. What You Will Own: - Own critical parts of the AI platform and orchestration stack - Build artifact handoff, workflow control, and service interfaces across research and production systems - Manage metadata, storage, run tracking, reproducibility, deployment, and operational reliability across the workflow - Create internal and product-facing interfaces used to launch, monitor, and retrieve AI jobs and outputs - Keep systems simple, reliable, observable, scalable, and cost-aware from prototype through production What You Will Do: - Build command-line tools, batch jobs, internal services, and APIs for AI pipeline execution - Define packaging and storage conventions for model inputs, outputs, metadata, and logs - Set up cloud-based inference and data-processing execution and evolve it toward production deployment patterns - Implement job status tracking, error handling, observability, security, and operational controls - Partner with data scientists and simulation engineers to reduce iteration friction across the team - Help design production architecture for scaling usage, managing cost, and serving downstream consumers

Requirements

Strong software engineering background in backend systems, data pipelines, ML platforms, distributed workflows, or production services
Experience with Python services, cloud infrastructure, APIs, and artifact-oriented pipelines
Familiarity with AWS compute, storage, batch-processing, and deployment patterns
Good systems judgment: able to build the minimum durable platform without overengineering while still planning for production realities
Comfortable operating in a small team where speed and clarity matter

Nice To Haves

Experience supporting GPU inference workloads, large-model deployment pipelines, perception platforms, or media-generation systems
Familiarity with experiment tracking, dataset versioning, MLOps workflows, or production observability practices
Experience with internal APIs, asynchronous job systems, security controls, or media-processing pipelines

Responsibilities

Own critical parts of the AI platform and orchestration stack
Build artifact handoff, workflow control, and service interfaces across research and production systems
Manage metadata, storage, run tracking, reproducibility, deployment, and operational reliability across the workflow
Create internal and product-facing interfaces used to launch, monitor, and retrieve AI jobs and outputs
Keep systems simple, reliable, observable, scalable, and cost-aware from prototype through production
Build command-line tools, batch jobs, internal services, and APIs for AI pipeline execution
Define packaging and storage conventions for model inputs, outputs, metadata, and logs
Set up cloud-based inference and data-processing execution and evolve it toward production deployment patterns
Implement job status tracking, error handling, observability, security, and operational controls
Partner with data scientists and simulation engineers to reduce iteration friction across the team
Help design production architecture for scaling usage, managing cost, and serving downstream consumers