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Machine Vision Software Engineer Jobs in Springfield, IL

TSI, Inc.

Electronics Test Engineer II

Auburn, IL · On-site

TSI's vision to create a better world by helping to protect people, products and the environment is ... Design and develop software applications and user interfaces using C#, .NET, Visual Studio, or ...

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All JobsMachine Vision Software Engineer JobsMachine Vision Software Engineer Jobs in Springfield, IL

Machine Vision Software Engineer information

See Springfield, IL salary details

$62.9K

$146.2K

$203.7K

How much do machine vision software engineer jobs pay per year?

As of Aug 11, 2026, the average yearly pay for machine vision software engineer in Springfield, IL is $146,212.00, according to ZipRecruiter salary data. Most workers in this role earn between $118,900.00 and $171,500.00 per year, depending on experience, location, and employer.

What does a machine vision software engineer do?

A Machine Vision Software Engineer designs, develops, and maintains software systems that enable computers to interpret and process visual information from the real world. They work with cameras, sensors, and advanced algorithms to automate tasks such as inspection, identification, measurement, and guidance in industrial and robotics applications. Their responsibilities often include integrating hardware with software, optimizing image processing algorithms, and ensuring the accuracy and reliability of vision systems. These engineers play a crucial role in industries like manufacturing, automotive, healthcare, and logistics where automated visual inspection and analysis are essential.

What are some of the main challenges machine vision software engineers face when integrating vision systems into manufacturing environments?

Machine Vision Software Engineers often encounter challenges such as ensuring reliable image capture despite varying lighting conditions and accommodating different types of defects or product variations. Integrating vision systems with existing automation hardware and production lines can require close collaboration with mechanical, electrical, and process engineers. Additionally, optimizing algorithms for real-time performance while maintaining high accuracy is a frequent necessity. Continuous testing and iterative development are key to addressing these challenges and delivering robust solutions in dynamic manufacturing settings.

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

To thrive as a Machine Vision Software Engineer, you need a solid background in computer science, image processing, and mathematics, usually backed by a relevant degree. Familiarity with programming languages such as Python or C++, machine vision libraries like OpenCV, and experience with deep learning frameworks are typically required. Strong problem-solving, attention to detail, and effective communication skills help engineers design robust solutions and collaborate with multidisciplinary teams. These competencies are crucial for developing accurate, efficient vision systems that meet real-world automation and quality control demands.

What is the difference between Machine Vision Software Engineer vs Computer Vision Engineer?

AspectMachine Vision Software EngineerComputer Vision Engineer
Required CredentialsBachelor's or Master's in CS, EE, or related; experience with image processingBachelor's or Master's in CS, EE, or related; strong programming skills in Python, C++
Work EnvironmentManufacturing, robotics, quality inspectionAutonomous vehicles, AI research, multimedia applications
Industry UsageManufacturing, industrial automation, roboticsTech, automotive, research institutions
Search & Comparison IntentFocus on industrial and automation applicationsFocus on AI, perception, and multimedia systems

While both roles involve image analysis and programming skills, Machine Vision Software Engineers primarily work on industrial automation and manufacturing systems, whereas Computer Vision Engineers focus on AI-driven perception in autonomous vehicles, robotics, and multimedia applications. The roles overlap in skills but differ in application environments and industry focus.

What are popular job titles related to Machine Vision Software Engineer jobs in Springfield, IL? For Machine Vision Software Engineer jobs in Springfield, IL, the most frequently searched job titles are:
What cities near Springfield, IL are hiring for Machine Vision Software Engineer jobs? Cities near Springfield, IL with the most Machine Vision Software Engineer job openings:
Machine Vision Software Engineer jobs near you
Infographic showing various Machine Vision Software Engineer job openings in Springfield, IL as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 16% Part Time, and 5% Contract. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution, with an average salary of $146,212 per year, or $70.3 per hour.

Senior Software Engineer Applied AI

Advanced Monitored Caregiving Inc.

Springfield, IL • On-site

$150 - $210/hr

Other

Posted 28 days ago

Job description

Senior Software Engineer: Applied AI (Voice Agents & ML Systems) The pitch

We build and operate production AI voice agents that hold real phone conversations in a regulated healthcare setting, plus the machine learning and LLM pipelines around them. This is one seat that spans four disciplines that rarely come together: real‑time systems, LLM engineering, traditional machine learning, and serious cloud infrastructure, all in production, all with real consequences. If you are the kind of engineer who gets restless doing one thing, this role is the opposite problem.

What you’ll work across
  • Streaming, low‑latency speech‑to‑speech systems built on modern LLMs
  • Telephony and real‑time media (call control, live audio streaming)
  • Audio handling and the quirks of real human conversation (interruptions, timing, noise)
  • Concurrency on a latency‑sensitive path, where p99 matters and a stall is something a caller hears
  • Wrapping nondeterministic models in deterministic control so they behave reliably in production
  • Multi‑model pipelines, prompt design, and cost/latency budgeting
  • Evaluation harnesses, including LLM‑as‑judge and automated agent‑tests‑agent approaches
  • Agentic tooling that gives AI systems safe, structured access to infrastructure
Traditional (non‑LLM) machine learning
  • End‑to‑end ML pipelines: feature engineering, model training, and scheduled inference
  • Imbalanced, messy real‑world data; calibration and explainability for non‑technical consumers
  • Turning research notebooks into reproducible, auditable production pipelines
Cloud and infrastructure
  • Infrastructure as code across multiple environments (we run on AWS)
  • Managed compute, data, streaming, and orchestration services
  • Security engineering in a regulated setting: encryption, least‑privilege access, strict data‑handling discipline
  • Observability and telemetry‑driven debugging, tracing a production issue from a metric anomaly to root cause
Plus

Occasional full‑stack work on internal tools, and an engineering workflow that leans heavily on AI coding assistants, with human accountability for every change.

What you’ll actually do
  • Ship and debug code on a live, real‑time voice pipeline where latency and correctness are user‑facing
  • Design control systems around LLMs: guardrails, budgets, watchdogs, safe fallbacks
  • Build and operate LLM evaluation and batch‑analysis pipelines
  • Own traditional ML workflows from data to scheduled production inference
  • Trace production issues from a metric anomaly to root cause, including building the evidence when the cause is a vendor
Must‑haves
  • 7+ years building and operating production backend systems, with strong general‑purpose programming skills (we work primarily in Python)
  • Experience running distributed systems in the cloud; comfortable debugging from telemetry to root cause
  • Hands‑on production experience with LLMs or generative AI (any provider or framework), plus the judgment to know when not to use a model
  • Working fluency across the traditional machine learning lifecycle (you productionize; you do not need to publish)
  • Disciplined in a regulated environment: small, reviewable changes and careful handling of sensitive data
Nice‑to‑haves
  • Real‑time media or telephony experience
  • Front‑end / full‑stack ability
  • ML pipeline experience, vector search, or embeddings
  • Fluency with AI coding assistants (our workflows assume them, with human accountability for every change)
How we work

Smallest correct change wins. Every behavior change is validated against the live system. Evidence over opinion in debugging. Code review is rigorous. Safety and privacy gate everything.

This role is open only to US citizens and lawful permanent residents (Green Card holders). We cannot consider candidates who require visa sponsorship now or in the future, and we are unable to make exceptions of any kind.

How to apply
  • Your LinkedIn profile URL
  • A phone number where we can reach you

A resume is welcome but optional; the two items above are required.

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