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Internship Software Engineer Applied Math Jobs in Mason, MI

Tech Tammina LLC

AI Engineer

Okemos, MI · On-site

AI Engineer / Applied AI Developer Okemos, MI (Hybrid): Minimum of 3 days per week onsite in Okemos. or 100% Remote: Open only to Senior-level candidates located in Eastern Time (EST). Must Have ...

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Internship Software Engineer Applied Math information

See Mason, MI salary details

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$33

How much do internship software engineer applied math jobs pay per hour?

As of Aug 23, 2026, the average hourly pay for internship software engineer applied math in Mason, MI is $22.21, according to ZipRecruiter salary data. Most workers in this role earn between $18.08 and $25.19 per hour, depending on experience, location, and employer.

What is the difference between Internship Software Engineer Applied Math vs Internship Data Scientist?

AspectInternship Software Engineer Applied MathInternship Data Scientist
Required SkillsProgramming, applied mathematics, algorithmsStatistics, programming, data analysis
Work EnvironmentTech companies, R&D labs, financeTech firms, consulting, research
Common Industry UsageSoftware development, modelingData analysis, predictive modeling

Internship Software Engineer Applied Math focuses on applying mathematical techniques to software development and algorithm design, often in tech and finance sectors. In contrast, Internship Data Scientist emphasizes analyzing data to extract insights, using statistical and machine learning methods. Both roles require programming skills, but their core focus and industry applications differ.

Internship Software Engineer Applied Math jobs near you

Senior Software Engineer Applied AI

Advanced Monitored Caregiving Inc.

Lansing, MI • On-site

$180 - $240/hr

Other

Re-posted 10 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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