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Senior Embedded Machine Learning Jobs in Gary, IN

AppFolio

Sr. Machine Learning Engineer

Chicago, IL

$107K - $147K/yr

Who We Are Looking For We're hiring a Senior Machine Learning Engineer to design and ship the next generation of voice and conversational AI agents within Realm-X. This role helps define AppFolio ...

Cccis

Senior Manager - Machine Learning

Chicago, IL · On-site

The Role We are seeking a Senior Manager to lead our Machine Learning (ML) team in the subrogation business. This role is critical to expanding the emerging subrogation market for CCC and as such is ...

The Hartford

Senior AI Machine Learning Engineer

Chicago, IL · On-site

$126K - $166K/yr

Sr Data Engineer - GE07BE We're determined to make a difference and are proud to be an insurance ... The Hartfordis seeking aSenior AI Machine Learning Engineerwithin Employee Benefits Applied AI and ...

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Senior Embedded Machine Learning information

See Gary, IN salary details

$75.1K

$144.1K

$192.6K

How much do senior embedded machine learning jobs pay per year?

As of Aug 18, 2026, the average yearly pay for senior embedded machine learning in Gary, IN is $144,063.00, according to ZipRecruiter salary data. Most workers in this role earn between $123,400.00 and $161,700.00 per year, depending on experience, location, and employer.

What does a senior embedded machine learning engineer do?

A Senior Embedded Machine Learning engineer designs, develops, and optimizes machine learning models to run efficiently on resource-constrained embedded devices such as microcontrollers, IoT devices, and edge hardware. They are responsible for integrating ML algorithms with embedded systems, ensuring low latency and minimal power consumption. Their work often involves collaborating with hardware engineers and software developers to deploy intelligent features in products like smart sensors, wearables, and autonomous systems.

What are the key skills and qualifications needed to thrive as a senior embedded machine learning engineer?

To thrive as a Senior Embedded Machine Learning Engineer, you need expertise in embedded systems, machine learning algorithms, and programming languages like C/C++ and Python, often backed by an advanced degree in computer science or electrical engineering. Familiarity with tools such as TensorFlow Lite, ONNX, and embedded hardware platforms (e.g., ARM Cortex-M, NVIDIA Jetson) is typically required. Strong problem-solving, project management, and communication skills distinguish top performers in this role. These capabilities are crucial for efficiently deploying optimized machine learning models on resource-constrained devices and effectively collaborating across multidisciplinary teams.

What are some common challenges faced by senior embedded machine learning engineers when deploying models on edge devices?

Senior Embedded Machine Learning Engineers often encounter challenges such as optimizing model size and inference speed to fit within the limited computational resources and memory of edge devices. Balancing accuracy and performance while minimizing power consumption is critical, especially for battery-operated products. Additionally, integrating models with existing embedded software and ensuring reliable, real-time operation can require close collaboration with hardware and firmware teams. Staying current with advancements in model compression and hardware acceleration is also essential for success in this role.

What is the difference between Senior Embedded Machine Learning vs Embedded Software Engineer?

AspectSenior Embedded Machine LearningEmbedded Software Engineer
Required CredentialsBachelor's/Master's in CS, EE, or related; experience in ML and embedded systemsBachelor's in CS, EE, or related; strong programming skills in C/C++
Work EnvironmentDeveloping ML models for embedded devices, hardware integrationDesigning and implementing embedded software for devices
Industry UsageAI/ML-focused companies, IoT, consumer electronicsAutomotive, industrial, consumer electronics

While both roles involve embedded systems, Senior Embedded Machine Learning focuses on integrating ML models into hardware, requiring knowledge of AI and data science. Embedded Software Engineers primarily develop software for embedded devices, emphasizing firmware and system-level programming. The roles overlap in embedded environment skills but differ in their core focus on AI versus traditional software development.

What are the most commonly searched types of Embedded Machine Learning jobs in Gary, IN?

The most popular types of Embedded Machine Learning jobs in Gary, IN are:

What cities near Gary, IN are hiring for Senior Embedded Machine Learning jobs?

Cities near Gary, IN with the most Senior Embedded Machine Learning job openings:

Senior Embedded Machine Learning jobs near you

Sr. Machine Learning Engineer

AppFolio

Chicago, IL

$107K - $147K/yr

Full-time

Re-posted 12 days ago

AppFolio rating

7.2

Company rating: 7.2 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

185th of 245 rated software companies

Job description

Hi, We're AppFolio
We're innovators, changemakers, and collaborators. We're more than just a software company — we're building the AI-native platform where the real estate industry comes to do business. We're transforming Property Management; how property managers operate, how residents live, and how intelligence flows across an entire industry.
Realm-X is AppFolio's AI-native platform powering this transformation. It enables a new generation of intelligent capabilities across our products, including Realm-X Assistant (copilot), Flows (AI Agentic workflows) and Performers (autonomous AI Agents). Realm-X serves as both a foundation for internal teams to build and scale AI-powered products, and a core layer delivering intelligent, high-impact experiences directly to our customers.
At its core, Realm-X is built on a structured domain ontology and a set of shared business primitives—such as transactions, actions, reports, metrics, and skills—that enable AI systems to deeply understand and operate across the full context of property management workflows. This foundation allows us to build context-aware, action-oriented AI systems that go beyond simple assistance to power real automation and decision-making.
Who We Are Looking For
We're hiring a Senior Machine Learning Engineer to design and ship the next generation of voice and conversational AI agents within Realm-X. This role helps define AppFolio's production voice and chat agent pipelines, working at the intersection of LLM agent frameworks, real-time voice technology, and streaming infrastructure.
You will work with Product, Voice channel, and ML Platform teams to translate cutting-edge agent and voice research into reliable, low-latency, multi-channel experiences that scale across our entire customer base.
Your Impact
  • Ship Voice & Text Agents: Architect and ship voice and text agent pipelines that handle real-time, multi-turn customer interactions.
  • Reasoning vs. Latency: Make principled trade-offs between reasoning depth and latency across frontier LLMs, smaller models, and routing strategies.
  • Lead a Pod: Lead a small pod of ML and platform engineers; raise the bar on agent evaluation, observability, and incident response.
  • Define Quality: Partner with Product and Voice channel teams to define KPIs, eval harnesses, and acceptance criteria for agent quality.
  • Optimize for Voice: Drive selective Small Language Model (SLM) fine-tuning and inference optimization for voice latency and cost.
Qualifications
  • You have shipped production AI agents serving real users in voice and/or text channels.
  • You think in pipelines and systems, not just models.
  • You move fast, deliver impact, and maintain sound engineering judgment.
  • You are humble, collaborative, and low-ego, and you elevate those around you.
  • You value work-life balance as a foundation for sustained high performance.
Must Have
  • Agent frameworks: Deep, shipped experience with LangChain, LangGraph, LangSmith, and LangChain Deep Agents (or equivalent agent frameworks).
  • Voice stack: Hands-on with Voice-to-Voice models and traditional TTS / STT pipelines; understands the trade-offs between end-to-end voice models and modular STT → LLM → TTS architectures.
  • LLM fluency: Strong grasp of LLM reasoning behavior, tool use, structured output, and reasoning-vs-latency trade-offs across providers.
  • Telephony & cloud: Production experience with Twilio (or comparable telephony) and AWS.
  • Engineering: Expert Python, async programming, and WebSockets for real-time, bidirectional streaming.
  • ML fundamentals: Solid foundation in deep learning, model evaluation, and inference optimization; able to deploy with Docker on AWS.
  • Leadership: Demonstrated ability to lead a small team, mentor engineers, and partner credibly with Product and Design.
Nice to Have
  • Experience fine-tuning Small Language Models for domain-specific voice applications.
  • Familiarity with RAG over structured business data and tool-using agents over API surfaces.
  • Prior experience in regulated or customer-facing industries with strict reliability requirements.
  • Publicly verifiable work on GitHub, in open-source agent frameworks, or in community competitions.
Location
Find out more about our locations by visiting our site. 
All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process.
Compensation & Benefits
The compensation that we reasonably expect to pay for this role is: 167,200 - 209,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate’s skills, education, experience, and internal equity.
Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type.
Regular full-time employees are eligible for benefits - see here.

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