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Part Time Embedded Ai Jobs (NOW HIRING)

$69K - $158K/yr

As an embedded software engineer, you know how to create and maintain crucial pieces of software ... Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible ...

CNO Developer

Annapolis, MD · On-site

$86K - $198K/yr

As a computer network operations (CNO) specialist, you know that embedded and application ... Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible ...

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Part Time Embedded Ai information

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$70K

$153.4K

$174K

How much do part time embedded ai jobs pay per year?

As of Jul 11, 2026, the average yearly pay for part time embedded ai in the United States is $153,383.00, according to ZipRecruiter salary data. Most workers in this role earn between $131,500.00 and $173,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Part-Time Embedded AI Engineer, and why are they important?

To thrive as a Part-Time Embedded AI Engineer, you need a solid background in embedded systems, machine learning fundamentals, and programming languages such as C/C++ and Python, often supported by a relevant engineering or computer science degree. Familiarity with AI development frameworks (like TensorFlow Lite), real-time operating systems (RTOS), and microcontroller or FPGA platforms is typically expected. Strong problem-solving, time management, and collaboration skills help you deliver results efficiently in a flexible, part-time setting. These competencies are vital to effectively design, implement, and optimize AI solutions within the constraints of embedded hardware and limited work hours.

What are the typical responsibilities and collaboration expectations for a part-time Embedded AI engineer?

As a part-time Embedded AI engineer, you’ll typically work on integrating AI algorithms into hardware devices, often focusing on optimizing models for resource-constrained environments. You’ll collaborate closely with firmware developers, hardware engineers, and sometimes data scientists to ensure seamless deployment and real-time performance. Communication and coordination are key, as you’ll need to align your schedule with the team’s milestones and contribute efficiently within your available hours. Regular code reviews, sprint planning, and status updates are common, even in part-time roles.

What is a Part Time Embedded AI job?

A Part Time Embedded AI job involves working on artificial intelligence (AI) algorithms and systems that are integrated into hardware devices, but on a part-time basis. Professionals in this role typically design, develop, and optimize AI models to run efficiently on embedded systems such as microcontrollers, IoT devices, or edge devices. The part-time nature of the job allows for flexible hours, making it suitable for students, freelancers, or those looking to supplement their income. Key responsibilities may include programming, testing, and deploying AI models within the constraints of limited computing resources. This role requires knowledge of both AI concepts and embedded systems programming.
More about Part Time Embedded Ai jobs
What are the most commonly searched types of Embedded Ai jobs? The most popular types of Embedded Ai jobs are:
Infographic showing various Part Time Embedded Ai job openings in the United States as of July 2026, with employment types broken down into 75% Full Time, 22% Part Time, and 3% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution, with an average salary of $153,383 per year, or $73.7 per hour.

Co-Founder & CEO - AI Healthcare Revenue Recovery

FutureSight

Austin, TX • Remote

$80K/yr

Full-time, Part-time

Posted 3 days ago

New


Job description

The Opportunity

FutureSight is seeking a Co-Founder & CEO to lead Reclaim, an AI-native denial management and revenue recovery venture. This is a co-founder partnership with meaningful founder equity, not a salaried executive role.

We are entering a $16B+ Total Addressable Market with a gaping $ 7.5B–$10B whitespace, targeting the highly underserved mid-market segment. Currently, 30–40% of denied medical claims go uncontested because practices lack the staff and capital to contest them. This systemic failure leaves between $50,000 and $300,000 in legally earned revenue permanently abandoned at each practice every year.

Reclaim AI resolves denied and aged claims end-to-end, embedding outcome-based contingency pricing—meaning practices pay zero upfront, and we only take a cut of the dollars we successfully recover, for our target ideal customer profile (practices with $80K+ in annual recoverable claims).

Market Context

We didn’t just read market reports; we spent weeks interviewing billing managers, practice owners, revenue cycle leaders at health systems, and even digital operations leaders at major insurance companies. The dysfunction we uncovered is staggering, creating a massive opportunity for a disruptor:

  • The pain is so severe, practices are building their own tech: The frustration of payer opacity is so deep that medical billers are hiring part-time software engineers to build in-house robotic process automation (RPA) scrapers just to check claim statuses.
  • Massive, emotional write-offs: Staff carry immense guilt over these administrative losses. One small-practice assistant admitted, "I feel like we probably had to write off probably close to a million dollars" simply because they couldn't keep up with the manual follow-ups.
  • The "Payer-Side Inversion" Opportunity: We discovered that insurance companies are drowning in the exact same claims dysfunction they generate. One major payer we interviewed reported a backlog of 31,000 contested claims that consumed 80% of their staff's operational capacity, forcing emergency hires just to manage the disputes.
  • The Competition is Distracted: Well-funded Tier-1 competitors are charging $15K+ upfront licensing fees and fighting over enterprise health systems. The competitive window to capture the mid-market with a contingency-priced autonomous agent is wide open.
About FutureSight

FutureSight is a leading venture builder that co-creates world-class software companies with values-driven entrepreneurs from inception to exit. We are a team of founders, operators and designers with experience successfully bringing software to market at scale.

You’ll co-create with a proven studio team, led by John Carbrey (4x entrepreneur, $100M ARR), Krista LaRiviere (3x exited, E&Y Top Women Entrepreneur), Alan Smith (Strategyzer co-founder, $120M in products built), Prathna Ramesh (former MD of Maple Leaf Angels, $275M in follow-on capital), and Johnny Tong (0-to-1 builder, acquired by SAP and Stripe) bring a rare combination of operator exits, institutional investing, and AI product depth.

The Partnership
  • Founder equity with meaningful ownership from inception
  • Pre-seed capital committed by FutureSight for early hires and MVP development, with potential for follow-on funding
  • Venture building resources, including embedded design, engineering, growth, and fundraising support from day one
  • Investor and advisor network across vertical AI and early-stage capital markets
  • A true co-creation model in which you operate as CEO with FutureSight's cross-functional team as your partner
What You’ll Own

As Co-Founder & CEO, you will set the venture's direction and lead its execution.

  • Strategy — Refine the ICP, pricing model, and product positioning
  • Customer Development — Lead pilots with practice owners and revenue cycle leaders, convert them to paid engagements, and build the go-to-market motion
  • Product — Partner with the FutureSight product and engineering team to ship V1 and iterate on user feedback
  • Capital — Lead the seed raise, supported by FutureSight's network and traction
  • Team — Recruit and lead the founding team, and establish the cultural foundation of the company
Co-Founder Profile
  • Domain & Workflow Depth: You have direct exposure to healthcare revenue cycle, medical billing, or denial management. You understand the nuanced differences in denial codes, know why administrative denials (like credentialing gaps) are uniquely painful, and intuitively understand the fragmented systems (e.g., Tebra, eClinicalWorks, Availity) that billers are forced to use
  • Previous founding experience at a venture-backed company
  • Demonstrated success in B2B AI or B2B SaaS go-to-market, including sales and customer engagement
  • Fundraising fluency, with the ability to develop investor narratives and close capital
  • Proven ability to attract, develop, and retain top talent
  • Clear-eyed understanding of the risks and demands of co-founding a venture-backed company
How to Apply

Please submit your resume, LinkedIn profile, and a brief note on why this venture aligns with your goals as a founder. We will move quickly for the right candidate.

FutureSight is committed to diversity, equity, and inclusion. We welcome applicants of all backgrounds and experiences.