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Hourly Embedded Machine Learning Jobs in Haledon, NJ

Join our dynamic Investment Data Science Team as an Embedded Data Scientist to partner directly ... The ideal candidate can move seamlessly between developing machine learning models and engaging ...

AlpInvest Embedded Data Scientist

New York, NY ยท On-site

$190K - $220K/yr

Join our dynamic Investment Data Science Team as an Embedded Data Scientist to partner directly ... The ideal candidate can move seamlessly between developing machine learning models and engaging ...

Multiple factors are taken into consideration to arrive at the final hourly rate/ annual salary to be offered to the selected candidate. Factors include, but are not limited to, the scope and ...

Showing results 21-40

Hourly Embedded Machine Learning information

See Haledon, NJ salary details

$70.7K

$155K

$175.8K

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

As of Aug 19, 2026, the average yearly pay for hourly embedded machine learning in Haledon, NJ is $154,971.00, according to ZipRecruiter salary data. Most workers in this role earn between $132,900.00 and $174,800.00 per year, depending on experience, location, and employer.

What is an hourly embedded machine learning engineer?

An Hourly Embedded Machine Learning engineer is a professional who specializes in developing and deploying machine learning models on embedded systems, such as microcontrollers, IoT devices, or edge devices, and is compensated on an hourly basis rather than a salaried or project-based arrangement. These engineers work to optimize algorithms so they can run efficiently on devices with limited computing power, memory, and energy resources. Their responsibilities often include model selection, quantization, optimization, and integration of machine learning pipelines into hardware. Hiring on an hourly basis allows for flexibility in project scope and duration, making it ideal for companies with specific, time-limited needs. They often collaborate with hardware engineers, data scientists, and software developers to create intelligent embedded solutions.

How does an hourly embedded machine learning professional typically collaborate with hardware and software teams during a project?

As an Hourly Embedded Machine Learning professional, you will often work closely with both hardware and software engineering teams to ensure that machine learning models are efficiently integrated into embedded systems. This typically involves frequent communication to align on hardware constraints, such as memory and processing power, and to optimize algorithms for real-time performance. You may also participate in joint debugging sessions and code reviews to address integration issues and streamline deployment. Collaboration is key, as successful projects depend on the seamless interaction between machine learning solutions and the embedded hardware platform.

What are the key skills and qualifications needed to thrive as an hourly embedded machine learning engineer, and why are they important?

To thrive as an Hourly Embedded Machine Learning Engineer, you need a solid background in embedded systems, machine learning algorithms, and programming languages like C/C++ and Python, often supported by a degree in computer engineering or a related field. Familiarity with tools such as TensorFlow Lite, embedded Linux, microcontroller development environments, and model optimization frameworks is typically required. Strong problem-solving skills, adaptability, and effective communication help you address complex technical challenges and collaborate with cross-functional teams. These skills are crucial for designing efficient, real-time ML solutions that operate reliably on resource-constrained embedded devices.

What is the difference between Hourly Embedded Machine Learning vs Hourly Data Scientist?

AspectHourly Embedded Machine LearningHourly Data Scientist
CredentialsKnowledge of embedded systems, programming, ML algorithmsDegree in Data Science, Statistics, or related field
Work EnvironmentEmbedded hardware, IoT devices, real-time systemsData analysis, modeling, visualization in office or cloud
Industry UsageConsumer electronics, automotive, IoT devicesFinance, healthcare, marketing, research

Hourly Embedded Machine Learning specialists focus on integrating ML models into embedded systems and hardware, often working with IoT devices and real-time constraints. In contrast, Hourly Data Scientists analyze large datasets to develop predictive models primarily in cloud or office environments. While both roles require programming skills, embedded ML emphasizes hardware integration, whereas data science centers on data analysis and visualization.

What job categories do people searching Hourly Embedded Machine Learning jobs in Haledon, NJ look for?

The top searched job categories for Hourly Embedded Machine Learning jobs in Haledon, NJ are:

What cities near Haledon, NJ are hiring for Hourly Embedded Machine Learning jobs?

Cities near Haledon, NJ with the most Hourly Embedded Machine Learning job openings:

Infographic showing various Hourly Embedded Machine Learning job openings in Haledon, NJ as of June 2026, with employment types broken down into 1% As Needed, 65% Full Time, 31% Part Time, 1% Temporary, 1% Contract, and 1% Nights. Highlights an 90% Physical, 1% Hybrid, and 9% Remote job distribution, with an average salary of $154,971 per year, or $74.5 per hour.

Principal Machine Learning Engineer, Matching and Recommendations

Bumble Inc.

Manhattan, NY โ€ข On-site

$345 - $410/hr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 4 days ago


Job description

What youโ€™ll do
  • Define and lead the technical strategy for AI and Machine Learning systems that power recommendations, ranking, and personalization across Bumble products, delivering measurable improvements in user engagement and safety
  • Design, develop, and deploy production-grade models using modern ML frameworks such as PyTorch, ensuring scalability and reliability in high-traffic environments
  • Build and deploy production AI Agents using raw and fine-tuned foundational Large Language Models (LLMs), along with sub-agents, tools, and MCP integrations
  • Architect end-to-end ML pipelines, integrating data processing (e.g. Spark, Airflow) with model training, evaluation, and deployment workflows
  • Drive experimentation frameworks, including A/B testing and offline evaluation, to continuously improve model performance and product outcomes
  • Partner cross-functionally with Product, Engineering, and Data leadership to translate business challenges into impactful ML solutions, collaborating with purpose and influencing at senior levels
  • Mentor and elevate senior individual contributors, fostering a culture of Excellence, Curiosity, and continuous learning across the ML community
  • Take ownership of complex, ambiguous problem spaces, seeing initiatives through from insight to impact while adapting approaches with an agile mindset
  • Champion responsible AI practices, ensuring fairness, transparency, and user safety are embedded into all machine learning systems
About You
  • Typically requires 10-15 years of experience, though we welcome candidates with alternative backgrounds that demonstrate equivalent skills.
  • Deep expertise in machine learning, with handsโ€‘on experience building and deploying large-scale systems in production environments
  • Strong proficiency in Python and at least one major ML framework (e.g. PyTorch, TensorFlow), with experience in areas such as recommendation systems, ranking models, or NLP
  • Expertise in prompting and fine-tuning Large Language Models (LLMs) and building production AI Agents
  • Proven experience designing scalable data and ML pipelines using tools such as Spark, Airflow, or similar distributed systems
  • Demonstrated ability to operate as a senior individual contributor, influencing technical strategy and decision-making without direct authority
  • Experience partnering effectively across functions, collaborating with purpose and taking ownership of outcomes in complex organisational environments
  • A track record of mentoring and uplifting others, role-modeling Respect and Excellence while building inclusive, high-performing teams
  • Strong AI fluency, with the ability to independently design, evaluate, and optimise ML systems, and guide others in the responsible and effective application of AI

$345,000 - $410,000 a year

Benefits & Perks
  • Insurance: Medical/dental/vision, 30-day eligibility.
  • Unlimited PTO + 1 company-wide week off + Focus Fridays every week.
  • Fully paid life and long-term disability insurance.
  • 401k with 4% company match if you contribute 6%, 90-day eligibility.
  • Monthly wellness benefit and access to Noom, Unmind, and Your Money Line.
  • Maternity and Fertility benefit + 26 week paid parental leave.
  • Premium App Access.
Inclusion at Bumble Inc.

Bumble Inc. is an equal opportunity employer and we strongly encourage people of all ages, colour, lesbian, gay, bisexual, transgender, queer and non-binary people, veterans, parents, people with disabilities, and neurodivergent people to apply. We're happy to make any reasonable adjustments that will help you feel more confident throughout the process, please don't hesitate to let us know how we can help.

In your application, please feel free to note which pronouns you use (For example: she/her, he/him, they/them, etc).

AI in Bumble Inc. Hiring

At Bumble, we may use AI tools to support parts of our recruitment process โ€” such as helping us record, transcribe, and summarize conversations, and supporting job alignment by comparing resumes and job descriptions to highlight skills and potential roles that may be a good match. These tools help us work more efficiently and stay focused on you during our conversations. Importantly, all hiring decisions are made by people. AI is used only to support our teamโ€™s efficiency and improve the candidate experience โ€” not to evaluate or decide on your candidacy. Participation in AI-supported interviews and conversations is completely voluntary and will not impact your candidacy. If youโ€™d prefer to opt out, simply let your recruiter or interviewer know at the start of a call, or anytime during the interview or conversation. Summaries and related data are retained only as long as needed in line with our internal data retention policies. If at any point youโ€™d like a transcription or summary deleted, please contact your recruiter directly.

For further information on how we hold and manage your data, please refer to our Privacy Policy.

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