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Adaptive Ml Jobs (NOW HIRING)

HARMAN International

Audio ML Engineer (Research)

... drive adaptive EQ/DRC/spatial parameters) while maintaining stability and explainability. • Define data collection and labeling strategies, data QA, augmentation, bias checks, and experiment ...

Pear

AI / ML Engineer - Known

San Francisco, CA · On-site

... adaptive recommendation loops, and natural voice/LLM-based interactions that help users connect ... Develop and maintain ML pipelines for data ingestion, feature generation, model training ...

Praxis, Inc.

AI / ML Engineer - Known

San Francisco, CA · On-site

$180 - $240/hr

... adaptive recommendation loops, and natural voice/LLM-based interactions that help users connect ... Develop and maintain ML pipelines for data ingestion, feature generation, model training ...

Sandisk

AI/ML ASIC Architect

Milpitas, CA

In this AI/ML ASIC Architecture position, you will develop AI Storage Solutions based advanced ... and highly competitive adaptive accelerators solutions. Typical activities include writing ...

Sandisk

AI/ML ASIC Architect

Milpitas, CA · On-site

In this AI/ML ASIC Architecture position, you will develop AI Storage Solutions based advanced ... and highly competitive adaptive accelerators solutions. Typical activities include writing ...

Sandisk

AI/ML ASIC Architect

Milpitas, CA · On-site

$194K - $322K/yr

In this AI/ML ASIC Architecture position, you will develop AI Storage Solutions based advanced ... and highly competitive adaptive accelerators solutions. Typical activities include writing ...

Sandisk

AI/ML ASIC Architect

Milpitas, CA · On-site

In this AI/ML ASIC Architecture position, you will develop AI Storage Solutions based advanced ... and highly competitive adaptive accelerators solutions. Typical activities include writing ...

Sandisk

AI/ML ASIC Architect

Milpitas, CA · On-site

$136K - $226K/yr

In this AI/ML ASIC Architecture position, you will develop AI Storage Solutions based advanced ... and highly competitive adaptive accelerators solutions. Typical activities include writing ...

Sandisk

AI/ML ASIC Architect

Milpitas, CA · On-site

In this AI/ML ASIC Architecture position, you will develop AI Storage Solutions based advanced ... and highly competitive adaptive accelerators solutions. Typical activities include writing ...

84.51°

Lead AI/ML Engineer (P4645)

Cincinnati, OH · On-site

$98K - $129K/yr

This is not a generalist ML role: you will bring deep optimization foundations and use them as the platform on which the next generation of intelligent, adaptive systems are built. You will ...

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How much do adaptive ml jobs pay per year?

As of Aug 23, 2026, the average yearly pay for adaptive ml in the United States is $102,439.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,500.00 and $119,000.00 per year, depending on experience, location, and employer.

What is an adaptive ML engineer?

An Adaptive ML Engineer is a professional who designs, develops, and maintains machine learning systems that can adjust and improve their performance dynamically in response to new data or changing environments. These engineers focus on creating algorithms and models that evolve over time, often using techniques like online learning, reinforcement learning, or continual learning. Their work is crucial in applications where static models are insufficient, such as real-time recommendations, autonomous vehicles, and personalized user experiences. Adaptive ML Engineers also ensure that their systems remain robust, accurate, and relevant as data patterns shift.

What are the key skills and qualifications needed to thrive as an adaptive ML engineer?

To thrive as an Adaptive Machine Learning Engineer, you need strong foundations in machine learning algorithms, data analysis, and programming (often with a degree in computer science or a related field). Familiarity with ML frameworks (such as TensorFlow or PyTorch), version control systems, and cloud platforms is typically required, along with knowledge of adaptive and online learning techniques. Strong problem-solving abilities, creativity, and effective communication skills help you design, iterate, and implement adaptive models that respond to evolving data. These skills ensure that ML solutions can dynamically adjust to new information, maximizing their long-term effectiveness and impact.

What are common challenges faced by professionals working in adaptive ML roles, and how can they overcome them?

Professionals in Adaptive Machine Learning often encounter challenges such as handling non-stationary data streams, ensuring model stability during continuous updates, and addressing concept drift where data patterns change over time. To overcome these, it's important to implement rigorous monitoring systems, use robust validation techniques, and collaborate closely with data engineering teams to ensure data quality. Staying up to date with the latest research and leveraging online learning frameworks can also help adapt models efficiently and maintain high performance.

What is the difference between Adaptive Ml vs Data Scientist?

AspectAdaptive MlData Scientist
Required CredentialsTypically a degree in Computer Science, Data Science, or related fields; knowledge of machine learning frameworksUsually a degree in Data Science, Statistics, Computer Science, or related fields; strong programming and statistical skills
Work EnvironmentTech companies, AI startups, research labs focusing on machine learning applicationsVaried environments including tech firms, finance, healthcare, and consulting firms analyzing data for insights
Employer & Industry UsageUsed in industries developing adaptive machine learning models and AI solutionsUsed across industries for data analysis, predictive modeling, and decision support

Adaptive ML specialists focus on developing and implementing machine learning models that adapt over time, often working on AI systems. Data Scientists analyze data, build models, and generate insights. While both roles require strong technical skills, Adaptive ML roles are more specialized in creating adaptive algorithms, whereas Data Scientists focus on broader data analysis and modeling tasks.

Is adaptive machine learning a high paying job?

Adaptive machine learning roles are generally well-paid due to the specialized skills required, such as expertise in algorithms, data analysis, and programming languages like Python or R. Salaries vary based on experience, location, and industry, but professionals in this field often earn above average wages compared to other tech roles.

What is adaptive learning in machine learning?

Adaptive learning in machine learning refers to systems that automatically adjust their models or algorithms based on new data or changing conditions to improve performance over time. For an adaptive ML role, skills in data analysis, model tuning, and familiarity with algorithms like reinforcement learning are essential to develop systems that learn and evolve dynamically.
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Adaptive Ml jobs near you
Infographic showing various Adaptive Ml job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 22% Full Time, 73% Part Time, and 4% Contract. Highlights an 31% Physical, 1% Hybrid, and 68% Remote job distribution, with an average salary of $102,439 per year, or $49.2 per hour.

Audio ML Engineer (Research)

HARMAN International

Remote

Full-time

Re-posted 23 days ago

Job description

Job Summary:
HARMAN International is a global technology company focused on innovative audio solutions. The Audio ML Engineer (Research) will develop machine learning models to enhance Intelligent Audio experiences across devices, focusing on perception and personalization while ensuring robust deployment in both embedded and cloud environments.
Responsibilities:
• Develop ML models for perception-related tasks (e.g., quality prediction, artifact detection, scene/context classification, personalization embeddings, preference modeling).
• Design solutions that can run on-device (quantized, efficient inference) and/or scale in cloud pipelines (batch evaluation, fleet learning, offline training + on-device inference).
• Build personalization and adaptation strategies that integrate with DSP pipelines (e.g., model outputs drive adaptive EQ/DRC/spatial parameters) while maintaining stability and explainability.
• Define data collection and labeling strategies, data QA, augmentation, bias checks, and experiment tracking—so results are reproducible and transferable to product.
• Apply compression/acceleration techniques (quantization, pruning, distillation, ONNX export, hardware-aware training) to meet latency and footprint constraints.
• Partner with DSP, perceptual, and productization engineers to deliver reference pipelines, integration guidelines, and acceptance metrics for OneUX releases.
• Use modern AI tooling (LLM-based coding assistants, data analysis copilots, automated report generation) to accelerate iteration while keeping rigorous review and validation.
Qualifications:
Required:
• Education: MS or PhD in CS/EE/Statistics/Applied ML (or BS with strong equivalent experience).
• Experience: 5+ years applied ML engineering experience; 2+ years specifically in audio/speech or time-series ML strongly preferred.
• ML Stack: Strong proficiency in Python, PyTorch/TensorFlow, dataset pipelines, evaluation methodology, and experiment tracking.
• Deployment Skills: Experience deploying models to embedded (TFLite / ONNX Runtime / custom inference) and/or cloud (service or batch pipelines, MLOps practices).
• Signal + Perception Understanding: Working knowledge of DSP/audio fundamentals and how ML interacts with perceptual outcomes.
• AI Tools: Demonstrated experience using AI-assisted tools to speed up coding, testing, debugging, and documentation.
Preferred:
• Experience with audio ML domains (speech enhancement, denoising, source separation, spatial audio ML, perceptual audio metrics, recommendation/personalization).
• Familiarity with on-device acceleration (NNAPI, Core ML concepts, CUDA/TensorRT-like optimization where applicable).
• Experience with privacy-preserving learning or on-device personalization approaches.
• Patents/publications or shipped ML features in consumer/automotive audio products.
Company:
Headquartered in Stamford, Connecticut, HARMAN (harman.com) designs and engineers connected products and solutions for automakers, consumers, and enterprises worldwide, including connected car systems, audio and visual products, enterprise automation solutions; and services supporting the Internet of Things. Founded in 1980, the company is headquartered in Stamford, USA, with a team of 10001+ employees. The company is currently Late Stage.

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