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Adaptive Ml Jobs (NOW HIRING)

Stratus

Principal or Senior AI/ML Architect

Design and evolve agent-based workflows, enabling reasoning-capable, adaptive, multi-step systems ... AI/ML capabilities are opinionated at the center, with foundations for marketplace or platform ...

CYNET SYSTEMS

ML Engineer

Santa Clara, CA · On-site

$55 - $60/hr

... plugin development, adaptive orchestration of multiple AI skills and APIs, contextual memory ... ML. * Python. * TensorFlow. * PyTorch. * Hugging Face Transformers. * LLMs. * Multi-modal models.

eTeam

AI/ML Engineer - Generative AI

Manhattan, NY · On-site

Job Summary : eTeam is a company seeking an AI/ML Engineer specializing in Generative AI. The role ... adaptive agent behavior. • Collaborate closely with GenAI engineers, application teams, MLOps ...

Maple AI, Inc

ML Research Engineer

New York, NY · On-site

$120K - $250K/yr

About the Role As an ML Research Engineer at Maple, you'll be a part of our core product team ... Develop pipelines to construct knowledge graphs from business data , powering adaptive AI ...

CYNET SYSTEMS

ML Engineer

Santa Clara, CA · On-site

$55 - $60/hr

... plugin development, adaptive orchestration of multiple AI skills and APIs, contextual memory ... ML. Python. TensorFlow. PyTorch. Hugging Face Transformers. LLMs. Multi-modal models. RAG pipelines.

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$102.4K

$150K

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.

SETA - Complex Adaptive Systems with Security Clearance

Oak Leaf Solutions

Arlington, VA • On-site

$99K - $134K/yr

Other

Posted 9 days ago

Job description

Complex Adaptive Systems: Clearance: TS with SCI eligibility. Active SAP eligibility preferred.
Location: Arlington, VA
Salary: Based on experience The successful candidate will serve as a Quantitative Analyst SETA supporting a government program managers in the development, integration, and scaling of complex adaptive system modelling and data-intensive analytical capabilities. This role involves integrating complex, multi-source intelligence and commercial/open-source data streams into graph-based, network analytical frameworks to support strategic competition analysis, industrial base resilience, and national security decision-making. Requirements: Master’s degree or PhD in Economics, Data Science, Applied Mathematics, or a related quantitative field
5+ years of relevant experience in quantitative economic modeling, advanced econometrics, and network data science/data engineering applied to complex adaptive systems
Demonstrated experience in large-scale data processing, multi-source data pipeline integration, and managing structured/unstructured data architectures
Practical experience with graph analytics, network modeling tools, and interconnected data architectures (e.g., Python/R quantitative libraries, graph databases, or network science frameworks)
Strong background working within or alongside the U.S. Intelligence Community (IC), including familiarity with IC mission environments, data workflows, and intelligence-derived datasets
Understanding of diverse data sources across global economics, financial networks, trade flows, defense industrial supply chains, and multi-INT sources
Strong communication skills—both written (including executive PowerPoint briefs) and oral—with the ability to translate complex econometric and data models for senior defense stakeholders Preferred: Active Special Access Program (SAP) access and experience working at TS/SCI and SAP levels
Experience with defense industrial base analysis, economic statecraft, strategic competition modeling, or macroeconomic resilience metrics
Hands-on experience building decision-support tools, AI/ML-enabled analytical tools, cloud data engineering workflows, or large language model (LLM) research pipelines
Prior background supporting new program start-ups, pilot demonstrations, and transition pathways within defense or intelligence organizations
Experience in high paced private sector quantitative production environment such as systematic investing or trading, real time ad technology development or pharmacological optimization and customization
Demonstrated ability to bridge communication and technical execution across academic economists, data engineers, software developers, and operational intelligence analysts
Responsibilities:
Oversee the design, evaluation, and application of quantitative economic models, network analysis frameworks, and graph-based data integration architectures
Oversee the integration of complex, multi-source intelligence and economic datasets into scalable, operational analytical pipelines
Serve as primary technical liaison between academic researchers, software engineering teams, and IC stakeholders to ensure tools align with operational requirements
Advise leadership on program execution risks, data architecture scalability, and capability transition strategy
Prepare technical documentation, program roadmaps, and executive briefs to communicate program progress and analytical findings to senior decision-makers

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