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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 ...

CDW

Senior ML, MLOps Engineer

Concord, NC · On-site

$97K - $133K/yr

The Senior ML / MLOps Engineer designs, builds, and operates scalable machine learning solutions on ... We encourage candidates to embrace an AI mindset, one that's curious, adaptive, and ready to ...

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 ...

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Adaptive Ml information

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$42K

$102.4K

$150K

How much do adaptive ml jobs pay per year?

As of Jul 16, 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 are the key skills and qualifications needed to thrive as an Adaptive Machine Learning Engineer, and why are they important?

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 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 common challenges faced by professionals working in Adaptive Machine Learning 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.

More about Adaptive Ml jobs
What cities are hiring for Adaptive Ml jobs? Cities with the most Adaptive Ml job openings:
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Adaptive Ml jobs near you
Infographic showing various Adaptive Ml job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 23% Full Time, 73% Part Time, and 3% Contract. Highlights an 32% Physical, and 68% Remote job distribution, with an average salary of $102,439 per year, or $49.2 per hour.

Principal AI/ML Engineer with Security Clearance

ClearanceJobs Workforce Solutions

Centennial, CO • On-site

Contractor

Posted 6 days ago

Job description

Duties: Location Englewood, Colorado Hybrid opportunity Will need to be able to obtain a clearance This is a Contract opportunity In this role, you will help shape the long-term AI/ML technical vision for the organization, guide high-impact R&D initiatives, and lead the development of advanced autonomy, perception, analytics, and generative AI capabilities.
You will be responsible for setting technical direction across multiple simultaneous efforts, defining architectural standards, and ensuring that JTF Sierra’s prototypes and initiatives represent industry-leading innovation.
This role requires exceptional technical depth, the ability to operate with extreme autonomy, and the leadership presence to influence engineering culture, collaborate with program leadership, mentor staff, and represent the team to senior executives, customers, and external partners.
Role Expectations Specific to This Team • Translate broad mission objectives into program-level AI/ML architectures, strategies, staffing needs, data plans, and technical frameworks
• Drive system-level AI/ML decision-making, establishing technical standards and guiding engineering trade studies that shape platform-level autonomy and perception capabilities
• Identify and champion high-value R&D opportunities, emerging technologies, and cross-organizational partnerships that accelerate SNC’s AI/ML advancements
• Provide deep technical consultation across JTF Sierra and adjacent Business Units product lines, ensuring architectural coherence and technical excellence
• Ensure AI/ML solutions are architected for scalability and integration with enterprise-wide platforms, collaborating with IT and infrastructure teams to define and implement the necessary tools. Data pipeline, and computing resources for sustainable AI/ML operations across the organization.
• Balance program-specific AI/ML solution development with strategic focus on establishing reusable frameworks, common data assets, and infrastructure that support cross-program and enterprise-wide AI/ML adoption
Skills: • Lead design and technical direction for next-generation architectures spanning deep learning, reinforcement learning, multimodal generative AI, and advanced perception/decision systems
• Architect and oversee end-to-end multi-program AI/ML systems across platforms and embedded systems
• Assist with development of long-term technical strategies, roadmaps, and requirements for emerging AI/ML initiatives
• Identify, define, and advocate for the foundational data, compute, MLOps, and cloud/on-prem infrastructure necessary to support sustainable and secure AI/ML development and deployment across JTF Sierra and related business units.
• Establish and promote best practices for the full AI/ML lifecycle—including data management, model versioning, CI/CD for ML, monitoring, and continuous improvement—to ensure reliable deployment and operation of AI/ML models in production.
• Oversee multiple development streams, providing technical reviews, risk assessments, and mitigations plans
• Shape system-level behavior and engineering tradeoffs when requirements are ambiguous
• Lead development of simulations, sensor fusion models, vision models, and planning/decision algorithms
• Represent JTF Sierra to leadership, customers, and partners (assist in developing and presenting briefings, demos, high-level technical presentations, etc)
• Establish adaptive, agile AI/ML validation, verification, and safety frameworks for proof-of-concept level mission-critical systems
• Evaluate and introduce emerging technologies (examples: transformers, RLHF, edge AI, XAI, GPU acceleration)
• Partner with Program Manager and Project Engineer to define staffing, data, schedules, and resources required to execute JTF Sierra technical initiatives
• Coach and develop engineering talent, raising JTF Sierra’s overall AI/ML capabilities
Education: • Bachelor’s degree in Computer Science, Engineering, Math, Statistics, or related STEM field
• 14+ years of experience in AI/ML or related fields, or 16+ years without a degree
• Demonstrated mastery of deep learning, reinforcement learning, generative models, and large-scale AI/ML system architecture
• Proven experience architecting and deploying mission-critical and/or large-scale AI/ML systems
• Strong proficiency in Python, C++, C#, and/or Java with experience building scalable Machine Learning systems
• Experience providing technical leadership across teams, projects, or programs
• Ability to define technical strategies, influence senior stakeholders, and make organization-level architecture decisions
• In-depth experience with aerospace/defense-relevant regulatory and cybersecurity considerations
• Demonstrated ability to mentor and grow engineering talent within an organization
Qualifications We Prefer
• Advanced degree (MS or PhD) in AI/ML or related field
• Experience applying AI/ML to autonomy, multimodal sensor fusion, or embedded/real-time platforms
• Experience establishing or scaling ML engineering standard (MLOps, validation frameworks, data management)
• Expertise with GPU acceleration, CUDA/TensorRT, or parallel computing
• Publications, patents, or thought leadership in AI/ML
• Familiarity with edge AI, explainable AI (XAI), or emerging/advanced ML topics
• Experience translating high-level mission objectives into complex AI/ML system architectures (HMI scenarios, autonomy stacks)

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