TensorFlow

TensorFlow Engineer Jobs

102 jobs found

1d ago

AI/Machine Learning Engineer SME

Integration Innovation, Inc. (i3) Washington, DC

Washington, DC

1d ago

$200k - $350k/yr Full time Artificial Intelligence Data Architecture Data Engineer

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Job Description

SME-level AI/Machine Learning Engineer to architect enterprise-ready AI/ML solutions for the Defense Intelligence Agency (DIA) Enterprise Data Management & Analytics (EDMA) effort.

Responsibilities

Architect enterprise-ready AI/ML solutions, covering model-training pipelines, inference services, feature stores, data-ingestion pathways, and MLOps frameworks aligned to requirements
Define the technical roadmap by translating complex mission needs into actionable architectures, workstreams, and implementation plans
Lead multi-disciplinary engineering teams, directing algorithms, model selection, data strategy, integration, and delivery cadence
Oversee sprint planning and technical execution to keep deliverables on schedule, clear blockers, and ensure engineering artifacts meet enterprise standards
Act as the primary technical interface with government stakeholders, explaining architecture decisions, model behaviors, risk tradeoffs, and recommended solution pathways
Establish and enforce best practices across ML engineering, DevSecOps, coding standards, test automation, and model life-cycle governance
Run technical reviews and mentor engineers to raise team capability and maintain alignment to mission outcomes
Guide evaluation and adoption of advanced ML tools and approaches, including PyTorch, TensorFlow, JAX, distributed compute, MLOps platforms, generative-AI modernization patterns, and enterprise-scale data solutions
Integrate AI capabilities into cloud-native or hybrid architectures while meeting security, accreditation, and enterprise data management requirements
Provide SME support for strategic planning, including solution briefs, architecture diagrams, CONOPS development, and decision-ready artifacts for government review panels
Champion responsible-AI and governance aligned to IC and DoD directives

Requirements

U.S. Citizenship
Active Secret clearance with the ability to obtain and maintain TS/SCI
Bachelor’s degree (Master’s preferred) in Computer Science, Engineering, Applied Mathematics, Data Science, or a related field
Minimum 14 years of progressive experience in AI/ML, data engineering, or advanced analytics
Demonstrated experience architecting and deploying enterprise AI/ML systems
Deep proficiency in Python and modern ML frameworks, including PyTorch, TensorFlow, and JAX
Experience leading technical teams, performing architecture trade studies, and guiding development across the full ML life cycle
Strong understanding of secure software engineering, distributed systems, DevSecOps, and containerization
Excellent communication skills and ability to interface with senior government stakeholders

Technologies

Python
PyTorch
TensorFlow
JAX
MLOps
DevSecOps

Preferred Qualifications

Experience supporting AI/ML projects in government, defense, or intelligence environments
Familiarity with Intelligence Community mission needs and data types
Exposure to cloud-native development (AWS, Azure, GovCloud)
Experience building or supporting NLP, computer-vision, generative-AI, or anomaly-detection models
Knowledge of MLOps, CI/CD, container orchestration, or distributed training frameworks

Location: Washington, DC (hybrid)

Salary: USD 200,000 - 350,000 per year

Company: Integration Innovation, Inc. (i3)

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2d ago

Senior Machine Learning Engineer

Intuit Mountain View, CA

Mountain View, CA

2d ago

$171k - $232k/yr Full time Ai Engineer Artificial Intelligence Automation

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Job Description

Join Intuit in Mountain View, CA (onsite) and help bring machine learning from prototype to dependable, scalable production. This role focuses on architecting, coding, optimizing, and deploying ML models at scale, with an emphasis on automating delivery, monitoring outcomes, and continuously improving performance for customer impact.

Compensation: USD 171,000 - 231,500 per year.

What you’ll do

Architect and build ML systems that improve scalability, usability, and performance.
Collaborate cross functionally with product managers, data scientists, and engineers to understand needs, implement solutions, refine approaches, and design machine learning and related algorithms.
Communicate results effectively to peers and leaders.
Evaluate state-of-the-art technologies and apply them to deliver customer benefits.
Work with a variety of data sources, partnering with others to refine features and build end-to-end pipelines.

How the work shows up

Model productionalization: partner with data scientists to move prototype models toward customer-scale use. This can include increasing training data, automating training and prediction, and orchestrating pipelines for continuous prediction.
Model comparison and metrics: apply understanding of the underlying data details and provide metrics to compare models.
Model enhancement: improve existing codebases to raise prediction performance or reduce training time, including both exploratory work and directed improvements based on performance needs and ideas from data science teammates.
Machine learning tools: build project-focused tools that reduce friction in the data science process, such as speeding up training, making data processing easier, or improving data management. These tools may span multiple projects and are generally decoupled from any single initiative.

Key skills and focus areas

You’ll be expected to help architect, code, optimize, and deploy ML models at scale using current industry tools and techniques. The role also includes automating, delivering, monitoring, and improving machine learning solutions, with strong emphasis on software development, systems engineering, data wrangling, feature engineering, architecting, and testing.

Requirements

Education: BS, MS, or PhD degree in Computer Science or related field (or equivalent practical experience).

Technologies you’ll work with

Programming: Scala, Java, Python, SQL
ML/DL: SkLearn, NLTK, Numpy, Pandas, TensorFlow, Keras
Big data: Spark, Hive, Flink
Cloud/ML ops: AWS, AWS Sagemaker, Docker, Kubernetes

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2d ago

Staff Machine Learning Engineer - Generative AI

Xometry Denver, CO

Denver, CO

2d ago

$140k - $230k/yr Full time AI Ai Ml Artificial Intelligence

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Job Description

Xometry is looking for a Staff Machine Learning Engineer to lead its Generative AI efforts focused on manufacturing-related challenges. This hybrid role is based in Denver, CO and centers on building and deploying multimodal generative models that help interpret documents and extract structured data.

You will collaborate across engineering and business teams to translate AI research and production-ready engineering into real product experiences across Xometry’s marketplace, using cloud infrastructure and modern ML tooling to operate at scale.

What you’ll do

Set the technical direction for the Generative AI team by defining best practices and driving high-impact innovation
Shape the AI roadmap by identifying where generative AI can create the most value across Xometry’s marketplace
Design, develop, and deploy large language and generative models for multimodal document processing and structured data extraction
Experiment with ways to combine text, images, and 3D data to improve solution quality and speed
Build data pipelines and training workflows capable of handling large, complex datasets
Use AWS and other platforms to train, optimize, and deploy models into production at scale
Partner with engineers, product leaders, and business teams to integrate AI capabilities into real products and customer workflows
Mentor teammates on advanced ML methods, model architecture, and best practices to raise team capability
Stay current with generative AI and deep learning research and bring new ideas into production

What you’ll need

Bachelor’s degree required; an advanced degree (M.S. or PhD) in Computer Science, Machine Learning, AI, or a related field is a plus
5+ years of experience in machine learning or data science, with deep expertise in generative models, LLMs, or computer vision
Demonstrated work building large-scale language and vision models (Transformers, GPT, VLMs)
Hands-on experience with multimodal data including text, images, and 3D
Proficiency in Python and key ML libraries: PyTorch, TensorFlow, pandas, and NumPy
Strong foundation in probability, statistics, and optimization for generative modeling
Experience deploying ML/AI models using cloud microservice architecture, with AWS preferred
Strong software engineering skills, including object oriented programming, testing, version control, CI/CD best practices, and IaC (terraform preferred)
Proven ability to communicate effectively with stakeholders across the organization, from executives to product managers
Background in manufacturing or supply chain is a plus; curiosity and drive are emphasized
Must be a U.S. Citizen or Green Card holder for ITAR compliance

Technologies

AWS
Python
PyTorch
TensorFlow
pandas
NumPy
Transformers
GPT
VLMs
CI/CD
IaC
terraform

Compensation and location

Location: Denver, CO (hybrid)
Salary: USD 140,000 - 230,000 per year
Experience: Minimum 5 years

Benefits

401(k) match
Medical, dental and vision insurance
Life and disability insurance
Generous paid time off including vacation, sick leave, floating and fixed holidays
Maternity and bonding leave
EAP
Other wellbeing resources

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21d ago

Machine Learning Engineer/Scientist

Precise Systems Edgewood, MD

Edgewood, MD

21d ago

$77k - $115k/yr Full time Ai Ml Artificial Intelligence Data Science

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Job Description

Precise Systems is seeking a Machine Learning Engineer/Scientist to support chemical and biological defense projects by building and validating ML-driven insights for laboratory automation.

Responsibilities

Perform end-to-end work on machine learning model research, development, testing, and evaluation for chemical and biological datasets under guidance of senior professionals.
Support chemical and biological R&D efforts by applying data science to help develop modern solutions to outpace emerging threats.
Partner with interdisciplinary teams to design machine learning solutions for biological and chemical challenges.
Develop and apply state-of-the-art ML tools and algorithms to optimize DoD research efforts.
Conduct internal verification and validation of models and code to support real laboratory deployment readiness.
Create dashboards and/or graphical user interfaces that support laboratory automation for experimentation workflows.
Analyze biological or chemical data using Python and R to produce mathematical insights and improve system performance.
Use DevSecOps and MLOps to integrate machine learning processes into secure data pipelines used in laboratory settings.
Collaborate with academic and industry partners to test and refine algorithms and biological systems in varied environments.
Carry out ongoing research to keep models and analysis current with latest advancements.

Requirements

Master’s degree in Computer Science, Computer Engineering, Computational Biology, or Bioinformatics.
OR Bachelor’s degree in a relevant field with 5 years of experience in a laboratory and/or computational environment.
3 years of experience in a laboratory and/or computational environment.
Hands-on experience applying machine learning algorithms and data science techniques to physical science problems.
Proficiency in Python and other programming languages commonly used for machine learning and data analysis.
Familiarity with physical science and laboratory experience.
Proficiency with software development approaches such as agile, git, DevSecOps, and/or MLOps.
Advanced communication skills for working with interdisciplinary teams, government representatives, and industry partners.
Willingness to learn new technologies and take on new challenges.
Strong written and oral communication, excellent interpersonal skills, and proficiency in PC software packages for data analysis and visualization.

Technologies

Python
R
DevSecOps
MLOps
git
agile
TensorFlow
PyTorch
PC software packages

Preferred

Ph.D. in Computational Biology/Chemistry, Machine Learning, Physical Sciences, or a related field with 1 year of experience in a laboratory and/or computational environment.
Expertise in advanced ML frameworks (e.g., TensorFlow, PyTorch) applied to biological data.
Experience with bioinformatics tools and databases.
Experience performing wet laboratory experimentation to validate software and models.
Knowledge of automation systems and robotics for laboratory workflows.
Collaboration experience with academic and industry partners on synthetic biology and machine learning projects.

Compensation

Salary range: $76,709.46 - $115,064.18 (annualized USD).
Compensation determined by education, experience, skills, competencies, and contract-specific requirements.
Salary is one component of Precise Systems’ total compensation package.

Benefits

Health insurance
Life and accidental death and dismemberment coverage
Disability insurance
Retirement plans
Holiday pay
Employee-managed leave
Professional growth opportunities

Location: Edgewood, MD (onsite)

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22d ago

Machine Learning Engineer

Cognizant New York, NY

New York, NY

22d ago

$110k - $135k/yr Full time Agentic Ai Ai Agent Ai Agent Platform

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Job Description

Cognizant is hiring a Machine Learning Engineer (Agentic AI) for a hybrid role in New York, NY. This position focuses on building and improving agentic AI systems and machine learning solutions that work reliably in production environments, while partnering across Data Science, Product, Engineering, and DevSecOps.

The anticipated annual salary is $110,000 to $135,000, depending on experience, qualifications, geographic location, skills, and other job-related factors. The role may also be eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to plan terms.

You will also have access to health and wellbeing support, retirement savings with company contributions (subject to plan provisions), paid time off, and professional development opportunities, including training and certification.

What you’ll do

Design, develop, deploy, and optimize machine learning models and Agentic AI systems to address real-world business challenges.
Collaborate with Data Science, Product, Engineering, and DevSecOps teams to deliver scalable, secure, production-ready AI solutions.
Build and maintain cloud-native AI applications, including data ingestion pipelines, memory frameworks, and model-serving architectures.
Apply MLOps and AgentOps best practices such as automated testing, CI/CD or CI/CD/CT pipelines, monitoring, observability, and model governance.
Drive continuous improvement by evaluating emerging technologies and applying engineering best practices across AI development projects.

What you bring

Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field (or equivalent professional experience).
Experience designing, developing, deploying, and supporting machine learning applications in enterprise environments.
Strong programming skills in Python and SQL, with exposure to C++ preferred.
Experience with ML and deep learning frameworks such as TensorFlow, PyTorch, and scikit-learn.
Solid software engineering fundamentals, including object-oriented programming, RESTful APIs, microservices, testing, version control, and system design.
Experience deploying ML solutions in cloud-based environments.
Familiarity with CI/CD and automated deployment practices, model versioning, and monitoring frameworks.
Knowledge of data engineering concepts including ETL, Spark/PySpark, distributed processing, and large-scale data environments.
Strong communication, problem-solving, and collaboration skills, including experience in Agile development environments.

Technologies you may work with

Python, SQL, C++, TensorFlow, PyTorch, scikit-learn, RESTful APIs, microservices, CI/CD pipelines, CI/CD/CT pipelines, MLOps, AgentOps, ETL, Spark, PySpark, MLFlow, Amazon SageMaker Pipelines, GitHub Actions, Jenkins, CloudBees, LLM development tools, AI-assisted software engineering platforms, relational databases, NoSQL databases, and graph databases.

Benefits and support

Medical, dental, and vision insurance
Health Savings Account (HSA) and Flexible Spending Accounts (FSA), where applicable
Company-paid life insurance and disability coverage
401(k) retirement savings plan with company contributions, subject to plan provisions
Paid time off, company holidays, and leave programs
Employee Assistance Program (EAP)
Wellbeing and mental health resources
Professional development, training, and certification opportunities
Career growth and internal mobility programs
Associate recognition and reward programs

Work model

Hybrid role requiring 3 days per week in a client or Cognizant office in New York, New York.

These can help you stand out

Experience developing Agentic AI applications and autonomous AI workflows
Familiarity with Agent Development Life Cycle (ADLC) methodologies and observability frameworks
Experience using LLM development tools and AI-assisted software engineering platforms
Knowledge of MLFlow, Amazon SageMaker Pipelines, GitHub Actions, Jenkins, CloudBees, or similar MLOps technologies
Understanding of model governance, explainability, drift detection, bias monitoring, and AI risk management practices
Experience working with relational, NoSQL, and graph databases
Knowledge of statistics, probability, linear algebra, predictive analytics, and machine learning optimization techniques
Passion for continuous learning and staying current with emerging AI technologies

Application details

Applications will be accepted until September 30, 2026.

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23d ago

AI Engineer

Microsoft Redmond, WA

Redmond, WA

23d ago

$83 - $94/hr Contract Ai Ml Artificial Intelligence Azure Machine Learning

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Job Description

Build and maintain AI and machine learning systems for embodied robotics research with simulation and robotic hardware.

Responsibilities

Fine-tune and improve sophisticated software implementation projects
Architect, design, and implement test automation infrastructure
Develop training and evaluation pipelines using Azure Machine Learning
Analyze and refine training datasets for robotic and AI applications
Create tools that support rapid deployment of novel algorithms in simulation and on hardware
Integrate robotic control and perception stacks with simulation frameworks
Adapt server and client code for new robotic or simulation APIs during system integration
Collect and convert demonstration data into correct formats for model training
Launch and monitor fine-tuning runs for vision-language-action and diffusion models

Requirements

5–7 years of related experience in AI engineering, machine learning, or robotics
Demonstrated experience integrating robotic control and perception stacks with simulation frameworks
Hands-on experience developing and maintaining training and evaluation pipelines
Experience working with robotic hardware or simulation environments in a research or production context
Master’s degree in Computer Science, Computer Engineering, or a related technical field (required)
PhD-level academic work or studies in a robotics lab (strongly preferred)
Python proficiency (5+ years)
PyTorch hands-on experience (2+ years)
ROS2 experience (1+ year)
Solid foundation in data structures, algorithms, and software design
Experience with machine learning frameworks such as TensorFlow or PyTorch
Familiarity with training robotic models including vision-language-action models, diffusion models, and reinforcement learning policies
Experience with simulation environments or robotic hardware for system integration
Experience performing in-depth troubleshooting and unit testing on new and legacy production systems
Strong problem diagnosis and resolution skills

Technologies

Azure Machine Learning
Python
PyTorch
ROS2
TensorFlow

Benefits

Medical, dental & vision
Hospital plans
401(k) Retirement Plan: pre-tax and Roth post-tax contributions available
Life Insurance: company paid Basic Life and AD&D; voluntary Life & AD&D for employee and dependents
Company paid short and long-term disability
Health & Dependent Care Spending Accounts (HSA & DCFSA)
Employee Assistance Program
Time Off/Leave: PTO, Allegis Group Paid Family Leave, Parental Leave

Job Details

Location: Redmond, WA (onsite)
Job type: Contract
Duration: 13 months
Weekly schedule: 40 hours
Status: Non-Exempt
Pay range: $83.50 - $94.50 per hour
Application deadline: Apply within 72 hours of the posting date to ensure consideration

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23d ago

AI Engineer

Chase Robotics and Ai Rocklin, CA

Rocklin, CA

23d ago

Up to $200k/yr Full time Ai Ml Artificial Intelligence Big Data

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Job Description

Chase Robotics and Ai is hiring an AI Engineer based in Rocklin, CA for a hybrid setup. In this role, you will design, build, and deploy advanced AI models and machine learning frameworks, with a focus on scalable data systems and production-ready integration across cloud environments.

The work blends model development with large-scale data engineering, statistical analysis, and collaboration on applications such as natural language processing and generative AI solutions.

Responsibilities

Develop, train, and evaluate machine learning models using frameworks such as TensorFlow to support accurate and efficient outcomes for diverse applications.
Design and implement scalable big data systems with tools such as Hadoop, Spark, and ETL processes to handle large datasets effectively.
Use statistical modeling and analysis methods with R, SAS, and statistical analysis software to derive meaningful insights from complex data.
Integrate AI models into production by applying deployment strategies that prioritize robustness and scalability across AWS cloud platforms.
Collaborate with cross-functional teams to build NLP applications and generative AI solutions aimed at improving user engagement.
Run model training cycles focused on predictive modeling analysis, model evaluation, and continuous improvement.
Use SQL databases, data mining techniques, and database design principles to improve data storage and retrieval for analytics.

Requirements

Proven experience in machine learning and AI-driven analysis, including a strong understanding of AI implementation in real-world scenarios.
Hands-on experience with machine learning frameworks such as TensorFlow, Spark MLlib, or similar tools.
Familiarity with big data systems, including components in the Hadoop ecosystem such as HDFS and MapReduce, along with Spark for large-scale processing.
Programming experience in Python, Java, C, VBA, and Bash (Unix shell), plus additional knowledge of SQL for database management.
Experience with cloud-based machine learning services on AWS (or similar providers) is highly desirable.
Knowledge of NLP, predictive modeling analysis, statistical analysis tools, and model evaluation techniques.
Understanding of analytics workflows involving data mining, data integration via Talend, visualization using Looker, and considerations for data scalability.

Salary: USD 200,000 per year.

Technologies: TensorFlow, Hadoop, Spark, ETL, R, SAS, statistical analysis software, AWS, NLP, SQL, data mining techniques, database design principles, Spark MLlib, HDFS, MapReduce, Python, Java, C, VBA, Bash (Unix shell), Talend, Looker