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189 jobs found

Today

Machine Learning Engineer - AI Trainer

DataAnnotation Massachusetts 🇺🇸 Remote

Massachusetts

Today

$50 - $100/hr Contract Ai Ml Ai Model Training Ai Training

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Job Description

DataAnnotation is seeking an experienced Machine Learning Engineer - AI Trainer to help evaluate and train AI systems focused on quantitative reasoning. This is a remote, contract role with flexible project selection after completing a short assessment.

Role Overview

You will review AI-generated quantitative outputs and contribute to the creation and improvement of training and benchmark tasks. Work is completed remotely using projects provided through the platform after you pass the initial assessment.

Key Responsibilities

Assess AI-generated quantitative work, including statistical analysis, predictive modeling, scientific reasoning, and data-driven insights, ensuring both technical accuracy and real-world validity.
Design and solve quantitative problems used to train and benchmark AI systems, including work across forecasting, experimental analysis, optimization, and statistical inference.
Produce clear technical explanations and maintain well-documented analytical code.
Provide feedback that informs and improves the next generation of AI models for quantitative reasoning.

Requirements

2+ years of hands-on experience in a quantitative role or research environment, such as data science, statistics, economics, finance, physics, biology, epidemiology, operations research, or a related field.
Some coding experience is required, with comfort writing and reviewing analytical code end-to-end.
Practical experience applying statistical methods, predictive modeling, and experiment design, including examples such as A/B testing, hypothesis testing, regression, classification, and time-series forecasting.
English fluency at native or bilingual level, with strong writing skills.
A bachelor’s degree in a quantitative field is preferred (Statistics, Computer Science, Mathematics, Engineering, or similar); a master’s or PhD is a plus.
Relevant credentials are a plus, such as Kaggle Competition ranking, AWS/GCP ML certifications, or equivalent demonstrated expertise.

Technologies

Kaggle
AWS
GCP

Compensation and Job Details

Job type: contract
Location: Massachusetts (remote)
Rate: USD 50 - 100 per hourly

Benefits

Fully remote: work from anywhere in the US, Canada, UK, Ireland, Australia, and New Zealand.
Flexible schedule: choose which projects to take on and when you work.
Competitive pay: projects are paid hourly, up to $60 USD per hour, with opportunities for higher-paying projects based on strong performance.
Impact: contribute to AI systems designed to reason about data and analytics.

Payment and Eligibility Notes

Payment is made via PayPal.
They will never ask you for money.
The role is available only to candidates in the US, Canada, UK, Ireland, Australia, and New Zealand.

How to Apply

After signing up for an account, you will complete a short assessment (serving as the company’s version of an interview). If you pass, you will receive an email confirmation, and paid work will become available on the platform.

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Today

Machine Learning Engineer - AI Trainer

DataAnnotation New York, NY 🇺🇸 Remote

New York, NY

Today

$50 - $100/hr Full time Ai Ml Ai Training Artificial Intelligence

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Job Description

DataAnnotation is seeking an experienced Machine Learning Engineer - AI Trainer to evaluate and help train AI systems focused on quantitative reasoning and analytical tasks. This fully remote role is paid hourly and supports a flexible approach to project selection and scheduling.

Role Overview

You will review AI-generated quantitative work and contribute to the creation of training and benchmark problems. The work spans statistical analysis, predictive modeling, scientific reasoning, and data-driven insight quality, with direct feedback that informs the next generation of AI models.

Responsibilities

Evaluate AI-generated quantitative outputs, including statistical analysis, predictive modeling, scientific reasoning, and data-driven insights, with attention to technical accuracy and real-world validity.
Design and solve quantitative problems used to train and benchmark AI systems across areas such as forecasting, experimental analysis, optimization, and statistical inference.
Write clear technical explanations and provide well-documented analytical code.
Deliver feedback that directly influences how future AI models for quantitative reasoning are developed.

Requirements

2+ years of hands-on experience in a quantitative role or research environment, such as data science, statistics, economics, finance, physics, biology, epidemiology, operations research, or a closely related area.
Some coding experience, including comfort writing and reviewing analytical code end-to-end.
Practical experience with statistical methods, predictive modeling, and experiment design, including examples such as A/B testing, hypothesis testing, regression, classification, and time-series forecasting.
Fluency in English (native or bilingual) with strong writing skills.
A bachelor’s degree in a quantitative field is preferred (Statistics, Computer Science, Mathematics, Engineering, or similar). A master’s or PhD is a plus.
Relevant credentials are a plus, such as Kaggle Competition ranking or AWS/GCP ML certifications (or equivalent demonstrated expertise).

Technologies

PayPal
AWS
GCP
Kaggle

Compensation and Work Setup

Location: New York, NY (remote)
Pay: USD 50 - 100 per hourly, with competitive hourly project rates up to $60 USD per hour
Experience level: 2+ years

Benefits

Fully remote: work from anywhere in the US, Canada, UK, Ireland, Australia, and New Zealand.
Flexible schedule: choose which projects you take on and when you work.
Higher-paying opportunities: available based on strong performance.
Impact: contribute to AI systems built to reason about data and analytics.

Note: Payment is made via PayPal. DataAnnotation will never ask you for money. This job is only available to applicants in the US, Canada, UK, Ireland, Australia, and New Zealand.

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Today

Machine Learning Engineer - AI Trainer

DataAnnotation Washington, DC 🇺🇸 Remote

Washington, DC

Today

$50 - $100/hr Full time Ai Ml Ai Model Training Ai Trainer

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Job Description

DataAnnotation is seeking an experienced Machine Learning Engineer - AI Trainer to evaluate and support the training of AI systems focused on quantitative reasoning. This fully remote role involves reviewing AI-generated analytical work, building training and benchmark problems, and providing feedback that improves model performance on real-world data tasks.

Work is paid hourly and becomes available after you pass an assessment. Projects can be selected based on your schedule, with payment handled via PayPal.

Responsibilities

Review AI-generated quantitative outputs, including statistical analysis, predictive modeling, scientific reasoning, and data-driven insights, ensuring technical accuracy and real-world validity.
Create and solve quantitative problems used to train and benchmark AI systems, covering areas such as forecasting, experimental analysis, optimization, and statistical inference.
Produce clear technical explanations and maintain well-documented analytical code.
Deliver feedback that informs the next iteration of AI models built for quantitative reasoning.

Requirements

2+ years of hands-on experience in a quantitative role or research environment, such as data science, statistics, economics, finance, physics, biology, epidemiology, operations research, or an adjacent field.
Some coding experience, with comfort writing and reviewing analytical code end-to-end.
Practical experience with statistical methods, predictive modeling, and experiment design, including examples like A/B testing, hypothesis testing, regression, classification, and time-series forecasting.
Fluency in English (native or bilingual) with strong writing skills.
A bachelor’s degree in a quantitative field is preferred (Statistics, Computer Science, Mathematics, Engineering, or similar). A master’s or PhD is a plus.
Relevant credentials are a plus, such as Kaggle Competition ranking, AWS/GCP ML certifications, or equivalent demonstrated expertise.

Benefits

Fully remote: work from anywhere in the US, Canada, UK, Ireland, Australia, and New Zealand.
Flexible schedule: choose which projects you take on and when you work.
Competitive pay: projects are paid hourly, with up to $60 USD per hour for high-performing projects. Salary range listed is USD 50 - 100 per hourly.
Impact: contribute to AI systems that reason about data and analytics.

Location: Washington, DC (remote). Payment: via PayPal. DataAnnotation states that they will never ask candidates for money. Availability: this role is limited to candidates in the US, Canada, UK, Ireland, Australia, and New Zealand.

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1d ago

Sr Lead Machine Learning Engineer

Capital One McLean, VA

McLean, VA

1d ago

$230k - $262k/yr Full time Ai Ml Artificial Intelligence Automation

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Job Description

Capital One is seeking a Sr Lead Machine Learning Engineer to join an Agile team focused on productionizing machine learning applications and systems at scale. The role involves designing, developing, and deploying ML models and infrastructure for real-time decisioning across customers’ credit journeys.

Responsibilities

Design, build, and/or deliver machine learning models and components to address real-world business needs in collaboration with Product and Data Science teams.
Drive ML infrastructure decisions based on ML modeling fundamentals, including model selection, data and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation.
Solve complex problems through application development and testing, building and validating ML models, and automating tests and deployment.
Collaborate within a cross-functional Agile team to create and enhance software supporting advanced big data and ML applications.
Retrain, maintain, and monitor models in production to support continued performance and reliability.
Leverage and/or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
Construct optimized data pipelines that provide inputs for ML models.
Apply continuous integration and continuous deployment best practices, including test automation and monitoring, to support successful deployment of ML models and application code.
Ensure code is managed to reduce vulnerabilities, keep models well-governed from a risk perspective, and apply Responsible and Explainable AI best practices.
Use programming languages such as Python, Scala, or Java.

Requirements

Bachelor’s Degree.
8+ years of experience designing and building data-intensive solutions using distributed computing (internship experience does not apply).
4+ years of experience programming with Python, Scala, or Java.
3+ years building, scaling, and optimizing ML systems.
2+ years leading teams developing ML solutions.

Technologies

Python, Kubernetes, Scala, Java
AWS, Azure, Google Cloud Platform
scikit-learn, PyTorch, Dask, Spark, TensorFlow

Preferred Qualifications

Master’s or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field.
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform.
4+ years of on-the-job experience with an industry recognized ML framework including scikit-learn, PyTorch, Dask, Spark, or TensorFlow.
3+ years of experience developing performant, resilient, and maintainable code.
3+ years of experience with data gathering and preparation for ML models.
3+ years of people management experience.
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents.
3+ years of experience building production-ready data pipelines that feed ML models.
Ability to communicate complex technical concepts clearly to a variety of audiences.
Experience leveraging interactive AI tooling to accelerate productivity using capabilities beyond basic code completion.

Location and Salary

McLean, VA (onsite). Salary range: USD 229,900 - 262,400 per year.

Benefits

Eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).
Comprehensive, competitive, and inclusive set of health, financial and other benefits.

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2d ago

Sr. Lead Machine Learning Engineer

Capital One Richmond, VA

Richmond, VA

2d ago

$209k - $239k/yr Full time Ai Ml Artificial Intelligence Data Analysis

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Job Description

Capital One is seeking a Sr. Lead Machine Learning Engineer in Richmond, VA (onsite) to productionize machine learning applications and systems at scale as part of an Agile team.

Responsibilities

Design, build, and deliver ML models and components that address real business problems in collaboration with Product and Data Science
Guide ML infrastructure decisions using knowledge of modeling tradeoffs, including model selection, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
Develop and validate ML solutions by writing and testing application code, building model workflows, and automating tests and deployment
Work in a cross-functional Agile team to create and enhance software enabling state-of-the-art big data and ML applications
Retrain, maintain, and monitor models in production
Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
Construct optimized data pipelines to supply ML models
Apply CI/CD best practices, including test automation and monitoring, to support successful releases of ML models and application code
Ensure well-managed code to reduce vulnerabilities, uphold risk governance for models, and follow Responsible and Explainable AI best practices
Use programming languages such as Python, Scala, or Java

Requirements

Bachelor’s Degree
8+ years experience designing and building data-intensive solutions using distributed computing (internship experience does not apply)
4+ years programming with Python, Scala, or Java
3+ years building, scaling, and optimizing ML systems
2+ years leading teams developing ML solutions
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
4+ years on-the-job experience with an industry-recognized ML framework: scikit-learn, PyTorch, Dask, Spark, or TensorFlow
3+ years developing performant, resilient, and maintainable code
3+ years experience with data gathering and preparation for ML models
3+ years of people management experience
3+ years building production-ready data pipelines that feed ML models
Ability to communicate complex technical concepts to a variety of audiences
Experience using interactive AI tooling to accelerate productivity, including capabilities beyond basic code completion

Technologies

Python
Scala
Java
AWS, Azure, Google Cloud Platform
scikit-learn, PyTorch, Dask, Spark, TensorFlow

Benefits

Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
Comprehensive, competitive, and inclusive set of health, financial and other benefits supporting total well-being

Preferred Qualifications

Master’s or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
Experience using interactive AI tooling to accelerate productivity beyond basic code completion

Salary

USD 209,000 - 238,500 per year for Richmond, VA.

Additional Information

Applications are expected to remain open for a minimum of 5 business days
No agencies please
Capital One is an equal opportunity employer (EOE) committed to non-discrimination in compliance with applicable federal, state, and local laws
Capital One promotes a drug-free workplace
Capital One may consider qualified applicants with a criminal history consistent with applicable laws
Accommodation requests: 1-800-304-9102 or RecruitingAccommodation@capitalone.com
Recruiting technical support: Careers@capitalone.com
Capital One does not provide, endorse, or guarantee third-party products, services, educational tools, or other information available through this site
Capital One Financial is made up of several different entities; positions may correspond to different entities depending on location

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2d ago

Principal Machine Learning Engineer

On behalf of Next Deavor New York, NY

New York, NY

2d ago

$200k - $250k/yr Full time Ai Ml Artificial Intelligence Data Pipeline

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Job Description

The Principal Machine Learning Engineer will take ownership of machine learning infrastructure that supports real-time compliance enforcement systems, spanning the full lifecycle from training and evaluation through production serving. This is a hybrid role based in the New York, NY area (3 days onsite).

Location

New York, NY (hybrid), with 3 days onsite in the New York City Metro area.

Compensation

USD 200,000 - 250,000 per year.

Role Summary

Own and evolve the systems that enable real-time compliance enforcement, driving reproducible model training, robust evaluation workflows, and low-latency production inference. The position also includes safe model update practices, monitoring for drift, and creating repeatable processes to adapt models to new domains and customer needs.

Responsibilities

Build and own training pipelines, including data preparation, reproducible fine-tuning runs, experiment tracking, and release automation
Develop evaluation infrastructure with automated eval runs, regression gates, dashboards, and dataset versioning
Own model serving in production, including low-latency inference, batching, optimization, autoscaling, and cost management
Ship model updates safely using versioning, canarying, rollback, and drift monitoring
Create repeatable workflows to adapt models to new domains and customer needs
Convert expert labels and reviewer feedback into clean training and evaluation datasets
Set the engineering bar for ML infrastructure as the team grows

Requirements

8+ years of software engineering experience, including 4+ years building infrastructure for ML or LLM systems in production
Hands-on experience with the modern LLM stack: PyTorch, distributed training, fine-tuning at scale (for example LoRA and SFT), and inference engines such as vLLM or TensorRT-LLM
Experience building eval harnesses, regression gates, or dataset pipelines; strong understanding of precision, recall, and calibration
Proven ownership of production model serving with real latency, reliability, and cost constraints
Strong fundamentals in Python, containers, CI/CD, cloud infrastructure, and observability
Ability to scope work, ship frequently, and make pragmatic build-vs-buy decisions
Experience collaborating closely with research partners and defining clear interfaces

Technologies

PyTorch
LoRA
SFT
vLLM
TensorRT-LLM
Python
containers
CI/CD
cloud infrastructure
observability

Additional Qualifications

Experience productionizing small or specialized language models
Experience with structured-output serving or constrained decoding in production
Prior work in regulated or high-stakes domains (fintech, healthcare, legal, trust and safety)
Experience deploying models into customer-controlled environments

Application Details

This role may fill quickly. Submit your resume to be considered.

Pay: $200,000.00 - $250,000.00 per year

Work Location: Hybrid remote in New York, NY 10001

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3d ago

Machine Learning Engineer

H&R Block St. Louis, MO

St. Louis, MO

3d ago

$91k - $146k/yr Full time Ai Ml Artificial Intelligence Data Science

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Job Description

The Machine Learning Engineer at H&R Block designs, develops, and deploys machine learning models, including generative AI models, to address business needs. This onsite role in Missouri focuses on model development, data preparation, performance optimization, and close collaboration with MLOps and cross-functional partners to bring ML capabilities into production systems.

Responsibilities

Design, build, and deploy machine learning models, including generative AI models, for complex business problems.
Collaborate with teammates to preprocess, clean, and analyze structured and unstructured data for machine learning pipelines.
Tune and optimize models to improve accuracy, efficiency, and scalability in production environments.
Work with MLOps Engineers to support deployment, monitoring, and ongoing maintenance of machine learning models.
Keep current with advances in machine learning, deep learning, and generative AI, applying relevant improvements as appropriate.
Support a collaborative engineering culture through knowledge sharing, participation in code reviews, and involvement in team initiatives.

Requirements

Bachelor’s degree in Computer Science, Statistics, Mathematics, or a related field (or equivalent experience).
3 years total experience.
Hands-on experience with machine learning frameworks such as scikit-learn, TensorFlow, PyTorch, and spaCy.
Proficiency in Python and familiarity with machine learning development workflows.
Strong analytical and problem-solving skills with attention to detail.
Effective communication and collaboration skills in a team-oriented environment.
Experience with generative AI is a plus.

Technologies

Python
scikit-learn
TensorFlow
PyTorch
spaCy

Preferred Qualifications

Master’s or PhD in Computer Science, Statistics, Mathematics, or a related field (or equivalent experience).
Hands-on experience building and deploying machine learning models for real time inference.

Compensation and Location

Location: Missouri (onsite)

Base pay range: USD 91,000 - 145,600 per yearly.

Local minimum wage laws apply. The posted range is provided pursuant to local requirements. Individual pay decisions depend on job-related factors including experience, education, skill, performance, and geographic location where work is performed. Successful candidates may be able to participate in one or more incentive compensation or short-term incentive plans, which could generate additional earnings according to the terms of each plan.

Benefits

Competitive pay
Comprehensive benefits
Support for life both in and outside of work
Medical and prescription drug coverage for qualifying associates and/or eligible dependents
H&R Block Retirement Savings Plan (401(k) Plan)
Employee Assistance Program
(virtual) fitness center programs
Associate discount program
Associate Tax Prep benefit