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Machine Learning Flexible Hours Jobs (NOW HIRING)

Itransition Group

NY · On-site

$100 - $140/hr

We are looking for a Machine Learning Researcher to design, develop, and evaluate predictive models ... Flexible working hours aligned to your schedule #J-18808-Ljbffr

Confie

Machine Learning Engineer

Addison, TX · On-site +1

$110K - $130K/yr

Flexible work options, including remote and hybrid opportunities, if eligible * Retirement Plan ... machine learning solutions on the Snowflake Cloud data warehouse platform using the Snowpark ...

Unissant

Machine Learning Engineer

Ashburn, VA · On-site

$110 - $170/hr

We are seeking a Machine Learning Engineer to join our team and support our client in Ashburn, VA ... Flexible in working extended hours. The above statements are intended to describe the general ...

Tapestry

Machine Learning Engineer

Mountain View, CA · On-site

About the role: We're looking for an early career Machine Learning Engineer to join our team. In ... Generous PTO and flexible hybrid work model * 401(k) with employer contribution * Professional ...

UNISSANT

Machine Learning Engineer

Ashburn, VA

We are seeking a Machine Learning Engineer to join our team and support our client in Ashburn, VA ... Flexible in working extended hours. The above statements are intended to describe the general ...

CoVar

Machine Learning Engineer

Durham, NC

Machine Learning Engineer About CoVar CoVar is a small AI/ML R&D software company in Durham, NC ... Flexible work schedule * Tuition support * PTO and paid holidays Visit us: www.covar.com

Orchard LLC

Machine Learning Engineer

... hours when required * Support troubleshooting and resolution of issues affecting machine learning ... applications and associated web or data capabilities. Requirements * Bachelor's degree in Computer ...

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Machine Learning Flexible Hours information

See salary details

$25.5K

$42.6K

$88K

How much do machine learning flexible hours jobs pay per year?

As of Aug 27, 2026, the average yearly pay for machine learning flexible hours in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What does a machine learning job with flexible hours involve?

A machine learning job with flexible hours typically allows professionals to set their own work schedules instead of adhering to a strict 9-to-5 routine. These roles still require expertise in data analysis, algorithm development, and model training, but provide the freedom to work remotely or during non-traditional hours. Flexible arrangements are common in tech companies and startups, enabling better work-life balance while meeting project deadlines and collaborating with teams virtually.

What are the key skills and qualifications needed to thrive as a machine learning engineer with flexible hours?

To thrive as a Machine Learning Engineer with flexible hours, you need a solid background in computer science, statistics, and mathematics, often supported by a relevant degree and experience in developing machine learning models. Familiarity with technical tools such as Python, TensorFlow, PyTorch, and cloud computing platforms, as well as relevant certifications, is highly valuable. Strong problem-solving skills, self-motivation, and effective communication help you excel when working independently and collaborating remotely. These skills are crucial for delivering impactful solutions, maintaining productivity, and ensuring successful project outcomes in a flexible work environment.

How do flexible hours impact collaboration and project delivery in a machine learning role?

In a Machine Learning role with flexible hours, collaboration is typically managed through asynchronous communication tools and scheduled meetings to ensure team alignment. While this flexibility allows for better work-life balance and can boost productivity, it also requires clear communication and proactive planning to meet project deadlines. Team members often coordinate their core working hours for critical discussions or decision-making, and use shared platforms to track progress and share updates. Adapting to this structure can be a challenge at first, but it often leads to a more autonomous and motivated team environment.

What is the difference between Machine Learning Flexible Hours vs Data Scientist Flexible Hours?

AspectMachine Learning Flexible HoursData Scientist Flexible Hours
CredentialsDegree in Computer Science, Data Science, or related field; knowledge of ML frameworksDegree in Data Science, Statistics, or related field; proficiency in data analysis tools
Work EnvironmentTech companies, research labs, startups; project-basedBusiness analytics, research institutions, tech firms; collaborative teams
Industry UsageAI development, automation, predictive modelingData analysis, reporting, strategic decision-making

Both roles often offer flexible hours, but Machine Learning roles focus on developing algorithms and models, while Data Scientists analyze data to inform decisions. The choice depends on your skills and career goals within the data and AI industry.

More about Machine Learning Flexible Hours jobs

What cities are hiring for Machine Learning Flexible Hours jobs?

Cities with the most Machine Learning Flexible Hours job openings:

What states have the most Machine Learning Flexible Hours jobs?

States with the most job openings for Machine Learning Flexible Hours jobs include:

What job categories do people searching Machine Learning Flexible Hours jobs look for?

The top searched job categories for Machine Learning Flexible Hours jobs are:

Machine Learning Flexible Hours jobs near you
Infographic showing various Machine Learning Flexible Hours job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 63% Full Time, 25% Part Time, and 11% Contract. Highlights an 83% Physical, 1% Hybrid, and 16% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Machine Learning

NY • On-site

Itransition Group
IT Services • 1 - 5K employees
Learn more about Itransition Groupexternal

$100 - $140/hr

Other

Posted 22 days ago

Job description

We are looking for a Machine Learning Researcher to design, develop, and evaluate predictive models for financial markets. You will work at the intersection of quantitative research, machine learning, and real-world trading constraints, contributing to alpha generation and risk modeling.

office remote Poland

Requirements
  • 3+ years of relevant experience
  • Strong Python skills and experience with ML ecosystems (AWS Sagemaker, MLFlow)
  • Hands‑on experience working with tabular/time series data with usage of ML
  • Solid understanding of machine learning fundamentals: Supervised learning, feature engineering, model evaluation; Overfitting, regularization, cross‑validation
  • Knowledge of statistical methods and probability theory
  • Experience with experiment design and offline evaluation
  • Ability to work with large datasets and build efficient data processing pipelines
  • Familiarity with SQL and data querying
  • Strong analytical and problem‑solving mindset
  • Ability to clearly communicate findings and trade‑offs
  • Ownership of tasks from research to implementation
  • Curiosity and willingness to explore new approaches
  • Level of English enough for efficient technical and business communication with native speakers
Nice to have
  • Experience in financial machine learning, quantitative finance, or trading systems
  • knowledge of signal generation, alpha research, portfolio construction or risk modeling
  • Experience with: Deep learning for tabular/time series data (Transformers, RNNs, etc.); Probabilistic modeling or Bayesian methods
  • Hands‑on experience with production ML systems (MLOps, monitoring, retraining)
  • Ability to define research direction and identify high‑impact opportunities
  • Ability to translate business problems into ML solutions
Responsibilities
  • Develop and validate machine learning models for financial time series and cross‑sectional data
  • Conduct research on alpha signals, feature engineering, and predictive modelling techniques
  • Design experiments and backtesting frameworks with proper statistical rigor
  • Work with large‑scale structured and unstructured financial datasets
  • Collaborate with engineering teams to deploy models into production pipelines
  • Analyze model performance, stability, and robustness under changing market conditions
  • Improve data pipelines, labeling strategies, and evaluation methodologies
We offer
  • Projects for such clients as PayPal, Wargaming, Xerox, Philips, Adidas and Toyota
  • Competitive compensation that depends on your qualification and skills
  • Career development system with clear skill qualifications
  • Flexible working hours aligned to your schedule
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