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Temporary Machine Learning Engineer Jobs (NOW HIRING)

Machine Learning Engineer Our client, a financial company, is looking for a Machine Learning Engineer for their McLean, VA location. Requirements: * Python, AWS, Kubernetes, Kubeflow, MLOps, ML ...

Machine Learning Engineer Washington, DC (Hybrid) About the Role: We are seeking a highly skilled Machine Learning Engineer to join our core AI team. In this role, you will focus on deploying ...

Machine Learning Engineer (AI Data Trainer) About the Role What if your expertise in machine learning could directly influence how the next generation of AI models reason, plan, and solve complex ...

Machine Learning Engineer Location: Fremont, CA Duration: 12+ Months Tesla/ $65 About the Role Our direct client is seeking a highly skilled Machine Learning Engineer to join their Software Machine ...

The Machine Learning Engineer will leverage their strong technical background and knowledge to support highly scalable machine learning-based applications, including both pipelines and services ...

Poesis Machine Learning Engineer At Poesis, machine learning and artificial intelligence open the door to improved alpha discovery, higher quality decision-making and intelligent risk management. We ...

Machine Learning Engineer LeanData helps the world's fastest-growing companies automate, simplify, and accelerate revenue. We are looking for a curious and innovative Machine Learning Engineer to ...

As a Machine Learning Engineer, you will help build and operate production systems that power fraud detection and risk-related products. You'll work closely with data scientists and engineers to ...

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Temporary Machine Learning Engineer information

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$31.5K

$128.8K

$193.5K

How much do temporary machine learning engineer jobs pay per year?

As of Jun 17, 2026, the average yearly pay for temporary machine learning engineer in the United States is $128,769.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,500.00 and $155,000.00 per year, depending on experience, location, and employer.

What is the difference between Temporary Machine Learning Engineer vs Data Scientist?

AspectTemporary Machine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related fields; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related fields; strong analytical skills
Work EnvironmentProject-based, often contract roles in tech or finance companiesResearch and analysis-focused, in tech, finance, or healthcare sectors
Employer UsageUsed for short-term ML projects, model deployment, or prototypingUsed for data analysis, insights, and predictive modeling

Temporary Machine Learning Engineers focus on implementing and deploying ML models on a short-term basis, often within project deadlines. Data Scientists analyze data to generate insights and develop models but may have a broader scope. Both roles require strong technical skills, but their primary functions differ in scope and application.

What engineers make $500,000?

Senior engineers in fields such as software, data engineering, and machine learning can earn $500,000 or more annually, especially with experience, specialized skills, and stock options. High compensation often involves leadership roles, working at large tech companies, or in high-demand industries with advanced technical expertise.

Which 5 jobs will survive AI?

For a Temporary Machine Learning Engineer, roles that require complex problem-solving, creativity, and human judgment are more likely to survive AI automation, such as data science, AI ethics, software architecture, technical consulting, and specialized research. These jobs often involve skills in critical thinking, domain expertise, and collaboration that are difficult for AI to replicate fully.

Which 3 jobs will survive AI?

For a Temporary Machine Learning Engineer, roles that require complex problem-solving, creativity, and human interaction are more likely to persist despite AI advancements. These include jobs in healthcare, such as medical professionals; skilled trades like electricians or plumbers; and roles in education that involve personalized instruction. Such positions often require emotional intelligence, adaptability, and hands-on skills that AI cannot easily replicate.

Can I learn ML in 3 months?

A Temporary Machine Learning Engineer can acquire foundational machine learning skills in three months with intensive study, focusing on programming (Python), algorithms, and tools like scikit-learn or TensorFlow. However, mastering complex models and gaining practical experience typically requires longer, ongoing learning and project work.
More about Temporary Machine Learning Engineer jobs
What cities are hiring for Temporary Machine Learning Engineer jobs? Cities with the most Temporary Machine Learning Engineer job openings:
What are the most commonly searched types of Machine Learning Engineer jobs? The most popular types of Machine Learning Engineer jobs are:
What states have the most Temporary Machine Learning Engineer jobs? States with the most job openings for Temporary Machine Learning Engineer jobs include:
Infographic showing various Temporary Machine Learning Engineer job openings in the United States as of June 2026, with employment types broken down into 78% Full Time, and 22% Contract. Highlights an 89% In-person, and 11% Remote job distribution, with an average salary of $128,769 per year, or $61.9 per hour.

Machine Learning Engineer

Kanak Elite Services Inc

Bodega Bay, CA โ€ข Remote

Contractor

Posted yesterday


Job description

Hello There,

My name is Himanshu Sharma, and I serve as the Recruitment Lead at Kanak-IT INC. I am reaching out to share an excellent career opportunity for the role of Machine Learning Engineerย with our esteemed client. If you are interested then please share your updated resume at Himanshu01@kanakits.com .

Job Description

Title:ย ย Machine Learning Engineer
Location:ย  South San Francisco, CAย  -ย hybrid role in Bay Arear
Position Type:ย  Contractย 
ย 

Note:ย DO NOT SEND WITHOUT MOLECULAR EXPERIENCE,ย 

Work on ML workflows for molecular property prediction & generative modeling to accelerate drug discovery. 3โ€“5 yrs esp. or PhD with publications in molecular design.

Must have Masters or PH.D. Must have experience in working environment or while getting Masterโ€™s or no to very little work exp with PH.Dย  in Molecular design. Need to have portfolio of their work or be published. Find me Machine Learning with Molecular experience in Bay Area or someone who will relocate as last resort.ย 
MindSource is looking for a Machine Learning Engineer to join our client's team in South San Francisco, CA.ย  They will be developing and deploying advanced computational methods for molecular design.ย  This is a 12-month hybrid contract.ย ย 

About the Role

  • Build pipelines forย probabilistic molecular property predictionย andย Bayesian acquisitionย to power active learningโ€“driven drug discovery.
  • Engineer workflows forย molecular generative modelingย and other innovative design approaches.
  • Collaborate with machine learning scientists, engineers, computational chemists, and biologists.
  • Partner with therapeutic development teams to analyze existing molecules and design new candidates.
  • Contribute to ongoing initiatives while driving new research directions.

Qualifications

  • PhD in Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics, or related quantitative field โ€” OR MS + 3+ years of relevant industry experience.
  • Demonstrated expertise inย production-ready ML workflowsย (e.g., PyTorch + Lightning + Weights & Biases).
  • Strong track record of achievement (e.g., high-impact first-author publication or equivalent).
  • Excellent written, visual, and verbal communication skills.

Preferred Experience

  • Knowledge of physical modeling (e.g., molecular dynamics) and cheminformatics (e.g., RDKit).
  • Background in molecular property prediction, computational chemistry, de novo drug design, medicinal chemistry, small molecule design, self-supervised learning, geometric deep learning, Bayesian optimization, probabilistic modeling, or statistical methods.
  • Hands-on experience withย Python, PyTorch, Torch Geometric, PyTorch Lightning, RDKit, and BoTorch.
  • Public portfolio of computational projects (e.g., GitHub).