Machine Learning AI Jobs

Discover the latest remote and onsite Machine Learning AI roles across top active AI companies. Updated hourly.

Check out 35 new Machine Learning AI roles opportunities posted on The Homebase

Senior AI Scientist

New
Top rated
You.com
Full-time
Full-time
Posted

Design and develop AI applications primarily in Python, run evaluations to validate models and package solutions for Kubernetes, AWS, or customer on-premises clusters. Lead discovery sessions, guide pilot projects, and ensure successful deployments, collaborating mostly remotely with occasional on-site workshops. Monitor system performance and reliability, add to logging, billing and auth services, and build internal tooling to automate repetitive tasks. Provide feedback on patterns, pain points, and reusable modules to the core product team to influence the future direction of the AI platform.

$165,000 – $200,000
Undisclosed
YEAR

(USD)

San Francisco or New York, United States
Maybe global
Hybrid
Python
Kubernetes
AWS
LLM
Machine Learning

Freelance Machine Learning AI Trainer (Python)

New
Top rated
Mindrift
Part-time
Full-time
Posted

Design original computational STEM problems simulating real scientific workflows. Create problems requiring Python programming to solve that are computationally intensive and cannot be solved manually within reasonable timeframes. Develop problems that require non-trivial reasoning chains and creative problem-solving approaches. Verify solutions using Python with standard libraries such as numpy, pandas, scipy, and sklearn. Document problem statements clearly and provide verified correct answers. Collaborate on AI projects aligned with the candidate's skills on their own schedule, contributing from creating training prompts to refining model responses.

$58 / hour
Undisclosed
HOUR

(USD)

France
Maybe global
Remote
Python
NumPy
Pandas
Scikit-learn
Machine Learning

Data Scientist - Network Value

New
Top rated
Plaid
Full-time
Full-time
Posted

Perform ad-hoc and strategic analyses to uncover opportunities for improved business outcomes and translate complex questions into actionable analytics projects. Design and maintain scalable data models and dashboards that increase visibility into core systems and drive operational excellence. Build and iterate on machine learning prototypes to power insight-driven products and unlock new sources of customer and business value. Define and track OKRs that quantify progress toward key business goals, ensuring alignment and accountability across teams. Design and analyze experiments to guide product decisions and optimize feature launches. Champion a data-first culture by promoting analytical rigor and evidence-based decision-making across the organization.

$162,000 – $222,000
Undisclosed
YEAR

(USD)

San Francisco, United States
Maybe global
Onsite
Python
SQL
Machine Learning
Data Pipelines
Data Modeling

Group Product Manager - Noida

New
Top rated
Level AI
Full-time
Full-time
Posted

Own the platform portfolio including Agentic AI and define and execute the product roadmap for Agentic AI aligning with company strategy and customer needs. Partner with Engineering and Data teams to scale services powering large-scale AI workloads. Translate complex data and usage patterns into clear business insights and KPIs for customers and internal stakeholders. Coach and mentor the team and own their impact. Work closely with GTM and customer teams to drive customer value.

Undisclosed

()

Noida, India
Maybe global
Hybrid
Python
AI
OpenAI API
Machine Learning
Product Management

Product Designer, Monetization

New
Top rated
Grammarly
Full-time
Full-time
Posted

As an Applied Research Scientist on the Agents team, you will develop state-of-the-art tools for correcting, improving, and enhancing written English using various NLP, ML, and DL technologies. You will productize and ship these features into Superhuman's product offerings, used by millions daily. You will stay up-to-date with the latest research trends that could improve the product and contribute to the research strategy and technical culture of the company. Additionally, you will help attract professionals in the industry to build a best-in-class research team creating a state-of-the-art writing and communication assistant.

Undisclosed

()

Berlin, Germany
Maybe global
Hybrid
Python
Machine Learning
Deep Learning
Generative AI
Large Language Models (LLMs)

Marketing Intern - Seoul

New
Top rated
Dataiku
Intern
Full-time
Posted

Help users discover and master the Dataiku platform through user training, office hours, demos, and ongoing consultative support. Analyse and investigate various kinds of data and machine learning applications across industries and use cases. Provide strategic input to the customer and account teams that help our customers achieve success. Scope and co-develop production-level data science projects with our customers. Mentor and help educate data scientists and other customer team members to aid in career development and growth.

Undisclosed

()

Seoul, South Korea
Maybe global
Hybrid
Python
JavaScript
SQL
PySpark
Machine Learning

Marketing Intern - Tokyo

New
Top rated
Dataiku
Intern
Full-time
Posted

Help users discover and master the Dataiku platform through user training, office hours, demos, and ongoing consultative support. Analyse and investigate various kinds of data and machine learning applications across industries and use cases. Provide strategic input to the customer and account teams that help our customers achieve success. Scope and co-develop production-level data science projects with our customers. Mentor and help educate data scientists and other customer team members to aid in career development and growth.

Undisclosed

()

Tokyo, Japan
Maybe global
Hybrid
Python
JavaScript
SQL
PyTorch
Spark

Senior Software Engineer, Pilots

New
Top rated
Haydenai
Full-time
Full-time
Posted

As a Senior Software Engineer on the Pilots team, the responsibilities include delivering robust, thoroughly tested, and maintainable C++ code for edge and robotics platforms, designing, implementing, and owning prototype perception systems that may transition into production-grade solutions, constructing and refining real-time perception pipelines including detection, tracking, and sensor fusion, adapting and integrating ML and CV models for Hayden-specific applications, driving technical decision-making balancing prototyping speed with production readiness, collaborating with the Product team and cross-functional Engineering departments, and contributing to shared infrastructure, tooling, and architectural patterns as pilots mature into foundational products.

$200,454 – $260,590
Undisclosed
YEAR

(USD)

San Francisco, United States
Maybe global
Remote
C++
Computer Vision
Machine Learning
Python
TensorFlow

Computer Vision Engineer

New
Top rated
Harmattan AI
Full-time
Full-time
Posted

The responsibilities include conducting research on state-of-the-art Computer Vision methodologies and participating in the creation and curation of training and validation datasets. Performing statistical analyses and developing visualization tools to ensure data quality. Building and refining training pipelines and metrics to enhance model performance. Developing and optimizing Computer Vision algorithms for multiple robotics/aerospace projects. Implementing ML/CV models into production-ready environments, ensuring seamless integration with Harmattan AI’s systems, and conducting rigorous code reviews. Testing algorithms in real-world environments, developing monitoring tools, tracking model performance, and continuously improving deployed solutions. Working closely with software and simulation teams to align development with system requirements and communicating findings effectively to stakeholders.

Undisclosed

()

Paris, France
Maybe global
Onsite
Python
C++
Computer Vision
Machine Learning
Model Evaluation

ML Application Engineer (French-speaking)

New
Top rated
Neural Concept
Full-time
Full-time
Posted

The ML Application Engineer will run projects with customer engineering teams, analyze and process engineering data using the company's platform and Python libraries, and develop tailored solutions and workflows. They will apply machine- and deep-learning workflows to various engineering and physics problems, deliver proofs-of-concept to demonstrate the value of the technology in CAD, CAE, and manufacturing, and train customer teams to effectively use the methods and platform to ensure smooth AI adoption. The engineer will also work closely with developers to translate customer needs and feedback into product improvements.

Undisclosed

()

Lausanne, Switzerland
Maybe global
Remote
Python
PyTorch
TensorFlow
Deep Learning
Machine Learning

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[{"question":"What are Machine Learning AI jobs?","answer":"Machine Learning AI jobs involve building, training, and deploying models that enable computers to learn from data. These roles focus on developing systems that can recognize patterns, make predictions, and automate tasks. Professionals in these positions work with frameworks like TensorFlow, PyTorch, and scikit-learn to create solutions for code generation, bug detection, predictive analytics, and personalized experiences."},{"question":"What roles commonly require Machine Learning skills?","answer":"Machine Learning skills are essential for Machine Learning Engineers who build and deploy models, Data Scientists who develop predictive analytics, and Software Developers using AI-powered code tools. Quality Assurance Specialists implement ML-driven testing systems, while DevOps Engineers automate pipelines with ML tools. Security Specialists also use these skills to identify vulnerabilities and monitor code for threats."},{"question":"What skills are typically required alongside Machine Learning?","answer":"Alongside Machine Learning expertise, professionals need natural language processing knowledge, understanding of deep learning techniques, and familiarity with frameworks like TensorFlow and PyTorch. Experience with data analysis, pattern recognition, and model evaluation is crucial. Knowledge of CI/CD pipelines and DevOps practices helps implement ML in deployment automation. Programming skills and understanding of ML deployment technologies are also essential."},{"question":"What experience level do Machine Learning AI jobs usually require?","answer":"Machine Learning AI jobs typically require varying experience levels based on role complexity. Entry-level positions often seek familiarity with ML frameworks and basic model training. Mid-level roles demand practical experience implementing ML solutions and working with specific tools like TensorFlow or PyTorch. Senior positions require deep understanding of algorithms, deployment technologies, and integration of ML into production systems."},{"question":"What is the salary range for Machine Learning AI jobs?","answer":"The research provided doesn't specify salary ranges for Machine Learning AI jobs. Compensation typically varies based on factors including experience level, specific role (ML Engineer, Data Scientist, etc.), industry sector, company size, geographical location, and specialized expertise in particular frameworks or applications. Salaries often reflect the high demand for ML skills in the current market."},{"question":"Are Machine Learning AI jobs in demand?","answer":"Yes, Machine Learning AI jobs are in high demand across industries. Organizations are actively integrating ML into software development processes. The field is described as increasingly significant as companies seek refined software solutions. ML tools are now considered essential in modern development, particularly as pre-trained models democratize AI access. The application of ML across various development stages indicates broad and growing adoption."},{"question":"What is the difference between Machine Learning and Deep Learning in AI roles?","answer":"Machine Learning is the broader field where algorithms learn from data to make decisions or predictions. Deep Learning is a specialized subset using neural networks with multiple layers to process complex patterns. In AI roles, professionals using ML might work on various algorithms for different applications, while those focusing on Deep Learning typically handle more complex tasks like image recognition or natural language processing that require neural network architecture."}]