Python AI Jobs

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

Check out 1009 new Python AI roles opportunities posted on The Homebase

Data Quality Specialist

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

Generate and validate high-quality data annotations based on guidelines and continuous feedback for the development and evaluation of AI models. Collaborate with the technical team to review and audit annotations, clarify requirements, share insights, and improve annotation processes, tools, and guidelines.

Undisclosed

()

Paris, France
Maybe global
Onsite
Python
Analytical Skills
Research
Data Quality
Annotation

Senior Software Engineering Director, Developer Experience

New
Top rated
Crusoe
Full-time
Full-time
Posted

As the Senior Director of Engineering for Developer Experience at Crusoe, you will own and drive the strategy, execution, and culture of the team responsible for how Crusoe's engineers and non-engineers build, ship, and operate software. Responsibilities include defining and executing the long-term vision for Crusoe's internal developer platform, which encompasses shared services, internal APIs, repositories, and self-service infrastructure to enable engineering teams to move quickly and confidently. You will also rapidly develop and productionize AI-powered tools for the entire company, creating and evangelizing best practices for productionizing AI-developed tools and evaluating SaaS purchases. Additionally, you will oversee the design, reliability, and continuous improvement of CI/CD pipelines, build systems, and deployment infrastructure to ensure safe and rapid scaling of engineering teams' shipping processes. Your role will also involve defining and driving organization-wide engineering productivity initiatives by establishing metrics, identifying bottlenecks, and implementing tooling and process improvements that enhance developer experience across Crusoe. People leadership is a key responsibility, including managing and growing a team of engineers and fostering a high-performance culture based on accountability, innovation, and continuous learning. Furthermore, you will collaborate with senior leaders across Engineering, Infrastructure, Security, and Product to align Developer Experience investments with company-wide engineering goals and priorities.

$301,750 – $355,000
Undisclosed
YEAR

(USD)

San Francisco, United States
Maybe global
Onsite
Python
JavaScript
CI/CD
Docker
Kubernetes

Deployed Engineer (Toronto)

New
Top rated
LangChain
Full-time
Full-time
Posted

The Deployed Engineer will co-architect and co-build production AI agents with customer engineering teams, own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations, help customers deploy and operate agent-based applications including conversational agents, research agents, and multi-step workflows, and advise customers post-sale on architecture, best practices, and roadmap-level decisions. They will also run technical demos, trainings, and workshops for developer audiences, surface field feedback, contribute reusable patterns, cookbooks, and example code that scale across customers, and occasionally contribute code upstream when it meaningfully improves customer outcomes.

Undisclosed

()

Toronto, Canada
Maybe global
Remote
Python
JavaScript
Prompt Engineering
MLOps
AWS

Senior Product Manager – Data & Quality

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

Partner with frontier AI research labs to design datasets and environments that improve model performance. Lead technical conversations with customer researchers to understand model capabilities, failure modes, data requirements, and success criteria. Probe model behavior through systematic evaluation to uncover weaknesses and identify high-impact data interventions. Design evaluation frameworks, calibration processes, and quality rubrics that establish measurable project success metrics. Develop technical specifications for data projects that balance research rigor with operational feasibility. Serve as thought partner to customer research teams throughout the sales cycle, building trust and credibility. Stay current on frontier AI research, RL environment design, post-training techniques, and evaluation methodologies.

$172,000 – $300,000
Undisclosed
YEAR

(USD)

San Francisco, United States
Maybe global
Hybrid
Python
MLflow
MLOps
LangChain
Transformers

Software Development in Test Intern

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

Advance inference efficiency end-to-end by designing and prototyping algorithms, architectures, and scheduling strategies for low-latency, high-throughput inference. Implement and maintain changes in high-performance inference engines such as SGLang- or vLLM-style systems and Together's inference stack, including kernel backends, speculative decoding (e.g., ATLAS), and quantization. Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Design and operate reinforcement learning (RL) and post-training pipelines (including RLHF, RLAIF, GRPO, DPO-style methods, and reward modeling) where most of the cost is inference, jointly optimizing algorithms and systems. Make RL and post-training workloads more efficient with inference-aware training loops such as asynchronous RL rollouts and speculative decoding techniques. Use these pipelines to train, evaluate, and iterate on frontier models based on the inference stack. Co-design algorithms and infrastructure to tightly couple objectives, rollout collection, and evaluation with efficient inference, identifying bottlenecks in training engines, inference engines, data pipelines, and user-facing layers. Run ablations and scale-up experiments to study trade-offs between model quality, latency, throughput, and cost and integrate findings into model, RL, and system design. Profile, debug, and optimize inference and post-training services under production workloads. Drive roadmap items requiring real engine modifications, including changing kernels, memory layouts, scheduling logic, and APIs. Establish metrics, benchmarks, and experimentation frameworks to rigorously validate improvements. Provide technical leadership by setting technical direction for cross-team efforts at the intersection of inference, RL, and post-training. Mentor engineers and researchers on full-stack ML systems work and performance engineering.

$200,000 – $280,000
Undisclosed
YEAR

(USD)

San Francisco
Maybe global
Onsite
Python
PyTorch
TensorFlow
Reinforcement Learning
MLOps

Software Engineer, Internal Tools

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

Use proprietary software applications to provide input/labels on defined projects. Support and ensure the delivery of high-quality curated data. Play a pivotal role in supporting and contributing to the training of new tasks, working closely with the technical staff to ensure the successful development and implementation of cutting-edge initiatives/technologies. Interact with the technical staff to help improve the design of efficient annotation tools. Choose problems from corporate accounting fields that align with your expertise, providing rigorous solutions and model critiques where you can confidently provide detailed solutions and evaluate model responses. Regularly interpret, analyze, and execute tasks based on given instructions.

$45 – $100 / hour
Undisclosed
HOUR

(USD)

Palo Alto, United States
Maybe global
Hybrid
Python
Prompt Engineering
Model Evaluation

Machine Learning Operations Engineer

New
Top rated
Haydenai
Full-time
Full-time
Posted

Optimize orchestration processes to ensure efficient deployment and management of AI models. Implement cost-saving strategies to minimize infrastructure expenses while maximizing performance. Upgrade throughput to enhance scalability and responsiveness of AI systems. Collaborate with cross-functional teams to identify bottlenecks and implement solutions to improve workflow efficiency. Ship new features and updates rapidly while maintaining high levels of quality and reliability. Deploy and monitor machine learning models produced by deep learning engineers. Design, deploy, and maintain performant and scalable processes for data acquisition and manipulation to enhance dataset accessibility. Participate actively in the team's software development process, including design reviews, code reviews, and brainstorming sessions. Maintain accurate and updated software development documentation.

$135,699 – $190,000
Undisclosed
YEAR

(USD)

San Francisco, United States
Maybe global
Remote
Python
C++
PyTorch
TensorFlow
MLflow

Software Engineering Manager, Autonomous

New
Top rated
Magical
Full-time
Full-time
Posted

As the Engineering Manager on the Autonomous team, you will lead and scale a high-calibre team of engineers dedicated to defining the future of AI agent development and advancing AI and backend systems. You will oversee the technical roadmap for the Autonomous team, translating architectural complexity into clear product strategies. You will mentor a diverse group of engineers, supporting their professional growth. You will partner closely with Product and Design to ensure the agent-building tools remain intuitive while supporting technical capabilities. You will champion a 'show > tell' culture by ensuring rapid shipping with a high standard for technical stability and user experience. You will clear technical and operational roadblocks to ensure the team operates with high agency and clarity.

Undisclosed

()

San Francisco, United States
Maybe global
Hybrid
Python
Kubernetes
Docker
AWS
CI/CD

Software Engineering Manager, Autonomous

New
Top rated
Magical
Full-time
Full-time
Posted

As an Engineering Manager on the Autonomous team, you will lead and scale a high-calibre team of engineers dedicated to defining the future of AI agent development and advancing AI and backend systems. You will oversee the technical roadmap for the team by translating architectural complexity into clear product strategies, mentor and support the professional growth of a diverse group of engineers, and partner closely with Product and Design to ensure the agent-building tools remain intuitive and technically robust. You will champion a "show > tell" culture to ensure rapid shipping while maintaining high technical stability and user experience standards, and clear technical and operational roadblocks to enable the team to operate with high agency and clarity. You will act as the bridge between product vision and technical execution.

Undisclosed

()

Toronto, Canada
Maybe global
Hybrid
Python
Docker
Kubernetes
AWS
CI/CD

AI Tooling Frontend Engineer - Helix Team

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

Design and build intuitive web interfaces for robot data annotation, datasets visualization, and experiment tracking. Utilize data-driven techniques to optimize interfaces for efficiency and fast iteration cycles. Integrate AI models to automate manual tasks. Work together with AI researchers, robot operators, and annotators to support new user experiences.

$150,000 – $250,000
Undisclosed
YEAR

(USD)

San Jose, United States
Maybe global
Onsite
TypeScript
React
AWS
GCP
Python

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[{"question":"What are Python AI jobs?","answer":"Python AI jobs involve developing intelligent systems using machine learning, deep learning, and natural language processing. These positions typically focus on creating algorithms, building predictive models, and implementing AI solutions across industries like finance, healthcare, and transportation. Professionals work with frameworks such as TensorFlow, PyTorch, and scikit-learn to develop AI applications that can analyze data, make predictions, and automate complex tasks."},{"question":"What roles commonly require Python skills?","answer":"Common roles requiring Python skills include AI developers, machine learning engineers, data scientists, and data analysts. Web developers building AI-enabled applications also need Python proficiency. The skill is in high demand across fintech, healthcare, travel, and transportation sectors. These professionals use Python for everything from data preparation and model building to deploying AI solutions and integrating with third-party services."},{"question":"What skills are typically required alongside Python?","answer":"Alongside Python, employers typically require knowledge of AI frameworks like TensorFlow, PyTorch, and scikit-learn. Proficiency with data libraries including NumPy, pandas, and Matplotlib is essential. Additional valued skills include machine learning concepts, data structures, algorithms, API development with Flask, Jupyter Notebooks for prototyping, and version control systems. Understanding of specific AI domains like natural language processing or computer vision is often needed for specialized roles."},{"question":"What experience level do Python AI jobs usually require?","answer":"Python AI jobs typically require foundational to intermediate programming proficiency. Candidates should understand core concepts like variables, loops, conditional logic, functions, and object-oriented programming. For entry-level positions, familiarity with basic AI libraries may suffice, while senior roles demand deeper expertise with advanced frameworks and problem-solving abilities. Most employers look for practical experience implementing AI solutions rather than just theoretical knowledge."},{"question":"What is the salary range for Python AI jobs?","answer":"Python AI jobs typically offer competitive compensation reflecting the high-value intersection of programming and artificial intelligence skills. Entry-level positions start higher than standard development roles, while experienced professionals command premium salaries. Compensation varies by location, industry, and specialization, with finance and technology sectors often paying more. AI specialists working with advanced deep learning models or specialized domains like computer vision tend to earn at the higher end of the range."},{"question":"Are Python AI jobs in demand?","answer":"Python AI jobs are in extremely high demand across industries. As businesses increasingly implement AI solutions, the need for skilled developers continues to outpace supply. The versatility of the language in handling data analysis, machine learning, and deployment makes it essential for companies building intelligent systems. This demand spans startups to enterprises, with particular growth in healthcare, finance, retail, and manufacturing sectors all seeking to leverage AI capabilities."},{"question":"What is the difference between Python and R in AI roles?","answer":"In AI roles, Python offers versatility and a comprehensive ecosystem for full development cycles, while R specializes in statistical analysis and visualization. Python excels at production-ready AI deployment with frameworks like TensorFlow and PyTorch, making it preferred for machine learning engineering. R provides superior statistical modeling tools beneficial for research-oriented data science. Python's syntax prioritizes readability and consistency, whereas R focuses on statistical computing with specialized packages for complex statistical operations."}]