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🔥 Senior Staff Software Engineer, Backend (ML Platform)

Posted about 17 hours ago

📍 United States

🧭 Full-Time

💸 232000.0 - 310000.0 USD per year

🔍 Software Development

🔧 Requirements

10+ years of experience designing, developing and launching backend systems at scale using languages like Python or Kotlin.
Strong experience leading multiple engineering teams to deliver high quality software
Track record of successfully leading engineering teams at both rapidly scaling startups and complex larger technology companies.
Expertise in synthesizing complex technical requirements, designs, trade-offs, and capabilities into clear decisions to influence ML & engineering direction
Extensive experience developing highly available distributed systems using technologies like AWS, MySQL, Spark and Kubernetes.
Experience building and operating online, real-time ML infrastructure including a model server and a feature store
Experience developing an offline environment for large scale data analysis and model training using technologies including Spark, Kubeflow, Ray, and Airflow
Experience delivering major features and system components

💡 Responsibilities

Set the multi-year, multi-team technical strategy for ML Platform and deliver it through direct implementation or broad technical leadership
Partner with technical leaders across the company to create joint roadmaps that will achieve business impacting goals through the advancement of machine learning
Act as a force-multiplier for your teams through your definition and advocacy of technical solutions and operational processes
You have an ownership mindset, and you will proactively champion investments in availability so that every project in your area achieves its availability targets
You will foster a culture of quality and ownership on your team by setting system design standards for your team, and advocating for them beyond your team through your writing and tech talks
You will help develop talent on your team by providing feedback and guidance, and leading by example

AWS Backend Development Leadership Project Management Python Apache Airflow Data Analysis Kotlin Kubeflow Kubernetes Machine Learning MySQL Software Architecture Cross-functional Team Leadership Data engineering Spark Communication Skills RESTful APIs DevOps

Posted about 17 hours ago

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🔥 Senior AI Engineer

Posted 6 days ago

📍 São Paulo, Rio Grande do Sul, Rio de Janeiro, Belo Horizonte

🧭 Full-Time

🔍 Software Development

🏢 Company: TELUS Digital Brazil

🔧 Requirements

Demonstrable experience in applied AI, with a foundation in machine learning, NLP, LLMs, and statistical analysis.
Strong understanding of the trade-offs between various generative AI models and the ability to choose the right model for specific use cases.
Experience with data embeddings and vector databases, understanding the trade-off between available options, and leveraging it to optimize data ingestion.
Experience in architecting and developing solutions that integrate generative AI with traditional software solutions with minimal to no oversight.
Experience building and testing a server-side platform for API development and orchestration.
Is proficient in the Python language and understands the trade-offs between multiple frameworks and patterns.
Skilled in creating and adjusting prompts for complex AI systems to meet diverse project requirements.
Familiarity with testing and evaluating AI systems using state-of-the-art methods and best practices.
Hands-on experience deploying software on leading cloud platforms and utilizing AI tools like AWS Bedrock, Azure AI Services, and Vertex AI.
Strong collaboration skills and ability to work alongside developers from multiple different areas.
Ability to communicate complex AI solutions and concepts effectively to technical and non-technical stakeholders.

💡 Responsibilities

Apply your knowledge of AI systems and software engineering to develop solutions that directly address and resolve business problems.
Partner with professionals from Data Science and Data Engineering to address complex technical challenges, ensuring that the latest and most effective Data & AI techniques are being utilized.
Take ownership of implementing and optimizing applied AI components, ensuring they meet project needs with high complexity and scale.
Navigate and manipulate generative AI models, including (but not limited to) LLMs, to create prompts and solutions tailored to specific use cases.
Develop and incorporate AI solutions while adhering to industry best practices, including moderation, security, monitoring, and compliance standards.
Understand and properly apply Responsible AI concepts in all the stages of the solution.
Lead the charge in designing, measuring, and evaluating AI model outputs, developing standard and custom metrics to ensure alignment with business objectives.
Translate AI research and PoCs into production-ready features, delivering robust and scalable AI components that integrate seamlessly with larger systems.
Drive the selection and application of appropriate evaluation metrics, ensuring that AI solutions are robust, unbiased, and meet all necessary performance standards.

AWS Python Artificial Intelligence Cloud Computing Data Analysis Kubeflow Machine Learning Algorithms API testing Data engineering Data Structures REST API CI/CD DevOps Microservices Software Engineering

Posted 6 days ago

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🔥 Lead Machine Learning Infrastructure Engineer - Infrastructure & Data

Posted 8 days ago

📍 United States

🧭 Full-Time

💸 185500.0 - 293750.0 USD per year

🔍 Software Development

🏢 Company: Upwork👥 501-1000💰 about 8 years ago🫂 Last layoff almost 2 years agoMarketplace Freelance Copywriting Peer to Peer

🔧 Requirements

Strong technical expertise in designing and building scalable ML infrastructure.
Experience with distributed systems and cloud-based ML platforms.
Proficiency in programming languages such as Python, Java, or Scala.
Deep understanding of ML workflows, including data pipelines, model training, and deployment.
Passion for innovation and eagerness to implement the latest advancements in ML infrastructure.
Strong problem-solving skills and ability to optimize complex systems for performance and reliability.
Collaborative mindset with excellent communication skills to work across teams.
Ability to thrive in a fast-paced, dynamic environment with evolving technical challenges.

💡 Responsibilities

Design, implement, and optimize distributed systems and infrastructure components to support large-scale machine learning workflows, including data ingestion, feature engineering, model training, and serving.
Develop and maintain frameworks, libraries, and tools that streamline the end-to-end machine learning lifecycle, from data preparation and experimentation to model deployment and monitoring.
Architect and implement highly available, fault-tolerant, and secure systems that meet the performance and scalability requirements of production machine learning workloads.
Collaborate with machine learning researchers and data scientists to understand their requirements and translate them into scalable and efficient software solutions.
Stay current with advancements in machine learning infrastructure, distributed computing, and cloud technologies, integrating them into our platform to drive innovation.
Mentor junior engineers, conduct code reviews, and uphold engineering best practices to ensure the delivery of high-quality software solutions.

AWS Docker Leadership Python Software Development SQL Cloud Computing Java Kubeflow Kubernetes Machine Learning MLFlow Algorithms Data engineering Data Structures REST API Collaboration CI/CD Problem Solving Mentoring Linux DevOps Terraform Excellent communication skills Scala Data modeling

Posted 8 days ago

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🔥 Senior ML Solutions Engineer

Posted 8 days ago

📍 Worldwide

🔍 AI-data Infrastructure

🔧 Requirements

5+ years of customer-facing experience.
Experience in a solution or sales engineering environment.
Strong background in Machine learning and Python.
Experience driving highly technical pilots/POCs with enterprise customers.
Great presentation skills.
Bachelor's degree in Computer Science, Mathematics, Machine Learning, Data Science, or equivalent experience.
Ability to quickly learn, understand, and work with new emerging technologies, methodologies, and solutions in the Artificial Intelligence technology space.
Outstanding English writing and verbal skills.
Great communication skills.

💡 Responsibilities

Working with account executives, lead technical pre-sales efforts to identify customer pain points and demonstrate how SuperAnnotate solutions can achieve desired outcomes
Recommend integration strategies, enterprise architectures, and application infrastructure required to implement a complete solution using best practices on SuperAnnotate successfully.
Drive pilots with enterprise clients, defining success metrics, and proving technical approval on SuperAnnotate integration and adoption across large organizations.
Provide in-depth machine learning and data expertise to support the technical relationship with SuperAnnotate’s clients, including product and solution briefings and proof-of-concept work.
Prioritize and ideate new solutions with product development impacting client adoption of SuperAnnotate.
Demonstrate and prototype ML workflows using SuperAnnotate with clients worldwide.
Articulate competitive differentiation to highlight SuperAnnotate strengths
Build technical credibility and trust with key customer relationships to help SuperAnnotate win business

AWS Python Cloud Computing Data Analysis Kubeflow Machine Learning MLFlow Numpy Product Development Data engineering Data science REST API Pandas Tensorflow Communication Skills Customer service Presentation skills Data visualization Computer skills Data modeling Prototyping English communication

Posted 8 days ago

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🔥 Head of AI, China (Remote)

Posted 14 days ago

📍 China

🔍 AI, Financial Services

🏢 Company: Bjak👥 101-250 Price Comparison InsurTech Information Technology

🔧 Requirements

Bachelor's, Master's, or Ph.D. in Computer Science, Artificial Intelligence, or a related field.
Proven experience as an AI engineer or data scientist, with a track record of leading successful AI projects.
Proficiency in AI and machine learning frameworks and programming languages (e.g., Python).
Strong expertise in data preprocessing, feature engineering, and model evaluation.
Excellent problem-solving and critical-thinking skills.
Effective leadership, communication, and team management abilities.
A passion for staying at the forefront of AI and machine learning advancements.

💡 Responsibilities

Lead and mentor a team of AI engineers, providing technical guidance, coaching, and fostering their growth.
Collaborate with product managers and stakeholders to define AI project objectives, requirements, and timelines.
Design, develop, and implement AI models, algorithms, and applications to solve complex business challenges.
Oversee the end-to-end AI model lifecycle, including data collection, preprocessing, model training, evaluation, and deployment.
Stay updated with the latest advancements in AI and machine learning, incorporating best practices into projects.
Drive data-driven decision-making through advanced analytics and visualization techniques.
Ensure the security, scalability, and efficiency of AI solutions.
Lead research efforts to explore and integrate cutting-edge AI techniques.

AWS Python Artificial Intelligence Data Analysis Kubeflow Machine Learning PyTorch Algorithms Data science Tensorflow Data visualization

Posted 14 days ago

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🔥 Senior Python Engineer, AI Team

Posted 17 days ago

📍 Worldwide

🧭 Full-Time

🔍 Software Development

🔧 Requirements

5+ years of experience coding in Python, building web systems, APIs, and scalable backend services.
Experience with real-world AI applications: You’ve worked on Gen-AI projects, including RAG, knowledge graphs, LLM routing, and fine-tuning. You understand the architecture and tools needed to make these systems work.
Deep knowledge of LLMs: You’ve worked with multiple LLM models and can explain their quirks, limitations, and how you’ve overcome them in past projects.
End-to-end development skills: You’ve developed systems from the ground up—from design and coding to deployment and testing.
Fast pace and high independence: You enjoy working in a fast-moving environment with lots of autonomy to solve problems and deliver results.
Product-focused mindset: You care about the impact of your work and strive to build systems that deliver tangible value to customers.

💡 Responsibilities

Develop end-to-end systems: Design, build, and deploy web systems and APIs from scratch. You’ll focus on building scalable, reliable solutions that integrate seamlessly with AI tools and workflows.
Work with modern Gen-AI architecture: Develop pipelines for RAG (retrieval-augmented generation), knowledge graphs, LLM routing, evaluation and testing frameworks, and fine-tuning models for specific use cases.
Optimize multi-model support: Work with multiple LLMs (like GPT-4, Claude, fine-tuned models) to ensure effective routing, dynamic task handling, and performance optimization.
Collaborate with product and engineering teams: Partner closely with technical PMs, designers, and other engineers to bring the platform to life and support innovation in AI products.
Solve real-world AI challenges: Apply your experience working with LLMs to navigate their quirks, troubleshoot issues, and build tools that ensure quality, scalability, and observability.
Move fast and iterate: Take ownership of your work, ship quickly, and learn from feedback to improve and optimize solutions.

AWS Backend Development Docker PostgreSQL Python SQL Artificial Intelligence Git Kubeflow Machine Learning Software Architecture Algorithms API testing Data Structures REST API CI/CD RESTful APIs Linux Microservices Data modeling Software Engineering Debugging

Posted 17 days ago

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🔥 Staff Machine Learning Engineer (Referrals)

Posted 19 days ago

📍 USA

🔍 Machine Learning, Digital Finance

🏢 Company: Referrals Only Board

🔧 Requirements

8+ years of industry experience in Machine Learning (or PhD+5)
MS in Machine Learning, Computer Science, other technical field
10x developer with ability to leverage auto-code generation techniques for ML and scalable distributed applications.
Strong foundation in modern ML techniques (DNNs, transformers, LLMs, classification)
Experience building and deploying production ML systems at scale.
Ability to balance ML model complexity with production requirements
Strong communication skills to work effectively with domain experts

💡 Responsibilities

Design and implement multi-modal ML models that can understand and extract information from various document types (IDs, proof of address, etc.)
Fine tune LLMs for automated document processing and risk assessment.
Design model for ascertaining user onboarding risk and triggering Know-Your-Customer and Enhance Due Diligence models.
Create real-time ML pipelines that can detect and prevent risks before they materialize
Work with experts to translate their domain knowledge into ML features and models
Build explainable ML systems that can justify their risk assessments
Collaborate with platform teams to deploy models at scale with high availability and low latency

AWS Docker PostgreSQL Python SQL Apache Airflow Data Analysis Kubeflow Kubernetes Machine Learning MLFlow Numpy Software Architecture Algorithms API testing Data engineering Pandas Tensorflow CI/CD RESTful APIs Data modeling Software Engineering

Posted 19 days ago

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🔥 Grupo QuintoAndar | MLOps Manager

Posted 21 days ago

📍 Brazil

🧭 Full-Time

🔍 Software Development

🏢 Company: Grupo QuintoAndar

🔧 Requirements

General understanding of machine learning concepts
Good understanding of the engineering challenges of deploying machine learning systems to production
Solid understanding of software and data engineering best practices
Experience with cloud-based services
Proficiency in Python or another major programming language
Experience leading teams and managing careers

💡 Responsibilities

Lead the MLOps Squad: Oversee a cross-functional team of Machine Learning Engineers and Data Scientists focused on building and maintaining our ML Platform. You'll guide the team in balancing long-term vision with the delivery of high-impact features.
Collaborate with Data Science Leaders: Work closely with the Data Science team to deeply understand and deliver on ML platform requirements, ensuring seamless integration between models and production systems.
Optimize Model Deployment & Monitoring: Ensure the smooth deployment and ongoing performance of models, implementing best practices for monitoring, scaling, and addressing model drift.
Foster Best Practices: Lead by example in promoting infrastructure and coding standards that align with machine learning requirements, ensuring efficient, scalable systems.
Drive Automation & Efficiency: Automate workflows, integrate new tools, and streamline pipelines to support fast iteration, model updates, and continuous delivery.

AWS Docker Leadership Python Software Development SQL Apache Airflow Cloud Computing Git Kubeflow Machine Learning MLFlow Numpy People Management Product Management PyTorch Software Architecture Algorithms API testing Data engineering Data science Data Structures REST API Pandas Tensorflow Communication Skills Analytical Skills CI/CD Team management Data modeling

Posted 21 days ago

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🔥 Senior ML Engineer – Speech/Audio (TTS / STT)

Posted 25 days ago

📍 Worldwide

🧭 Full-Time

🔍 Speech Technology

🏢 Company: Synthflow AI

🔧 Requirements

At least 3 years of hands-on experience in speech technology, specifically in developing TTS and STT systems.
Strong software development skills and experience with machine learning model implementation.

💡 Responsibilities

Architect and develop state-of-the-art machine learning models for TTS and STT applications.
Lead the training, fine-tuning, and optimization processes to ensure our speech models achieve top-tier performance.
Collaborate with cross-functional teams to integrate these technologies into scalable and robust products.
Drive innovation by staying updated with the latest industry trends and research in speech technologies.
Collaborate with NLP researchers and Software Engineers for the integration of new speech processing algorithms.

AWS Python Software Development Cloud Computing Image Processing Kubeflow Machine Learning MLFlow Numpy PyTorch Algorithms Data engineering Data science Data Structures REST API Tensorflow CI/CD RESTful APIs Microservices Software Engineering

Posted 25 days ago

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🔥 ML Ops Engineer (Remote)

Posted 28 days ago

📍 United States

🔍 SaaS

🏢 Company: Canibuild Au Pty Ltd

🔧 Requirements

4+ years in MLOps, AI infrastructure, or DevOps
Strong expertise in CI/CD tools for ML (e.g., MLflow, Kubeflow, Airflow)
Experience with cloud ML services (AWS SageMaker, Google Vertex AI, Azure ML)
Proficiency in container orchestration (Docker, Kubernetes)
Understanding of AI model monitoring, logging, and explainability frameworks

💡 Responsibilities

Implement CI/CD pipelines for model training, testing, and deployment.
Develop scalable ML infrastructure to ensure reliable AI model performance.
Automate model retraining, versioning, and monitoring using MLflow, Kubeflow, or Airflow.
Deploy ML models on cloud platforms (AWS, Azure, GCP) and manage Kubernetes/Docker environments.
Assist in optimizing data pipelines and integrating AI models with production systems.
Ensure AI deployments adhere to security, governance, and compliance standards.

AWS Docker Python GCP Kubeflow Kubernetes Machine Learning MLFlow Airflow Azure Data engineering CI/CD DevOps

Posted 28 days ago

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