Machine Learning Engineer intern Job at Voi Technology (Stockholm)

Search Machine Learning Engineer Intern

Perplexity is looking for a Search Machine Learning Engineer Intern to help buil...

Location

Serbia , Belgrade

Salary:

Not provided

Perplexity

Expiration Date

Until further notice

Requirements

Strong foundation in machine learning and statistics, with coursework or projects related to information retrieval, ranking, or recommender systems
Experience with Python and common ML frameworks (e.g. PyTorch, TensorFlow, JAX) through academic, open source, or personal projects
Self-driven and curious, with a strong sense of ownership, willingness to learn, and comfort working in a fast-paced environment

Job Responsibility

Contribute to experiments that improve search quality through better models, data usage, and evaluation tools, under the guidance of senior engineers
Design and implement components of the search platform and model stack, including retrieval, ranking, and classification models
Train evaluating models (including LLM-based approaches) for retrieval, ranking, and classification tasks
Support deployment and monitoring of search and ranking models in a scalable and performant way
Help build and iterate on RAG pipelines for grounding and answer generation
Collaborate with Data, AI, Infrastructure and Product teams to deliver improvements quickly and learn best practices in production ML

Fulltime

New

Staff Applied Machine Learning Engineer - Intelligent Data, Signals & Systems

As a Staff Applied Machine Learning Engineer focused on Intelligent Data, Signal...

Location

United States , Bay Area

Salary:

Not provided

Block

Expiration Date

Until further notice

Requirements

12+ years building and operating production software and ML systems for business-critical products
Deep expertise in intelligent systems such as ranking/retrieval, recommendations, search, personalization, growth and lifecycle ML, customer intelligence, propensity/churn/LTV, next-best-action, or model-derived risk signals
Strong production ML judgment across feature pipelines, model serving, experimentation, monitoring, feedback loops, online/offline consistency, and reliable signal interfaces
Ability to evaluate impact beyond short-term conversion, including trust, fairness, access, risk, compliance, and long-term engagement
Experience using AI-assisted engineering tools with appropriate verification, testing, and review for customer-impacting systems

Job Responsibility

Build and operate production ML systems that turn customer and product context into trusted signals, rankings, recommendations, and decision capabilities
Design production data and signal contracts that define intended use, freshness, provenance, confidence, eligibility, and calibration for downstream consumers
Own ranking, retrieval, recommendation, search, propensity, and next-best-action systems end to end, from feature and candidate generation through serving, experimentation, monitoring, and feedback loops
Evaluate customer and business impact beyond short-term conversion, including trust, fairness, access, risk, compliance, long-term engagement, and segment-level performance
Partner across product, growth, data, platform, modeling, risk, and compliance to translate ambiguous goals into measurable ML system designs
Use AI and agents to accelerate development, analysis, testing, documentation, and operations while exposing reusable capabilities to product services, internal tools, and AI-assisted workflows

What we offer

Remote work
Medical insurance
Flexible time off
Retirement savings plans
Modern family planning

Fulltime

New

Staff Applied Machine Learning Engineer - Fraud & Abuse

As a Staff Applied Machine Learning Engineer focused on Fraud & Abuse, you will ...

Location

United States , Bay Area

Salary:

276800.00 - 415200.00 USD / Year

Block

Expiration Date

Until further notice

Requirements

12+ years building and operating production software and ML systems for business-critical products
Deep expertise in fraud/risk domains such as payment fraud, identity/account integrity, merchant or marketplace risk, scams, trust & safety, abuse prevention, or compliance decisioning
Strong production ML judgment across feature pipelines, model serving, evaluation, monitoring, low-latency integration, safe rollout, and incident response
Sound judgment around false-positive tradeoffs, noisy labels, adversarial behavior, customer harm, and cross-functional decisions
Experience using AI-assisted engineering tools with appropriate verification, testing, and review for high-stakes systems

Job Responsibility

Build and operate real-time and batch ML decisioning systems for payment fraud, scams, identity and account integrity, merchant and marketplace risk, and abuse prevention
Integrate behavioral, graph, device, network, event-stream, and third-party signals into low-latency model serving, decision APIs, and product controls
Own the production lifecycle for risk decisions, including data contracts, feature quality, online/offline consistency, monitoring, drift detection, safe rollout, rollback, and incident response
Develop feedback loops and verified AI-assisted workflows for triage, investigation support, alert clustering, graph exploration, simulation, and post-incident learning
Partner with modelers, analysts, product, compliance, and operations to balance fraud losses, customer access, false positives, product velocity, support burden, and long-term trust
Create reusable decision and evaluation capabilities that product services, internal tools, and AI-assisted workflows can safely consume

What we offer

Remote work
medical insurance
flexible time off
retirement savings plans
modern family planning

Fulltime

Applied Science Intern (Machine Learning, Recommender Systems)

Are you excited about leveraging state-of-the-art Deep Learning, Recommender Sys...

Location

Australia , Sydney; Melbourne; Adelaide; Brisbane; Botany; Craigieburn

Salary:

Not provided

Amazon Pforzheim GmbH

Expiration Date

Until further notice

Requirements

Are enrolled in a PhD in computer science, machine learning, engineering, or related fields
Experience with video and image processing and compression algorithms and standards, computer vision and/or machine learning
Strong programming skills (Python preferred)

Job Responsibility

Develop novel solutions and build prototypes
Work on complex problems in Machine Learning and Information Retrieval
Contribute to research that could significantly impact Amazon operations
Collaborate with a diverse team of experts in a fast-paced environment
Collaborate with scientists on writing and submitting papers to top conferences, e.g. NeurIPS, ICML, KDD, SIGIR
Present your research findings to both technical and non-technical audiences

What we offer

Work in a team of ML scientists to solve recommender systems problems at the scale of Amazon
Access to Amazon services and hardware
Become a disruptor, innovator, and problem solver in the field of information retrieval and recommender systems
Potentially deliver solutions to production in customer-facing applications
Opportunities to be hired full-time after the internship

Fulltime

New

GenAI / Machine Learning Engineer - VOIS

We are seeking a GenAI / ML Engineer with 3-4 years of overall professional expe...

Location

India , Pune

Salary:

Not provided

Vodafone

Expiration Date

Until further notice

Requirements

3-4 years of overall experience in software engineering, AI/ML engineering, data engineering, cloud engineering, or related technology roles
At least 1 year of relevant domain experience in areas such as Generative AI, LLM-based applications, NLP or conversational AI, data or analytics engineering, cloud-native development, or enterprise chatbot platforms
Strong hands-on experience with Python for application development, automation, AI/ML workflows, or backend services
Working knowledge of machine learning and Generative AI concepts, including LLMs, embeddings, prompts, and RAG-based patterns
Comfortable working with SQL and enterprise datasets to support analytical and data-driven use cases
Good working knowledge of Google Cloud Platform or similar cloud environments
Experience developing or integrating APIs and backend or cloud-based applications
Ability to debug, test, and optimise AI/ML or data-driven solutions for accuracy, reliability, and performance
Communicate effectively with both technical and business stakeholders and work collaboratively across teams

Job Responsibility

Design, develop, and enhance GenAI and ML-based solutions for enterprise business use cases
Build and improve Natural Language to SQL and Retrieval-Augmented Generation based chatbot capabilities
Develop and maintain Python-based backend services, APIs, and AI workflows supporting LLM-driven applications
Work with Google Cloud Platform services to build, deploy, and optimise scalable, cloud-native AI applications
Improve retrieval quality, prompt orchestration, SQL generation accuracy, chatbot response quality, and overall solution performance
Collaborate with internal stakeholders to translate business requirements into scalable AI/ML product capabilities
Contribute to testing, evaluation, monitoring, documentation, and production-readiness of GenAI solutions
Support continuous improvement of AI engineering practices, including prompt design, evaluation frameworks, observability, and responsible AI usage

What we offer

Opportunity to work on production-grade Generative AI solutions with direct business impact
Hands-on exposure to modern GCP services, cloud-native deployment patterns, and enterprise-scale AI architectures
Experience across LLMs, RAG, Natural Language to SQL, enterprise data platforms, APIs, and chatbot engineering
A high-visibility role with clear growth pathways into senior engineering, technical leadership, solution architecture, or AI product ownership roles

Fulltime

Senior Machine Learning Engineer, ML Training Platform

Location

United States

Salary:

216700.00 - 303400.00 USD / Year

Expiration Date

Until further notice

Requirements

5+ years of software engineering experience, with a focus on Platform Engineering, ML Infrastructure, or Backend Systems
Deep Kubernetes Expertise: You know K8s beyond just 'deploying pods.' You understand CRDs, Controllers and the Operator pattern
Jupyter Ecosystem Knowledge: Experience customizing JupyterHub, JupyterLab extensions, or building similar interactive computing platforms
Strong Coding Skills: Proficiency in Python (for the ML ecosystem) and Go (for Kubernetes controllers/infrastructure tooling)
GPU Experience: Hands-on practice with CUDA environments, GPU virtualization/containerization, and doing it all within Kubernetes
Cloud Provider Experience: Familiarity with both managed ML offerings (Vertex AI, Sagemaker, etc) and building custom ML components in AWS and/or GCP
Experience working with distributed training frameworks, including Ray and Kubernetes
Comfortable with distributed systems, big data (Petabyte scale) and data-intensive systems
Strong focus on scalability, reliability, performance, and ease of use. You are an undying advocate for platform users and have a deep intuition for the machine learning development lifecycle
Strong organizational & communication skills

Job Responsibility

Lead the building, testing, and maintenance of ML training infrastructure at Reddit
Play a pivotal role in designing, building, and optimizing the infrastructure and tooling required to support large-scale machine learning workflows
Evolve the MLE experience, from provisioning interactive GPU environments through large-scale training, supporting on-demand and self-service workflows
Kubernetes Automation: Write custom Kubernetes Controllers and Operators to manage the lifecycle of interactive Jupyter workspaces and long-running ML training jobs, handle auto-idling, and ensure fault tolerance
GPU Orchestration: Work with the underlying compute team to ensure MLEs have efficient access to training hardware resources and handle resource contention gracefully
Developer Experience (DevX): Treat internal MLEs as your customers. Conduct user research, reduce friction in the 'Idea-to-Prototype' loop, and standardize software environments (Docker images, Python dependency management)

What we offer

Comprehensive Healthcare Benefits and Income Replacement Programs
401k Match
Family Planning Support
Gender-Affirming Care
Mental Health & Coaching Benefits
Flexible Vacation & Reddit Global Days off
Generous paid Parental Leave
Paid Volunteer time off

Fulltime

Sr Staff Machine Learning Engineer (ADEM)

The ADEM engineering team is the engine of innovation at Palo Alto Networks. We ...

Location

United States , Santa Clara

Salary:

147000.00 - 237500.00 USD / Year

Palo Alto Networks

Expiration Date

Until further notice

Requirements

6+ years of software engineering experience, with a significant portion dedicated to designing and operating complex, resource-constrained client-side or endpoint software in a large-scale deployment
As a leader in engineering efficiency, you must be an expert in leveraging AI-powered development tools—including Claude Code, Cursor, Windsurf, and GitHub Copilot—to radically accelerate the SDLC and automate complex refactoring and testing workflows
Proven track record of architecting and shipping production-quality software for multiple operating systems (Windows, macOS, Linux) with a deep understanding of OS internals, networking stacks (L3-L7), and system performance tuning
Coding Rust, or Go (Golang) for high-performance client development is required
BS/MS/PhD in Computer Science or a related technical field, or equivalent high-level professional experience.

Job Responsibility

Define the long-term architectural vision for the ADEM Agent across Linux, Windows, macOS, and mobile platforms, ensuring robust performance, minimal resource consumption
Lead the 'Design Review Board' for all Agent-side features, ensuring all components adhere to Secure AI by Design, operate with high-availability patterns in diverse OS environments, and maintain a low CPU/memory footprint
Drive the evolution of the Agent's telemetry collection and processing pipeline, optimizing for low-latency, encrypted data transfer and ensuring resilience in low-bandwidth or disconnected environments
Partner with Product Management, Core Infrastructure, and Security teams to translate ambiguous business requirements into robust, high-performance technical specifications for the client software
Act as a force multiplier by mentoring Staff and Senior engineers, fostering a culture of rigorous platform-specific testing, high code quality, and proactive technical debt management within the Agent codebase
Serve as the ultimate technical escalation point for complex, systemic production issues related to endpoint stability or performance, leading post-mortems that drive permanent architectural improvements in the Agent

What we offer

restricted stock units
bonus

Fulltime