Role 

We are looking for an AI Operations Engineer to join our team. This is a hybrid role based in our Zagreb office, reporting to Dominik Jurincic within the Red Teaming department. As an AI Operations Engineer, you will support the AI Red Team by operating and scaling the evaluation infrastructure that powers SPLX’s model benchmarks and MCP server registry. You’ll be the engineering force behind our continuous AI security testing workflows, ensuring our evaluation pipelines run reliably across a rapidly expanding ecosystem of LLMs and agentic tools.

What you’ll do (Role Expectations)

• Operate and extend our LLM benchmarking pipeline by integrating new models — whether through provider APIs or self-hosted deployments on virtualized infrastructure — and executing our security suite to populate and maintain the model benchmarks page
• Kick-off and maintain the SPLX MCP server registry by onboarding open-source MCP servers, provisioning their runtime dependencies, and running our dynamic code scanning and security testing against them
• Build and maintain reproducible deployment environments for evaluating third-party AI systems, ensuring consistent and reliable test conditions across diverse model and tooling configurations
• Partner with Red Team engineers and researchers to streamline evaluation workflows, automate recurring testing operations, and surface insights from benchmark and registry data
• Continuously improve the team’s internal tooling and operational processes, identifying opportunities to reduce manual effort and accelerate the cadence of model and MCP evaluations

Who You Are (Success Profile)

• You thrive in ambiguity. You're comfortable building the path as you walk it. You thrive in a dynamic environment, seeing ambiguity not as a hindrance, but as the raw material to build something meaningful.
• You act like an owner. Your passion for the mission fuels your bias for action. You operate with integrity because you genuinely care about the outcome. You adapt to what’s needed, navigating seamlessly between high-level strategy and hands-on execution.
• You are a high-trust collaborator. You are ambitious for the team, not just yourself. You embrace our challenge culture by giving and receiving ongoing feedback—knowing that candor delivered with clarity and respect is the truest form of teamwork and the fastest way to earn trust.
• You are a learner. You have a true growth mindset and never stop developing yourself, actively seeking feedback to become a better partner and a stronger teammate. You love what you do and you do it with purpose.
• You operate with urgency. You understand that in a high-growth environment, speed and quality are not mutually exclusive. You have a relentless focus on execution and a bias for action, delivering high-impact results quickly to win for the customer and the team.

What We’re Looking for (Minimum Qualifications)

• 1+ years of professional software engineering experience with Python, including the ability to ship reliable, production-quality code in a collaborative environment
• Working familiarity with the modern AI/ML ecosystem, including LLM provider APIs, model deployment patterns, and emerging protocols such as the Model Context Protocol (MCP)
• Hands-on experience with containerization (Docker) and deploying applications across cloud or virtualized infrastructure
• Solid working knowledge of REST APIs and modern integration patterns, including authentication, service orchestration, and debugging across distributed components
• Strong attention to detail and a track record of operating consistently in environments where reproducibility, rigor, and follow-through matter

What Will Make You Stand Out (Preferred Qualifications)

• Prior exposure to LLM evaluation, AI security, or red teaming workflows in a research or applied setting
• Familiarity with the broader open-source AI tooling landscape — including MCP servers, model serving frameworks, or agentic systems — and the curiosity to keep up with a fast-moving space
• Background in DevOps, platform engineering, or infrastructure automation, with comfort building reproducible test environments at scale

#LI-MM8 

#LI-HYBRID

Staff Software Engineer — Data Platform (Europe)

This is a rare role for an engineer who's genuinely comfortable on both sides of the stack — reading Java Spring Boot services in the morning and writing PySpark pipelines in the afternoon. You'll own the end-to-end data flow that sits behind every pricing decision Duetto makes for thousands of hotels worldwide, bridging our Java core platform and Python data layer while working in an engineering culture where AI is already how the work gets done.

What Makes Us Different?

Duetto is the hospitality industry's leading revenue management platform, founded in 2012 by former Wynn Resorts executives who knew the industry needed better technology. We built the world's first Revenue & Profit Operating System — a suite of tools (GameChanger, ScoreBoard, BlockBuster, Advance and more) that goes beyond room pricing to give hotels, resorts and casinos a complete picture of their revenue and profitability. Trusted by clients ranging from independent boutique hotels to global chains, we've been named the #1 Revenue Management Software by HotelTechAwards four years running and the #1 Best Place to Work in Hotel Tech in 2025. Backed by GrowthCurve Capital since 2024, we're accelerating our investment in AI — and we're genuinely passionate about the industry we serve. We build products we're proud of, for customers we care about.

What You'll Be Doing

• You'll own data pipelines from source to gold — taking data from MongoDB, Kinesis events, RabbitMQ, and PMS/CRS integrations through the full bronze → silver → gold lakehouse architecture, including Iceberg-based ingestion patterns for both batch and near-real-time workloads.
• You'll build connectors and transformations for new data sources — Salesforce, Amadeus, Expedia, and internal platform events — expanding the reach and reliability of the data layer the whole product depends on.
• You'll work across system boundaries: reading Kinesis event schemas and MongoDB data models in the Java platform, then applying that understanding in the Python pipeline codebase — driving schema evolution strategies, data contract enforcement, and backward compatibility across both worlds.
• You'll drive data quality and governance — extending Great Expectations and Data Contract CLI across pipeline tiers, owning Athena views and SQL assets, and building monitoring and alerting for pipeline health, data freshness, and quality drift.
• You'll partner with DevPlatform on event schema design and with data science to operationalise ML workloads, ensuring clean data handoffs across every system boundary.
• You'll work AI-first every day — using Claude Code and MCP tools as a core part of your workflow, contributing to AI-assisted pipeline scaffolding and data discovery tooling alongside a custom multi-agent system built around 17 specialised agents.

What We're Looking For

You may be a good fit if you have:

• 7+ years building production data or backend systems
• Strong proficiency in both Python (PySpark, data engineering) and Java — you can read and navigate Spring/JVM codebases comfortably, not just Python
• Experience with lakehouse or data warehouse architectures on cloud — Iceberg, Delta Lake, Redshift, BigQuery, or Snowflake
• Production experience with AWS data services: Glue, Athena, S3, Lambda
• Experience with workflow orchestration — Airflow, Kestra, Step Functions, or similar
• The ability to work across system boundaries: you understand upstream event schemas, data models, and downstream consumer needs

Strong candidates may also have:

• Hands-on experience with Apache Iceberg — MERGE operations, schema evolution, partition evolution
• Experience with Trino or Presto for federated or interactive SQL analytics at scale
• Experience with dbt for data transformation, modelling, and testing
• Familiarity with data quality frameworks — Great Expectations, Monte Carlo, or similar
• A background in event-driven architectures: Kinesis, Kafka, SQS
• Genuine interest in AI-assisted development and LLM-based engineering workflows
• Familiarity with the hospitality domain or multi-tenant B2B SaaS data challenges

Why Duetto?

• AI-first is the reality, not the roadmap. Every engineer uses Claude Code daily, and you'll be contributing to a custom multi-agent system with 17 specialised agents, human-in-the-loop approval gates, and AI-assisted pipeline generation. This is what modern data engineering looks like.
• Cross-cutting technical ownership. This isn't a siloed data role — you'll operate across Java upstream systems and Python pipelines, with real architectural influence over how data flows through the entire platform.
• The scale and the stakes are real. Millions of pricing decisions processed daily, 80+ integration partners, and a live evolution toward near-real-time streaming. The technical challenges are genuine.
• A team with the right values. Low ego, high EQ, intellectual curiosity, and active mentorship in both directions across a collaborative US and Europe group.
• Modern stack at real scale. Python/PySpark, Apache Iceberg, Airflow, AWS Glue, Terraform, Datadog — tools that matter, used on problems that matter.

The Details

• Location: Croatia (Remote)
• Team: Data Platform
• Direct reports: None
• Travel: None

Duetto is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by applicable law.

Sound like you?

You don't need to be an expert in every technology on this list. If you're a strong engineer who spans Java and Python, cares about data quality, and wants to work somewhere AI is genuinely part of how you build — we'd love to hear from you.

#LI-REMOTE

As a Senior Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Partner with product and engineering to identify and execute machine learning use cases that deliver measurable impact
• Design, build, and iterate on machine learning solutions (e.g., classifiers, regressors, ranking/retrieval, and rule-based components)
• Contribute across the ML lifecycle: data exploration, feature engineering, training, evaluation, deployment, and monitoring
• Implement reliable training/inference pipelines and help improve reproducibility, testing, and observability
• Communicate model behavior, trade-offs, and results clearly to both technical and non-technical stakeholders
• Contribute to team standards: code quality, documentation, experimentation hygiene, and responsible ML practices

We're looking for someone with:

• Bachelor’s degree in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field (Master’s a plus)
• 4+ years of industry experience building and deploying ML systems
• Solid proficiency in Python and familiarity with common ML libraries (e.g., PyTorch, XGBoost) and SQL
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies
• Demonstrated ability to write maintainable, tested code, participate in code reviews, and follow engineering best practices
• Strong problem-solving skills with the ability to break down ambiguous problems into scoped tasks and deliver iteratively

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects.

As a Senior Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Partner with product and engineering to identify and execute machine learning use cases that deliver measurable impact
• Design, build, and iterate on machine learning solutions (e.g., classifiers, regressors, ranking/retrieval, and rule-based components)
• Contribute across the ML lifecycle: data exploration, feature engineering, training, evaluation, deployment, and monitoring
• Implement reliable training/inference pipelines and help improve reproducibility, testing, and observability
• Communicate model behavior, trade-offs, and results clearly to both technical and non-technical stakeholders
• Contribute to team standards: code quality, documentation, experimentation hygiene, and responsible ML practices

We're looking for someone with:

• Bachelor’s degree in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field (Master’s a plus)
• 4+ years of industry experience building and deploying ML systems
• Solid proficiency in Python and familiarity with common ML libraries (e.g., PyTorch, XGBoost) and SQL
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies
• Demonstrated ability to write maintainable, tested code, participate in code reviews, and follow engineering best practices
• Strong problem-solving skills with the ability to break down ambiguous problems into scoped tasks and deliver iteratively

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects.

As a Senior Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Partner with product and engineering to identify and execute machine learning use cases that deliver measurable impact
• Design, build, and iterate on machine learning solutions (e.g., classifiers, regressors, ranking/retrieval, and rule-based components)
• Contribute across the ML lifecycle: data exploration, feature engineering, training, evaluation, deployment, and monitoring
• Implement reliable training/inference pipelines and help improve reproducibility, testing, and observability
• Communicate model behavior, trade-offs, and results clearly to both technical and non-technical stakeholders
• Contribute to team standards: code quality, documentation, experimentation hygiene, and responsible ML practices

We're looking for someone with:

• Bachelor’s degree in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field (Master’s a plus)
• 4+ years of industry experience building and deploying ML systems
• Solid proficiency in Python and familiarity with common ML libraries (e.g., PyTorch, XGBoost) and SQL
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies
• Demonstrated ability to write maintainable, tested code, participate in code reviews, and follow engineering best practices
• Strong problem-solving skills with the ability to break down ambiguous problems into scoped tasks and deliver iteratively

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects.

As a Senior Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Partner with product and engineering to identify and execute machine learning use cases that deliver measurable impact
• Design, build, and iterate on machine learning solutions (e.g., classifiers, regressors, ranking/retrieval, and rule-based components)
• Contribute across the ML lifecycle: data exploration, feature engineering, training, evaluation, deployment, and monitoring
• Implement reliable training/inference pipelines and help improve reproducibility, testing, and observability
• Communicate model behavior, trade-offs, and results clearly to both technical and non-technical stakeholders
• Contribute to team standards: code quality, documentation, experimentation hygiene, and responsible ML practices

We're looking for someone with:

• Bachelor’s degree in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field (Master’s a plus)
• 4+ years of industry experience building and deploying ML systems
• Solid proficiency in Python and familiarity with common ML libraries (e.g., PyTorch, XGBoost) and SQL
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies
• Demonstrated ability to write maintainable, tested code, participate in code reviews, and follow engineering best practices
• Strong problem-solving skills with the ability to break down ambiguous problems into scoped tasks and deliver iteratively

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito)
• Exposure to streaming data platforms like Kafka
• Contributions to open-source ML projects

As a Senior Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Partner with product and engineering to identify and execute machine learning use cases that deliver measurable impact
• Design, build, and iterate on machine learning solutions (e.g., classifiers, regressors, ranking/retrieval, and rule-based components)
• Contribute across the ML lifecycle: data exploration, feature engineering, training, evaluation, deployment, and monitoring
• Implement reliable training/inference pipelines and help improve reproducibility, testing, and observability
• Communicate model behavior, trade-offs, and results clearly to both technical and non-technical stakeholders
• Contribute to team standards: code quality, documentation, experimentation hygiene, and responsible ML practices

We're looking for someone with:

• Bachelor’s degree in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field (Master’s a plus)
• 4+ years of industry experience building and deploying ML systems
• Solid proficiency in Python and familiarity with common ML libraries (e.g., PyTorch, XGBoost) and SQL
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies
• Demonstrated ability to write maintainable, tested code, participate in code reviews, and follow engineering best practices
• Strong problem-solving skills with the ability to break down ambiguous problems into scoped tasks and deliver iteratively

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects.

THE ROLE 

The Senior Application Security Engineer is a key member of Cognism’s Information Security Team, reporting into the Application & Infrastructure Security Manager. Your mission is to embed security by design across our engineering and product organization by integrating modern application security practices throughout the full development lifecycle. We focus on building secure, scalable, and resilient systems while enabling the business to innovate quickly and safely. It is to drive a culture where the fastest path for our engineers is the securest path. 

You will work closely with Product, Engineering, Architecture, and Data teams to understand risks within our platform, including risks introduced by AI powered features, and ensure the right controls, guardrails, and security patterns are built into the product at its inception – all the while ensuring a balanced approach to the product experience that our thousands of large global enterprise customers use every day. 

This role is ideal for a senior IC who is technical, collaborative, and pragmatic, with the ability to influence engineering teams while driving hands on improvements to Cognism’s secure SDLC. 

KEY RESPONSIBILITIES 

Security by Design & Product Integration 

• Partner with Product, Web and Data Engineering teams from the ideation stage to ensure security requirements are considered early in feature and model design.
• Translate product and application risks into actionable security controls, making recommendations repeatable to build guardrails and guidance that product, design, and engineering teams can apply as they scale and build the products in the teams they own.
• Help shape security acceptance criteria and guide engineering teams during design reviews and backlog planning. 

Application Risk Assessment & AI Security 

• Identify and assess application risks across Cognism’s SaaS platform, data processing pipelines, including emerging risks associated with AI/ML capabilities.
• Contribute to AI feature reviews and participate in AI risk assessments to ensure responsible and secure use of models, in a way that balances the need to innovate and challenge the status quo in AI implementation.
• Assess and pragmatically recommend mitigations for security risks in data pipelines, model-training workflows, feature stores, and ML systems, ensuring strong controls for data access, data lineage, model integrity, and protection of sensitive datasets. 

Secure SDLC & DevSecOps Enablement 

• Partner with our engineering and platform team to embed security guardrails into our software development lifecycle and agile engineering workflows.
• Partner with engineering teams to integrate and optimize security tooling into CI/CD pipelines (SAST, SCA, DAST, container scanning, IaC scanning).
• Drive automation and developer-friendly security processes that minimize friction and support rapid delivery. 

Threat Modelling & Architecture Support 

• Conduct and facilitate a self-serve, risk-driven approach to pragmatic threat modelling sessions for new features, services, and AI components.
• Conduct threat modelling for data architectures, including ingestion, transformation, storage, streaming, and ML model deployment patterns, ensuring data confidentiality, integrity, and responsible use.
• Provide hands-on application security guidance to engineering teams, helping them implement secure APIs, microservices, data flows, and integrations.
• Maintain and expand Cognism’s secure coding standards, guidance, and reusable security patterns. 

Application Security Testing & Engineering 

• Perform hands-on security testing (manual and automated) for web applications, microservices, APIs, and cloud components.
• Plan, coordinate, and oversee penetration tests, red team exercises, and third-party security assessments, ensuring findings are addressed and tracked.
• Validate findings, assist with prioritization, and partner with engineering teams on remediation strategies. 

Collaboration, Education & Influence 

• Work directly with product squads, acting as a trusted advisor and embedded security partner. Take the time to understand what other teams are working on, what business priorities are, and partner with teams to recommend risk mitigations that balance risk with opportunity and that take into account the threat landscape. 
• Deliver security training, workshops, and guidance to improve engineering teams’ security maturity.Communicate security risk and tradeoffs clearly and constructively to technical and nontechnical stakeholders. 

CORE COMPETENCIES 

• Strong technical depth in application security, cloud security, and secure development.
• Understanding of modern data stack components (e.g., data pipelines, feature stores) and the ability to collaborate effectively with data practitioners.
• Deep understanding of communication protocols used for web development is a must-have.
• Hands on experience with web application development, specifically for back-end development is a must-have competency. 
• Risk-Based Prioritization: Ability to distinguish between theoretical security risks and actual business threats, demonstrating a "risk-driven" rather than "compliance-driven" mindset. Comfortable balancing security risk with product and commercial realities.
• Contextual Communication: Capacity to translate complex technical vulnerabilities into business-impact stories that resonate with non-technical stakeholders and product owners. 
• Collaborative Conflict Resolution: Proven track record of approaching engineering friction with transparency and pragmatism, balancing a firm stance on security with a deep care for the developer experience. Pragmatic problem solver with a growth mindset and bias toward action.
• Architectural Empathy: Ability to put yourself in the position of your users (the engineers) to build security guardrails that are repeatable and embedded into existing workflows rather than added as hurdles. 

EDUCATION & EXPERIENCE 

• 5–10+ years of experience in Application Security, Product Security, or Security Engineering roles.
• Strong experience securing cloud native SaaS platforms (AWS preferred) and Data pipelines. 
• Handson experience with secure coding, application testing, and CI/CD security automation.
• Experience working closely with engineering and product teams in agile environments.
• Familiarity with security frameworks such as OWASP ASVS, OWASP Top 10, NIST, ISO 27001/2.
• Experience evaluating and securing AI/ML enabled features is a strong advantage.
• Experience in SME or high-growth SaaS environments preferred.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Partner with product and engineering to identify and execute machine learning use cases that deliver measurable impact
• Design, build, and iterate on machine learning solutions (e.g., classifiers, regressors, ranking/retrieval, and rule-based components)
• Contribute across the ML lifecycle: data exploration, feature engineering, training, evaluation, deployment, and monitoring
• Implement reliable training/inference pipelines and help improve reproducibility, testing, and observability
• Communicate model behavior, trade-offs, and results clearly to both technical and non-technical stakeholders
• Contribute to team standards: code quality, documentation, experimentation hygiene, and responsible ML practices

We're looking for someone with:

• Bachelor’s degree in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field (Master’s a plus)
• 2+ years of industry experience building and deploying ML systems
• Solid proficiency in Python and familiarity with common ML libraries (e.g., PyTorch, XGBoost) and SQL
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies
• Demonstrated ability to write maintainable, tested code, participate in code reviews, and follow engineering best practices
• Strong problem-solving skills with the ability to break down ambiguous problems into scoped tasks and deliver iteratively

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito)
• Exposure to streaming data platforms like Kafka
• Contributions to open-source ML projects

As a Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Partner with product and engineering to identify and execute machine learning use cases that deliver measurable impact
• Design, build, and iterate on machine learning solutions (e.g., classifiers, regressors, ranking/retrieval, and rule-based components)
• Contribute across the ML lifecycle: data exploration, feature engineering, training, evaluation, deployment, and monitoring
• Implement reliable training/inference pipelines and help improve reproducibility, testing, and observability
• Communicate model behavior, trade-offs, and results clearly to both technical and non-technical stakeholders
• Contribute to team standards: code quality, documentation, experimentation hygiene, and responsible ML practices

We're looking for someone with:

• Bachelor’s degree in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field (Master’s a plus)
• 2+ years of industry experience building and deploying ML systems
• Solid proficiency in Python and familiarity with common ML libraries (e.g., PyTorch, XGBoost) and SQL
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies
• Demonstrated ability to write maintainable, tested code, participate in code reviews, and follow engineering best practices
• Strong problem-solving skills with the ability to break down ambiguous problems into scoped tasks and deliver iteratively

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito)
• Exposure to streaming data platforms like Kafka
• Contributions to open-source ML projects

As a Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Partner with product and engineering to identify and execute machine learning use cases that deliver measurable impact
• Design, build, and iterate on machine learning solutions (e.g., classifiers, regressors, ranking/retrieval, and rule-based components)
• Contribute across the ML lifecycle: data exploration, feature engineering, training, evaluation, deployment, and monitoring
• Implement reliable training/inference pipelines and help improve reproducibility, testing, and observability
• Communicate model behavior, trade-offs, and results clearly to both technical and non-technical stakeholders
• Contribute to team standards: code quality, documentation, experimentation hygiene, and responsible ML practices

We're looking for someone with:

• Bachelor’s degree in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field (Master’s a plus)
• 2+ years of industry experience building and deploying ML systems
• Solid proficiency in Python and familiarity with common ML libraries (e.g., PyTorch, XGBoost) and SQL
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies
• Demonstrated ability to write maintainable, tested code, participate in code reviews, and follow engineering best practices
• Strong problem-solving skills with the ability to break down ambiguous problems into scoped tasks and deliver iteratively

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito)
• Exposure to streaming data platforms like Kafka
• Contributions to open-source ML projects

About Vumedi:

Vumedi is the largest video education platform for doctors worldwide, dedicated to advancing medical education through innovative video-based learning. Our mission is to empower healthcare professionals by providing them with access to the latest clinical knowledge and surgical techniques from experts around the globe. We curate a vast library of high-quality educational content, enabling users to enhance their skills, stay informed about industry trends, and improve patient outcomes. We are headquartered in Oakland, CA, and have additional offices in Minneapolis, MN, and Zagreb, Croatia. 

We're hiring a Senior DevOps Engineer to lead the development of our digital platform and products at this critical stage of Vumedi’s growth. 

Why join Vumedi right now?

• Build technology that matters in a fast-scaling Silicon Valley digital healthcare company: Your work directly impacts how doctors across the world learn and make decisions that save lives.
• Grow as we grow: Be part of a company in an accelerated growth phase, where expanding teams, products, and markets create real opportunities for ownership, leadership, and career progression.
• Build with AI: Work on applied LLM systems - from intelligent search to AI-driven content agents - and shape how AI transforms medical knowledge delivery.
• Own your craft end-to-end: Take full responsibility for building systems that scale globally and power mission-critical workflows.
• Collaborate globally: Join a world-class team of passionate engineers on modern tech stack which will further drive your career development.
• Have real product impact: Influence the direction of product development by collaborating closely with product and leadership teams.

About the role:

We are looking for a Senior DevOps Engineer to join our engineering team and take ownership of our infrastructure, deployment processes, and overall platform reliability. You will work closely with backend and data teams to support a growing video and data platform used by millions of healthcare professionals worldwide.

In this role, you will focus on improving our CI/CD pipelines, system reliability, and developer experience, while helping scale our cloud infrastructure in a secure and cost-efficient way. You will work extensively with AWS services (compute, storage, networking, IAM, monitoring) and help ensure our systems are reliable, observable, and well-architected.

You’ll also support and enable emerging AI/ML and LLM-powered systems used for large-scale medical content processing, helping build and operate the infrastructure required for these workloads. This includes improving data pipelines, optimizing resource usage, and ensuring production-grade reliability of AI-driven services.

This is a high-impact role with a broad scope—from supporting production systems and data pipelines to driving long-term improvements in how we build, deploy, and operate our platform, with strong ownership and autonomy in shaping DevOps practices.

What you will do:

• Own and improve our infrastructure, CI/CD pipelines, and deployment processes across multiple environments
• Work with AWS services (compute, storage, networking, IAM, monitoring) to ensure scalable, secure, and reliable systems
• Collaborate closely with backend and data teams to support production systems, data pipelines, and overall platform reliability
• Continuously improve developer experience by streamlining workflows, reducing friction, and enabling faster, safer deployments
• Contribute to improving security practices, access control, and compliance of our infrastructure
• Automate infrastructure and workflows using Python 
• Improve observability by implementing and maintaining monitoring, logging, and alerting systems
• Troubleshoot production issues, participate in incident response, and implement long-term fixes to improve system stability
• Identify and drive improvements in performance, scalability, and cost efficiency across the platform
• Support and scale AI/ML and LLM-based systems, ensuring reliable infrastructure for data processing and content classification workloads

Who you are: 

• You have 5+ years of experience in DevOps, SRE, or infrastructure engineering, with a strong focus on cloud-native environments (preferably AWS)
• You have managed cloud infrastructure (networking, IAM, compute, storage) with a strong understanding of security best practices and cost optimization
• You have experience building and maintaining CI/CD pipelines to support rapid, reliable software delivery across multiple environments
• You are comfortable writing Python for automation, scripting, and building internal tooling to improve infrastructure and developer workflows
• You have a strong understanding of monitoring, logging, and observability (e.g., Datadog, Prometheus, CloudWatch), and proactively identifying and resolve issues
• You are comfortable debugging production issues across systems and collaborating with engineering teams to resolve them
• You are proactive, take ownership, and enjoy working in environments with high autonomy and evolving processes
• You communicate clearly and collaborate effectively with engineers, product managers, and other stakeholders
• You are curious and motivated to learn, especially in areas like AI/ML infrastructure and large-scale systems

Required Qualifications:

• 5+ years of experience in DevOps, Site Reliability Engineering, or infrastructure-focused roles
• Proven experience designing and operating scalable, reliable, and secure cloud infrastructure (preferably AWS) in production environments
• Strong understanding of cloud security best practices (IAM, network security, secrets management), preferably within AWS
• Proficiency in Python for automation, scripting, and tooling
• Hands-on experience building and maintaining CI/CD pipelines
• Experience with monitoring, logging, and alerting tools (e.g., Datadog, CloudWatch, Prometheus)
• Experience working in a Linux-based environment
• Ability to drive infrastructure and DevOps strategy, balancing scalability, reliability, and cost
• Experience working cross-functionally and influencing engineering teams on best practices and architectural decisions
• Strong ownership mindset with the ability to operate autonomously in ambiguous environments

Preferred Qualifications:

• Experience supporting or scaling AI/ML or LLM-based systems in production
• You have worked with containerized applications (Docker) and are familiar with orchestration concepts (Kubernetes or ECS is a plus)
• You are familiar with Infrastructure as Code principles (e.g., Terraform) and have experience implementing Infrastructure as Code from scratch in existing environments
• You have experience working with or supporting backend systems and data platforms (e.g., Postgres, Airflow is a plus)
• Background in backend engineering or software development
• Experience working in a fast-paced startup or scale-up environment
• Experience leading and mentoring engineers, while contributing to team-wide best practices

You can expect:

The base salary range for this role is determined by a number of factors, including but not limited to skills and scope required, unique relevant experience, and job-related skills.

Cash salary is but one component of our compensation package. It’s inclusive of base salary, education budget, supplemental health insurance, free meals allowance, work from an alternative workplace allowance, occasional awards - Christmas bonus, and an annual bonus.

We also offer stock options as part of our compensation package, giving you the opportunity to share in the long-term success and growth of the company.

This is a hybrid role, working 3 days a week (Monday, Wednesday, and Friday) in our Zagreb office.

Are you excited about building analytics foundations that people can truly rely on, turning complex business questions into trusted datasets, metrics, and reporting experiences? At Super Technologies, data is a strategic enabler of decision-making, experimentation, and performance measurement. Our Analytics Platform team builds the foundations that make data reliable, accessible, and actionable, partnering with product, commercial, and engineering teams to measure what matters.

You’ll be joining a mature and growing community of 40+ data engineers and analytics engineers who are shaping one of the most advanced data ecosystems in the industry.

What you'll be doing:

• Design and build curated analytical datasets, metric implementation, and semantic models that enable consistent self-service analytics across the company
• Work hands-on with data warehouse modelling to turn evolving product and business needs into scalable, reusable data structures
• Partner closely with product, data scientists and business stakeholders to clarify requirements and translate them into reliable data models and reporting-ready outputs
• Build and own production dashboards (Tableau) on top of curated datasets and metric definitions, ensuring correctness, performance, and a consistent “single source of truth”
• Contribute to data quality, observability, lineage, and documentation practices to increase trust and reduce firefighting
• Promote reuse over reinvention, identify ad-hoc logic in reporting views or custom SQL and refactor it into curated, high-quality models that multiple teams can rely on.
• Raise the engineering bar through reviews, testing, automation, and pragmatic architectural improvements
• Apply engineering rigor to analytics - treat semantics as code utilizing version control, testing, automation, and CI/CD practices to manage the lifecycle of our business logic.
• Drive self-service adoption - create the foundations that allow analysts and business users to explore data safely and independently, without needing to reinvent the wheel.
• Work embedded within Data Engineering squads, collaborating daily with both data engineers and analysts on shared foundations and end-to-end delivery

We're looking for:

• Strong SQL skills and proven experience building analytical datasets and metrics in a modern data warehouse (Snowflake preferred, BigQuery, Redshift and similar also valuable)
• Solid understanding of data warehouse modeling and best practices (dimensional modeling, fact/dimension design, grains, slowly changing dimensions, semantic consistency)
• Experience working with reporting and BI tools (Tableau preferred), including understanding how data sources, extracts, and dashboard logic impact correctness, performance, and trust
• A production mindset: you care about reliability, maintainability, documentation, and operational ownership, not just creating a dataset once
• Strong ownership and collaboration skills, able to drive clarity in ambiguous problem spaces and partner effectively with both technical and non-technical teams
• Excellent communication skills with a pragmatic, problem-solving approach

Bonus points for:

• Experience with orchestration and workflow tooling (Airflow or similar)
• Experience with data cataloging, lineage, and governance tooling (e.g., DataHub)
• Background in product-led, experimentation-driven, or high-growth environments where definitions evolve quickly
• Experience supporting downstream operational consumers (e.g., CRM/audience platforms) or ML/feature engineering use cases
• Familiarity with streaming/event-heavy ecosystems (Kafka or similar)

Are you excited about building analytics foundations that people can truly rely on, turning complex business questions into trusted datasets, metrics, and reporting experiences? At Super Technologies, data is a strategic enabler of decision-making, experimentation, and performance measurement. Our Analytics Platform team builds the foundations that make data reliable, accessible, and actionable, partnering with product, commercial, and engineering teams to measure what matters.

You’ll be joining a mature and growing community of 40+ data engineers and analytics engineers who are shaping one of the most advanced data ecosystems in the industry.

What you'll be doing:

• Design and build curated analytical datasets, metric implementation, and semantic models that enable consistent self-service analytics across the company
• Work hands-on with data warehouse modelling to turn evolving product and business needs into scalable, reusable data structures
• Partner closely with product, data scientists and business stakeholders to clarify requirements and translate them into reliable data models and reporting-ready outputs
• Build and own production dashboards (Tableau) on top of curated datasets and metric definitions, ensuring correctness, performance, and a consistent “single source of truth”
• Contribute to data quality, observability, lineage, and documentation practices to increase trust and reduce firefighting
• Promote reuse over reinvention, identify ad-hoc logic in reporting views or custom SQL and refactor it into curated, high-quality models that multiple teams can rely on.
• Raise the engineering bar through reviews, testing, automation, and pragmatic architectural improvements
• Apply engineering rigor to analytics - treat semantics as code utilizing version control, testing, automation, and CI/CD practices to manage the lifecycle of our business logic.
• Drive self-service adoption - create the foundations that allow analysts and business users to explore data safely and independently, without needing to reinvent the wheel.
• Work embedded within Data Engineering squads, collaborating daily with both data engineers and analysts on shared foundations and end-to-end delivery

We're looking for:

• Strong SQL skills and proven experience building analytical datasets and metrics in a modern data warehouse (Snowflake preferred, BigQuery, Redshift and similar also valuable)
• Solid understanding of data warehouse modeling and best practices (dimensional modeling, fact/dimension design, grains, slowly changing dimensions, semantic consistency)
• Experience working with reporting and BI tools (Tableau preferred), including understanding how data sources, extracts, and dashboard logic impact correctness, performance, and trust
• A production mindset: you care about reliability, maintainability, documentation, and operational ownership, not just creating a dataset once
• Strong ownership and collaboration skills, able to drive clarity in ambiguous problem spaces and partner effectively with both technical and non-technical teams
• Excellent communication skills with a pragmatic, problem-solving approach

Bonus points for:

• Experience with orchestration and workflow tooling (Airflow or similar)
• Experience with data cataloging, lineage, and governance tooling (e.g., DataHub)
• Background in product-led, experimentation-driven, or high-growth environments where definitions evolve quickly
• Experience supporting downstream operational consumers (e.g., CRM/audience platforms) or ML/feature engineering use cases
• Familiarity with streaming/event-heavy ecosystems (Kafka or similar)