Role Description Next-Generation Infotainment System project for German premium carmaker at Luxoft Automotive Division. There is a wide range of applications provided by domains for the Head Unit, such as Tuner, Player, etc. The project life-cycle covers all stages from design to development and delivery to the client. The project team works in tight collaboration with one of the premium OEMs in the world and is located in the Southeast Asia region. You will have the opportunity to be part of the team that is developing functionality for the most technologically advanced cars in the world. - Develop and maintain Automotive HMI applications using Qt/QML and C++ - Implement UI features and integrate QML frontend with C++ backend - Collaborate with Architects, Developers, Test Engineers, and Product Owners to deliver high-quality software - Participate in design discussions, code reviews, estimation, and troubleshooting activities - Contribute to software quality, performance optimization, and continuous improvement initiatives Qualifications - Upper Intermediate level of English - 4+ years of software engineering experience - 2+ years of Qt/QML development experience - 1+ year of Automotive project experience (IVI, Cluster, Cockpit, or HMI) - Strong C++11/14 knowledge - Strong Qt Framework and QML knowledge - Experience with Embedded Linux development - Experience with Git and code review processes - Good understanding of object-oriented design principles Requirements - Experience working in an Agile/Scrum environment - Knowledge of automotive communication protocols (CAN, SOME/IP) - Experience with Android Automotive OS (AAOS) - Knowledge of CommonAPI and/or FRANCA - Understanding of ASPICE processes - Excellent interpersonal skills and high motivation Company Description

Role Description - Design and implement scalable data processing pipelines for ML training and validation - Build and maintain feature stores with support for both batch and real-time features - Develop data quality monitoring, validation, and testing frameworks - Create systems for dataset versioning, lineage tracking, and reproducibility - Implement automated data documentation and discovery tools - Design efficient data storage and access patterns for ML workloads - Partner with data scientists to optimize data preparation workflows Qualifications - 7+ years of software engineering experience, with 3+ years in data infrastructure - Strong expertise in GCP's data and ML infrastructure: - BigQuery for data warehousing - Dataflow for data processing - Cloud Storage for data lakes - Vertex AI Feature Store - Cloud Composer (managed Airflow) - Dataproc for Spark workloads - Deep expertise in data processing frameworks (Spark, Beam, Flink) - Experience with feature stores (Feast, Tecton) and data versioning tools - Proficiency in Python and SQL - Experience with data quality and testing frameworks - Knowledge of data pipeline orchestration (Airflow, Dagster) Requirements - Experience with streaming systems (Kafka, Kinesis) - Experience with GCP-specific security and IAM best practices - Knowledge of Cloud Logging and Cloud Monitoring for data pipelines - Familiarity with Cloud Build and Cloud Deploy for CI/CD - Experience with streaming systems (Pub/Sub, Dataflow) - Knowledge of ML metadata management systems - Familiarity with data governance and security requirements - Experience with dbt or similar data transformation tools

Role Description As a Machine Learning Engineer on our Sourcing team, you will work on developing and deploying ML and AI solutions in production. You’ll be collaborating with a cross-functional product team to drive value and better customer experiences for our Sourcing product, which connects employers with new talent through AI. This MLE will help us improve our automated outreach quality, building new agentic experiences, and LLMOps reliability and infrastructure. - Develop a variety of models from prototype to production. - Implement best practices for evaluation, monitoring, and Machine Learning Operations (MLOps). - Partner with cross-functional teams across the company on model inference and product applications. - Participate in code review and process innovation for the team's software engineers and Machine Learning Engineers, and document your work. - Prioritize technical and product thinking, and show ownership for team priorities and Indeed’s mission to help people get jobs. Qualifications - Requires a Bachelor's degree in Computer Science, Mathematics, Statistics, or related field and a minimum of 2 years of related experience; or an advanced degree without experience. - Familiarity with agent orchestration frameworks and LLM observability tools. - Significant domain knowledge with training, fine-tuning, and evaluating language models. - Knowledge and practical experience working on ML/LLM/Deep Learning models (like Torch, Tensorflow, Huggingface, XGBoost, etc.). - Python and SQL programming experience. Salary Range Disclaimer The salary range for this role reflects the minimum and maximum compensation for the role. Offers are typically made between the range minimum and the range midpoint. Actual compensation will be determined based on job-related skills, experience, and expertise, as evaluated during the interview process. The range(s) listed is just one component of Indeed's total compensation package for employees. Other rewards may include quarterly bonuses, Restricted Stock Units (RSUs), a Paid Time Off policy, and many region-specific benefits. Compensation may also vary based on where a role is performed, as work locations are grouped into geographic pay tiers to reflect cost of labor differences in different geographic markets. - Tier 1 - United States of America: $118,000 - $176,000 USD per year - Tier 2 - United States of America: $130,000 - $196,000 USD per year - Tier 3 - United States of America: $143,000 - $215,000 USD per year - Tier 4 - United States of America: N/A - Tier 5 - United States of America: $163,000 - $245,000 USD per year EEO and Accommodations Indeed is deeply committed to building a workplace and global community where inclusion is not only valued, but prioritized. We’re proud to be an equal opportunity employer, seeking to create a welcoming and diverse environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, family status, marital status, sexual orientation, national origin, genetics, neuro-diversity, disability, age, or veteran status, or any other non-merit based or legally protected grounds. Indeed provides reasonable accommodations to qualified individuals with disabilities in the employment application process. To request an accommodation, please visit this link . If you are requesting accommodation for an interview, please reach out at least one week in advance of your interview. Inclusion & Belonging Inclusion and belonging are fundamental to our hiring practices and company culture, forming an integral part of our vision for a better world of work. At Indeed, we’re committed to the wellbeing of our employees and on a mission to make this the best place to work and thrive. We believe that fostering an inclusive environment where every employee feels respected and accepted benefits everyone, fueling innovation and creativity. - We value diverse experiences, including those who have had prior contact with the criminal legal system. - We are committed to providing individuals with criminal records, including formerly incarcerated individuals, a fair chance at employment. - Those with military experience are encouraged to apply. - Equivalent expertise demonstrated through a combination of work experience, training, military experience, or education is welcome.

Role Description We are looking for a Staff Machine Learning Engineer to define and build the machine learning platform architecture for the organization. This team will create the enabling layer that allows Data Scientists to self-serve deployment, experimentation, batch scoring, online inference, monitoring, and safe rollout workflows. This is a platform creation role, not a platform operations gatekeeper role. The success metric is not how many deployments the team executes directly, but how effectively the platform allows domain Data Scientists to deploy independently through highly reliable self-service workflows. The initial Staff MLE hires will establish the architectural foundations, engineering standards, reusable tooling strategy, and platform roadmap that the Senior MLE team will scale. This role is based out of Toronto. Qualifications - Master’s degree in Computer Science, Engineering, Distributed Systems, Machine Learning, or another related STEM field - Bachelor’s degree with exceptional relevant platform engineering depth is acceptable Requirements - 5+ years of hands-on experience in ML engineering, platform engineering, or large-scale production ML systems - Proven experience designing platform architecture and reusable ML tooling standards - Experience building self-service internal platforms, developer tooling, or ML deployment frameworks - Strong experience enabling applied Data Science teams through reusable infrastructure rather than centralized service models - Experience leading architecture decisions and mentoring engineers - Deep expertise in ML systems architecture across batch and low-latency real-time serving - Strong hands-on experience with Docker, Kubernetes, infrastructure automation, and cloud-native ML workloads - Strong expertise in model lifecycle tooling including MLFlow, registries, validation gates, and promotion workflows - Advanced experience designing CI/CD, canary, rollback, and deployment safety systems for ML - Experience with feature stores, online/offline feature parity, and low-latency feature retrieval - Strong Python engineering standards and ability to write production-grade frameworks and SDKs - Demonstrated ability to define technical direction for platform teams - Strong mentorship track record for Senior and mid-level MLEs - Strong cross-functional influence with DS, data platform, and product engineering teams - Bias toward building self-service systems that maximize organizational leverage Preferred Qualifications - Experience building greenfield ML platforms from zero to scaled enterprise adoption - Experience supporting self-service recommendation, ranking, forecasting, and optimization systems - Familiarity with Databricks, Azure ML, SageMaker, Vertex AI, or equivalent ML platforms - Experience building internal developer portals, CLIs, or workflow SDKs - Strong platform product thinking focused on usability, adoption, and DS productivity Company Description Scientific Games is the global leader in lottery games, sports betting and technology, and the partner of choice for government lotteries. From cutting-edge backend systems to exciting entertainment experiences and trailblazing retail and digital solutions, we elevate play every day. We push game designs to the next level and are pioneers in data analytics and iLottery. Built on a foundation of trusted partnerships, Scientific Games combines relentless innovation, legendary performance, and unwavering security to responsibly propel the global lottery industry ever forward.