Role Description The Machine Learning Engineer will design, develop, deploy, and maintain advanced machine learning models and data analysis systems to support specialized domain modeling and proprietary feature engineering. The person in this role will: - Conduct applied research and experimentation to design, train, evaluate, and refine machine learning models, including performing feature engineering, selecting modeling techniques, validating model performance, and documenting analytical methods. - Develop, test, and deploy production machine learning systems by managing the complete MLOps lifecycle, including experiment tracking, model versioning, containerization, orchestration of automated workflows, and monitoring of model performance in production environments. - Maintain and optimize machine learning infrastructure to support training, inference, and data processing workflows, including configuring cloud compute environments, tuning distributed computation jobs, and improving system efficiency and scalability. - Collaborate with business stakeholders to translate analytical requirements into quantifiable modeling objectives, define evaluation criteria, validate assumptions with data, and communicate analytical findings and modeling results. - Design, prepare, and review technical documentation, including model design specifications, architecture diagrams, data-flow documentation, and systems integration requirements to support maintainability and long-term scalability. - Develop AI-driven automation solutions using Large Language Models to streamline internal workflows, design LLM-based agentic processes, validate automated outputs, and measure accuracy and efficiency improvements. - Design and develop internal software tools and backend services that support data quality, enable analytical workflows, expose model insights to internal teams, and integrate with organizational data systems and APIs. No travel is required. Fully remote position (100%) from anywhere in U.S. reporting to HQ in San Francisco, CA. Qualifications - Master’s degree (or foreign equivalent) in data science, economics, mathematics or computer science. - 1 year of experience in any occupations in which required experiences were acquired (may be pre-Master’s). Requirements - 1 year of experience designing, training, and evaluating machine learning models using Python-based data science libraries, including experience performing feature engineering and developing predictive and descriptive models using tools such as scikit-learn and pandas. - Experience using machine-learning lifecycle tools, including MLflow (or similar platforms) for experiment tracking, reproducible training workflows, and model versioning. - Experience using Docker for containerization to package machine learning pipelines and ensure reproducible deployment environments. - 1 year of experience orchestrating data processing and machine learning workflows, including scheduling, monitoring, and managing dependencies using Apache Airflow. - 1 year of experience using CI/CD tools to automate model training, testing, and deployment processes. - 1 year of experience configuring and optimizing cloud compute environments to support training, inference, and large-scale data processing tasks. - 1 year of experience developing AI-driven automation workflows using language models, including integrating language-based model components into analytical or operational processes. - Experience developing backend services and APIs using FastAPI, REST, or GraphQL to support data access, model serving, and analytical tooling. - 1 year of experience working with SQL databases, including PostgreSQL, and cloud data platforms (e.g., Snowflake), including writing analytical queries and implementing data-quality validation workflows. - Experience using distributed data-processing tools, including Spark, for large-scale data transformation and feature-engineering operations. Benefits - Salary offered: From $196,776 per year. - Job location: San Francisco, CA. To Apply Send resume to: Eryn@alt.xyz (write “Machine Learning Engineer” in subject line).

• Build Production-Grade Evaluation Systems: Design and implement comprehensive evaluation frameworks that measure agent performance, track improvements over time, and ensure our AI systems deliver consistent value to customers • Drive Experimentation-to-Production Pipeline: Own the entire ML lifecycle from prototype to production, building scalable systems that enable rapid iteration while maintaining reliability and performance in customer environments • Enable Cross-Team ML Integration: Work closely with product teams to seamlessly integrate ML capabilities into customer-facing features, ensuring technical excellence translates into user value and product differentiation • Optimize AI Agent Performance: Continuously improve our AI agents through systematic experimentation, prompt engineering, and architectural enhancements, measuring success through customer impact and system performance • Scale ML Infrastructure: Build the foundational ML systems, monitoring, and tooling that will support our growth from startup to scale, ensuring we can deploy new capabilities quickly without compromising quality • Partner with Engineering Leadership: Collaborate directly with our CTO through regular check-ins and strategic alignment while operating with high autonomy and self-direction in day-to-day execution • Mentor Through Excellence: Provide natural mentorship to junior ML engineers through code reviews, technical guidance, and sharing practical experience from building production ML systems

• Design machine learning solutions and tools across the full ML lifecycle • Contribute to all areas of our data platform • Work closely with other machine learning engineers and data engineers

Role Description We are building the next generation of AI-driven game experiences, running generative models on-device, right where the players are — on phones, tablets, laptops, and desktops. As a Senior Machine Learning Engineer for On-Device & Mobile AI, you will take state-of-the-art multi-modal models and make them run fast, small, and reliably on mobile and constrained hardware. This is a deeply hands-on role. You will own the optimization and deployment of significant parts of the inference stack, shaping the latency, quality, memory footprint, and battery profile of AI features experienced by billions of players. What you'll be doing - Inference & On-Device Optimization: - Own the optimization pipeline for the models you ship: model export, graph transformation, operator fusion, memory-layout planning, and hardware-specific tuning. - Apply quantization (INT4/INT8/FP16), weight sharing, structured/unstructured pruning, and knowledge distillation. - Do low-level performance work: write and tune WebGPU compute shaders and native kernels; profile with browser and platform tools. - Apply efficiency techniques as engineering levers to meet budgets on target SKUs. - Runtime & Systems Integration: - Work with WebGPU-targeted inference runtimes and extend or build glue code where necessary. - Build parts of the integration between the ML runtime and the game engine. - Build supporting engineering for your components: model packaging, on-device fallbacks, crash/quality telemetry, and automated on-device benchmarking. - Research Productionization: - Partner with research scientists to turn novel CV and multi-modal architectures into deployable implementations. - Provide a feedback loop into research: surface hardware constraints and op-support gaps early. - Track breakthroughs in efficient inference and assess them pragmatically. - Collaboration & Engineering Quality: - Contribute to engineering best practices, code-review standards, and performance-regression gates. - Support a culture of measurement: track KPIs for latency, quality, memory, and power. - Partner with platform engineers, product managers, and runtime teams. - Share knowledge and mentor junior and mid-level engineers. Qualifications - 5+ years in software/ML engineering, with meaningful time focused on on-device / edge inference or real-time, performance-critical systems. - Production deployment of transformer- and/or diffusion-based models on mobile, desktop, or embedded hardware. - Hands-on experience with at least one major inference runtime and a working understanding of operator fusion, memory layout, and runtime scheduling. - Low-level performance engineering: solid command of at least one GPU/compute API and the profiling tools to go with it. - Working knowledge of model-optimization techniques and the judgment to apply them effectively. - Understanding of target hardware: mobile SoCs and/or desktop/laptop GPUs. - Strong Python for export pipelines and training-side tooling; familiarity with core languages of a browser-native runtime is a plus. - Working fluency with the models you deploy. - A collaborative working style: clear communication, reliable delivery, and a willingness to support and learn from teammates. Requirements - Experience shipping world-model, neural-rendering, or real-time generative pipelines on device. - Hands-on experience deploying models through WebGPU. - Game-engine or real-time-graphics background. - Contributions to open-source ML inference frameworks or GPU/compute libraries. - Familiarity with compiler stacks for custom kernel generation and graph optimization. - Experience with on-device benchmarking infrastructure and performance-regression CI. - Proficiency in C++/Objective-C/Swift for runtime integration. Benefits - Comprehensive health, life, and disability insurance. - Commute subsidy. - Employee stock ownership. - Competitive retirement/pension plans. - Generous vacation and personal days. - Support for new parents through leave and family-care programs. - Office food snacks. - Mental Health and Wellbeing programs and support. - Employee Resource Groups. - Global Employee Assistance Program. - Training and development programs. - Volunteering and donation matching program.