• Build and Train ML Pipelines: Develop, train, and fine-tune machine learning models for multimodal sensor data (e.g., vision, LiDAR). • Support Model Deployment: Implement scalable data preprocessing and augmentation pipelines. • Data Mining & Analysis: Help develop embedding-based search tools and "active learning" workflows. • Monitor Production Performance: Help build and maintain dashboards to monitor model health. • Learn and Apply Best Practices: Follow software engineering standards for ML code. • Collaborate Across Teams: Work closely with senior engineers and machine learning engineers.

Role Description As a Staff Machine Learning Engineer at Striveworks, you will be challenged—and trusted—on day one to be both a core contributor and a customer-facing technical leader on the projects and direction of the company. In this mission-critical work, you will design AI-powered solutions, you will bring them into being—from data shaping and ingestion, through analytics, to end-user delivery—and you will inform future platform capabilities. Your day-to-day will include: - Working with customers, engineers, and other stakeholders to define clear requirements that solve the customers’ problems and leverage the capabilities of our AI operations platform. - Translating requirements into a technical approach, design, scoping estimate, and execution plan. - Leading execution teams to achieve on-time completion of project deliverables mapped to customer business value while making key individual contributions throughout the process. - Designing, orchestrating, and automating complex data pipelines and algorithms within modern architectures (cloud, event-driven, microservices, etc.). - Guiding the development of machine learning models and custom analytics applied to image, video, text, geospatial, time series, and structured data. - Raising insights, opportunities, challenges, and feedback in order to improve group-level practice, capture reusable functionality, expand company opportunities, and accelerate time to value. - Conducting mission-critical fieldwork and interfacing with customers and other stakeholders at their work sites. This position offers a fully remote work environment, or you can work hybrid/on site at our office in northwest Austin, TX. You will be expected to travel up to 20% of the time. Qualifications - Advanced degree in data science, machine learning, computer science, or a related discipline and 10+ years of relevant experience. - Broad proficiency in programming languages common to machine learning (excellence in Python is essential, as is knowledge of libraries like TensorFlow, PyTorch, and scikit-learn) and systems programming (e.g., Go, Rust, C++, Java, Scala, etc.). - Proficiency in the design and delivery of algorithms, data structures, and production analytics and in the use of design patterns in cloud environments. - Demonstrated experience defining, scoping, planning, and delivering complex technical solutions in production environments. - Proficiency with modern software engineering tools and processes (Agile, version control, issue tracking, CI/CD, debugging, etc.). - Demonstrated ability to lead, manage, and mentor small cross-functional teams that work across office, remote, and customer sites. - Ability to communicate complex topics with professionalism, competence, and clarity to internal and external stakeholders (both technical and non-technical) via documents, presentations, and conversations. - Active Secret (or above) US security clearance. - Due to the nature of this role, candidates must have US citizenship. Requirements - Experience with Kubernetes (k8s) and horizontally scaled architectures. - Knowledge of messaging systems such as NATS, Kafka, RabbitMQ, or similar. - Experience building AI agents and workflows. - Experience with complex and varied data types, such as multi-spectral imagery, full motion video, acoustic or sonar signals, synthetic aperture radar, or hardware telemetry. - Experience delivering technology solutions in secure government environments. Benefits - Medical/dental/vision insurance. - Voluntary life, long-term disability, accident, and hospital indemnity insurance. - HSA and FSA (including dependent care FSA) plans. - 401(k) plan. - Unlimited PTO. - Paid parental leave.

• Collaborate with cross-functional teams to assist in the design and productionization of machine learning, ML ops, traditional AI, LLMs, and agentic AI solutions. • Support the implementation of application integrations to leverage newly built machine learning, ML ops, traditional AI, LLMs, and agentic AI products. • Assist in building solutions that improve the delivery speed and scalability of data and product pipelines. • Under minimal supervision, work on leveraging managed and serverless cloud offerings for application solutions and data engineering pipelines. • Support client-facing teams by preparing technical documentation and assisting in project communications. • Stay up to date with the latest advancements in data science, machine learning, and AI technologies.

• Lead and drive ambitious research initiatives that advance the state of the art in computer vision, multimodal understanding, and visual generation. • Develop novel models, algorithms, and training methodologies for challenging vision problems such as image understanding, video understanding, visual search, scene representation, segmentation, detection, generation, and multimodal reasoning. • Translate cutting-edge research into practical model improvements that can shape product direction and unlock new user experiences. • Design, implement, train, and optimize large-scale vision and multimodal foundation models across diverse datasets and tasks. • Partner closely with applied scientists, ML engineers, and product teams to move research from exploration to production-ready systems. • Set technical direction for high-impact research areas, identifying promising bets and influencing longer-term strategy for vision and multimodal AI. • Mentor other scientists and engineers, raise the quality bar for research, and help build a strong scientific culture across the team. • Stay at the forefront of research in computer vision, multimodal learning, generative modeling, and foundation models, and apply emerging techniques to real-world problems.