Senior Software Engineer - Machine Learning (Remote US)

Staff Applied Machine Learning Engineer (Remote USA)

***This role requires expert level in development and deployment of machine learning models***

***US Citizenship or Greencard (Permanent US Residency) is required due to US Government contract requirements***

***You must be domiciled in the U.S. (must live permanently and maintain home address in the US)***

***As a step in the application process you will be required to provide legally authorized Identification to show verification of eligibility through third party provider CLEAR ID***

As a Senior Software Engineer, Machine Learning, you will design, build, and evolve the core AI capabilities that power intelligent experiences across our enterprise AI platform. You will integrate modern foundation models into production systems, develop reusable AI capabilities, and continuously improve their quality, reliability, and performance. Working closely with cross-functional teams, you will transform advances in AI into secure, scalable, and production-ready capabilities that accelerate innovation across the platform.

• Design, build, and maintain the core AI capabilities that power intelligent experiences across the platform.
• Develop reusable capabilities for model integration, reasoning, multimodal understanding, structured generation, evaluation, and other foundational AI functionality.
• Optimize the AI systems that power these capabilities through experimentation, quantitative evaluation, context engineering, and iterative refinement.
• Apply your production software engineering experience to build backend services, APIs, and distributed AI systems.
• Partner closely with cross-functional partners to deploy scalable AI services that enable product teams to rapidly build AI-powered experiences.
• Rapidly prototype emerging AI techniques and translate successful experiments into production-ready platform capabilities.
• Support production systems by debugging failures and continuously improving latency, reliability, observability, and operational performance.
• Stay current with advances in AI engineering, foundation models, and applied machine learning, applying them where they create meaningful customer value.
• Optimize AI systems for performance, latency, reliability, scalability, and cost.
• Contribute to a collaborative engineering culture focused on ownership and continuous learning.

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience.
• 5+ years of experience building production software systems.
• Strong software engineering skills with experience in Rust, Python, or similar programming languages.
• Proven ability to build scalable backend services, distributed systems, APIs, or data-intensive applications.
• Hands-on experience integrating large language models or other foundation models into production applications.
• Ability to design, evaluate, and improve AI systems through experimentation, quantitative evaluation, and continuous iteration.
• Familiarity with modern AI engineering patterns such as structured outputs, tool calling, context engineering, multimodal systems, or AI workflows.
• Strong understanding of software engineering best practices, including testing, observability, reliability, and maintainability.
• Demonstrated ability to take ownership of technically ambiguous projects from design through production.
• Excellent communication and collaboration skills with the ability to work effectively across cross-functional teams.
• Growth mindset and low ego, with a willingness to learn, adapt, and collaborate.

• Experience building reusable AI capabilities or shared AI services.
• Familiarity with deploying, optimizing, or serving open-source foundation models.
• Knowledge of evaluation frameworks, benchmarks, or datasets for AI applications.
• Experience with Kubernetes, cloud-native infrastructure, or distributed systems.
• Experience developing production software in Rust.
• Familiarity with retrieval-augmented generation (RAG), multimodal AI, AI agents, or model orchestration.
• Domain expertise building complex AI systems such as multimodal reasoning, long-running inference workflows, AI agents, or model orchestration.