Staff Software Engineer, Merlin

• You are a senior individual contributor with deep expertise in building the systems that make large-scale machine learning possible in production
• You've experience in the selection of optimum foundation models, adopt/tune for target application needs, designed required data pipelines that handle real-world messiness, built training infrastructure that scales reliably, and created evaluation frameworks that give engineers genuine confidence in their development
• You care about the craft of software engineering as much as you care about AI models — you know that great AI systems are only as good as the infrastructure underneath them
• You enjoy operating with significant autonomy, influencing technical decisions across multiple teams, and mentoring engineers who are earlier in their careers
• Most importantly, you want to work on a problem that genuinely matters: putting safe autonomous aircraft into the skies
• 8+ years of software engineering experience, with a substantial portion focused on state of the art and next-gen AI model development, ML infrastructure, MLOps, data engineering, or AI platform development
• Experience in development of the AI-first Autonomy software tech stack that includes Perception, Behavior Planning, Prediction and Actuation ( based on Transformer architectures)
• Demonstrated ability to design and deliver large-scale, production-grade systems independently — from initial architecture through deployment and operation
• Deep proficiency in Pytorch, TensorFlow and at least one systems language such as C++,C, Rust, or Go
• Hands-on experience with distributed training frameworks (Python extensions, JAX, or equivalent) and the infrastructure required to run them reliably at scale
• Experience in onboarding trained models into edge environments consisting of High performance compute System-on-Chips
• Strong background in data pipeline design, including streaming and batch processing, data versioning, and handling high-volume, heterogeneous sensor data
• Experience building model evaluation and validation frameworks leveraging MLOps frameworks (like Weights and Biases. MLFlow etc) that go beyond accuracy metrics to assess real-world reliability and safety-relevant behavior
• Proven track record of influencing technical direction across teams and driving alignment on shared infrastructure and standards
• Excellent written communication skills, with the ability to produce clear design documents and architecture proposals that hold up to rigorous review
• Comfort working in a fast-moving startup where the scope of problems and priorities evolve as the product matures

Desirable

• Technically strategize on Foundation model selection, refactor, design, build, and maintain Merlin's core AI models,training and inference infrastructure, including distributed training pipelines, experiment tracking, and model registry systems
• Define and drive standards and benchmarks for model evaluation, benchmarking, and regression testing to ensure AI systems meet safety and performance thresholds before deployment
• Architect and own the foundational data pipeline that ingests, processes, labels, and versions flight data for use across all autonomy and AI teams
• Identify and resolve systemic technical bottlenecks that slow down AI development velocity across Merlin's engineering organization
• Collaborate with AI infra, Simulation, and Flight Software teams to define interfaces and shared abstractions that make the broader stack more coherent and maintainable
• Mentor, build and technically guide junior and mid-level engineers on the AI Foundation team, conducting design reviews and raising the overall quality of the team's output
• Lead technical scoping and estimation for large AI foundational model projects, breaking ambiguous requirements into actionable engineering plans
• Evaluate and adopt relevant open-source tooling, frameworks, and research, contributing back where appropriate
• Document architecture decisions, system designs, and operational runbooks to a standard that supports safety review and long-term maintainability
• Contribute to hiring by conducting technical interviews and helping define the engineering bar for the AI Foundation team