Senior Principal Software Engineer, Machine Learning

This is a fully remote position, open to applicants in United States.

📋 Description

• Take charge of the technical strategy for the ML Platform, encompassing feature store, model hosting and serving, experimentation, and training infrastructure.

• Influence architectural choices regarding scalability, reliability, latency, and cost-effectiveness.

• Spearhead the design and execution of extensive platform projects from initial concept to full production.

• Collaborate across ML, data, and infrastructure teams.

• Identify and address systemic issues related to online/offline feature parity, model deployment, experimentation speed, GPU utilization, and inter-team dependencies.

• Uphold a high standard of engineering quality through active code contributions, design evaluations, and mentorship.

• Work alongside ML engineering, data science, product, and platform leadership to align ML strategy with technical roadmaps.

• Establish secure pathways for model deployment, including feature registration, canary rollout, monitoring, and rollback processes.

• Utilize AI-enhanced development tools to boost development speed and enhance code quality.

⛳️ Requirements

• Over 10 years of experience in delivering complex backend or infrastructure systems at scale.

• Direct involvement in building or managing core ML infrastructure, such as feature stores, model serving, experimentation platforms, or training orchestration.

• Proficiency in a contemporary backend programming language, preferably Java or Kotlin.

• Extensive knowledge of distributed systems principles, including consistency, latency, throughput, fault tolerance, and observability.

• Strong grasp of data modeling, query languages, and the online/offline data patterns that support ML systems.

• Proven track record of technical leadership with the ability to foster cross-team alignment and influence engineering, product, and business stakeholders.

• Bachelor's degree in Computer Science or a related discipline, or equivalent hands-on experience.

• Desirable experience with ML platform components such as Tecton, MLflow, SageMaker, or Databricks.

• Desirable experience in creating experimentation or A/B testing platforms at scale.

• Desirable familiarity with Kafka, Flink, or Spark Streaming.

• Desirable experience in deploying LLMs or deep-learning models in production, GPU capacity planning, and inference optimization.

• Desirable experience in supporting internal developer-facing platforms with a product-oriented mindset.

🏝️ Benefits

• Competitive salary and benefits packages.

• Cash compensation that may include overtime and bonuses/commissions if applicable.

• Equity options available.

• Flexible work arrangements and a hybrid work model that respects individual needs.

• Access to AI tools across various disciplines to facilitate quicker, more independent, and higher-quality work.

• Reasonable accommodations provided for individuals with disabilities.