We are looking for a Machine Learning Engineer (MLE) to work on building foundational language models and agentic AI systems that empower small and medium-sized businesses (SMBs) in their financial and operational journeys.
In this role, youll work alongside Machine Learning Engineers, AI scientists and product teams to build LLM based solutions for millions of small and medium busniesses around the world, and build foundational models, involving translate breakthrough research into production systems – designing, implementing, optimizing cost, latency, serving models that reason, act, and learn across real-world business contexts. Youll build scalable pipelines and infrastructure that drive impact on customers lives with the state-of-the-art AI from prototype to customer impact.
This is a unique opportunity for an engineer who thrives at the intersection of science and production, combining deep technical understanding of AI systems with the rigor and creativity needed to deliver robust, reliable, and high-performing products at scale.
Responsibilities:
Implement, optimize, and deploy foundation and agentic models for large-scale use in financial reasoning, decision support, and business automation.
Collaborate with MLE, AI scientists to operationalize research ideas, turning model architectures and training methods into efficient, production-grade implementations.
Design, build, and maintain ML pipelines for data ingestion, preprocessing, training, evaluation, and continuous improvement.
Optimize inference efficiency and model latency, ensuring production systems meet real-time performance requirements.
Contribute to retrieval, memory, and orchestration components that enable agentic reasoning and long-term context management.
Establish best practices for model monitoring, evaluation, and safe deployment, including automated testing and observability in production environments.
Drive applied innovation, bridging the gap between research prototypes and large-scale, reliable business solutions used by millions of customers.
In this role, youll work alongside Machine Learning Engineers, AI scientists and product teams to build LLM based solutions for millions of small and medium busniesses around the world, and build foundational models, involving translate breakthrough research into production systems – designing, implementing, optimizing cost, latency, serving models that reason, act, and learn across real-world business contexts. Youll build scalable pipelines and infrastructure that drive impact on customers lives with the state-of-the-art AI from prototype to customer impact.
This is a unique opportunity for an engineer who thrives at the intersection of science and production, combining deep technical understanding of AI systems with the rigor and creativity needed to deliver robust, reliable, and high-performing products at scale.
Responsibilities:
Implement, optimize, and deploy foundation and agentic models for large-scale use in financial reasoning, decision support, and business automation.
Collaborate with MLE, AI scientists to operationalize research ideas, turning model architectures and training methods into efficient, production-grade implementations.
Design, build, and maintain ML pipelines for data ingestion, preprocessing, training, evaluation, and continuous improvement.
Optimize inference efficiency and model latency, ensuring production systems meet real-time performance requirements.
Contribute to retrieval, memory, and orchestration components that enable agentic reasoning and long-term context management.
Establish best practices for model monitoring, evaluation, and safe deployment, including automated testing and observability in production environments.
Drive applied innovation, bridging the gap between research prototypes and large-scale, reliable business solutions used by millions of customers.
Requirements:
Masters degree in Computer Science, Machine Learning, or a related field (Ph.D. a plus).
Proven experience in building and deploying large-scale ML systems in production environments.
Strong proficiency in Python, with hands-on experience using PyTorch, JAX, or TensorFlow.
Expertise in distributed training and inference optimization (e.g., GPU/TPU scaling, mixed precision, model parallelism).
Familiarity with agentic system design, retrieval-augmented generation (RAG), or multi-step reasoning systems.
Experience with cloud and MLOps technologies such as Kubernetes, Docker, Ray, Airflow, or SageMaker.
Demonstrated ability to collaborate with research and product teams to deliver robust, high-impact AI capabilities.
Passion for building applied AI that simplifies complex business workflows and empowers entrepreneurs around the world.
Masters degree in Computer Science, Machine Learning, or a related field (Ph.D. a plus).
Proven experience in building and deploying large-scale ML systems in production environments.
Strong proficiency in Python, with hands-on experience using PyTorch, JAX, or TensorFlow.
Expertise in distributed training and inference optimization (e.g., GPU/TPU scaling, mixed precision, model parallelism).
Familiarity with agentic system design, retrieval-augmented generation (RAG), or multi-step reasoning systems.
Experience with cloud and MLOps technologies such as Kubernetes, Docker, Ray, Airflow, or SageMaker.
Demonstrated ability to collaborate with research and product teams to deliver robust, high-impact AI capabilities.
Passion for building applied AI that simplifies complex business workflows and empowers entrepreneurs around the world.
This position is open to all candidates.
