Machine Learning Engineer

Role Overview:
We're looking for a Machine Learning Engineer who thrives at the intersection of research and production. You'll be joining a fast growing AI organization building scalable systems that power next generation language and reasoning models. You'll work on end to end pipelines: from prototyping novel algorithms, to deploying them in production, monitoring and optimizing them at scale.

What You'll Do:

• Collaborate with research teams to implement and refine machine learning/AI models (LLMs, multimodal, reasoning agents)

• Build robust ML pipelines: data collection, preprocessing, model training/finetuning, deployment and monitoring

• Work with production infrastructure: containerized environments, distributed systems, scalable compute resources

• Develop and deploy models in a cloud native setting (AWS/Azure/GCP) with production constraints (latency, throughput, reliability)

• Monitor model performance in production: latency, accuracy drift, cost, resilience

• Debug and optimize systems for scale: memory, compute, inference cost, streaming data

• Collaborate cross functionally: data engineers, software engineers, product teams, researchers

• Stay abreast of the latest ML/AI research and bring actionable improvements into production

What You Bring:

• M.Sc. or Ph.D. in Computer Science, Electrical Engineering, Mathematics or related field (or equivalent experience)

• 3+ years of hands on experience implementing ML/AI systems in production

• Strong programming skills in Python (and optionally C++/Go)

• Experience with ML frameworks (e.g., PyTorch, TensorFlow, JAX)

• Proven track record deploying models into production (cloud, containers, microservices)

• Familiarity with distributed computing, scalable architectures and production ML constraints

• Good understanding of ML operations: monitoring, model drift, post deploy maintenance

• Strong debugging and optimization skills: performance, cost, latency

• Excellent collaboration and communication skills

• Bonus: experience with large language models, retrieval augmented generation, and multimodal systems