Machine Learning Engineer

BetterHelp

2h•$150,000 - $180,000•Remote

About The Position

BetterHelp is looking for a Machine Learning Engineer to help build and scale the next generation of AI-powered experiences across our platform. This role will focus on Natural Language Processing (NLP), Large Language Models (LLMs), evaluation systems, and AI safety infrastructure. We're looking for someone with a strong foundation in machine learning and NLP who is excited about building reliable, scalable AI systems in production. The ideal candidate has experience working with language models, evaluating model quality, implementing guardrails, and optimizing inference systems for real-world applications.

Requirements

3+ years of industry experience building machine learning systems.
Strong Python programming skills.
Experience working with NLP and Large Language Models.
Experience with PyTorch (preferred) or TensorFlow.
Strong understanding of deep learning fundamentals, transformers, embeddings, and modern NLP architectures.
Experience evaluating machine learning models and designing quality measurement frameworks.
Experience fine-tuning language models for production use cases.
Familiarity with model serving, inference optimization, and deployment workflows.
Experience working with SQL and large-scale datasets.
Excellent communication and collaboration skills.

Responsibilities

Develop and improve NLP systems and language model-powered experiences.
Fine-tune and optimize open-source and proprietary language models for domain-specific use cases.
Evaluate model performance and identify opportunities for quality improvements.
Design and implement evaluation frameworks to measure model quality, reliability, and business impact.
Build automated testing pipelines to identify regressions, quality drops, hallucinations, and failure modes.
Develop metrics and monitoring systems to continuously assess model performance in production.
Design and implement guardrails that improve model reliability, safety, and consistency.
Build systems to detect unsafe outputs, prompt injection attempts, abuse patterns, and fraud-related behaviors.
Partner with product and engineering teams to ensure AI systems behave predictably and responsibly.
Optimize inference performance through quantization, distillation, batching, and model serving improvements.
Deploy and maintain production-grade ML systems running on GPU infrastructure.
Improve latency, scalability, and operational efficiency of LLM-powered applications.
Partner closely with Product, Engineering, and Data Science teams to identify opportunities where AI can improve user experiences.
Translate business requirements into scalable ML solutions.
Communicate technical concepts clearly to both technical and non-technical stakeholders.