Machine Learning Engineer

Who are we and why should you join us?

BetterHelp is on a mission to remove the traditional barriers to therapy and make mental health care more accessible to everyone. Founded in 2013, we are now the world’s largest online therapy service, providing affordable and convenient therapy across the globe. Our network of over 30,000 licensed therapists has helped millions of people take ownership of their mental health and change their lives forever. And we’re not stopping there – as the unmet need for mental health services continues to grow, BetterHelp is committed to being part of the solution.

As a Machine Learning at BetterHelp, you’ll join a diverse team of licensed clinicians, engineers, product pros, creatives, marketers, and business leaders who share a passion for expanding access to therapy. And as a mental health company, we take employee mental health just as seriously as we do our mission. We deeply invest in our team’s well-being and professional development, because we know that business and individual growth go hand-in-hand. At BetterHelp, you’ll carve your own path, make an immediate impact, and be challenged every day – with a supportive community behind you the whole way.

What are we looking for?

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.

What will you do?

• 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.

What will you NOT do?

• You will NOT worry about "runway", "cash left", or "how much time we have until the next round". We have the startup DNA but we're fully backed and funded, all the way to success.
• You will NOT be confined to your "job". You will get involved in product, marketing, business strategy, and almost everything we do.
• You will NOT be bogged down by office politics, ego, or bad attitude. Only positivity and collaboration matter.