Pathos AI Logo

Pathos AI

AI Research Engineer

Reposted 3 Months Ago
Hybrid
San Francisco, CA, USA
Mid level
Hybrid
San Francisco, CA, USA
Mid level
The AI Research Engineer will design, implement, and improve AI systems for therapy, enhance evaluation methods, and collaborate with various teams to innovate and ensure safety in AI therapy.
The summary above was generated by AI
Mission

The Path is a research lab and consumer health company on a mission to provide therapy-grade help and coaching for anyone, anytime.

Over 3 billion people a year have a problem large enough to warrant therapy but the barriers to access - around price, time, geography, stigma, and language - are so large that less than 10% of these people are getting the help they need.

The Path exists because help shouldn’t depend on privilege, because intelligence should serve humanity, and because clarity is the first act of care.

Our purpose-built AI therapists provide evidence-based mental health support delivered through artificial intelligence, designed to get smarter with every session. The Path was built by a neuroscientist who understands how the brain actually changes. Most AI chat tools were not.

Every role here moves that work forward. Join a small, passionate in-person team in San Francisco building the AI and the clinical research behind it!

The Opportunity

Design, train, ship, iterate on, and innovate on the AI brains behind The Path’s AI Therapist. Combine research, data science, and engineering to create models, orchestration, and evaluation systems that make therapy conversations deeply effective, clinically grounded, and safe.

Note - Below contains the outcomes and competencies for the team. If you bring standout strengths in some areas but not all, you are still encouraged to apply.

Responsibilities
  • Improve quality of AI Therapy: Deliver measurable improvements in conversation quality, therapeutic alliance, and user outcomes through fine-tuning strategies, training data curation, building RL environments, new model architectures and other AI innovations.

  • Improve evaluation of AI quality: Improve on and maintain a robust eval stack that includes scripted tests, LLM-as-judge evaluations, human ratings, and safety checks. Improve automated regression testing, detection of defects, and observability (eg dashboards).

  • Own AI system. Build, maintain, and iterate on the production codebase that delivers AI therapy and supports the evaluation and iteration of our AI.

  • Productionize Models and Pipelines. Own The Path from notebook to production: training jobs, model packaging, deployment, monitoring, and rollback strategies. Keep latency, reliability, and cost within agreed budgets while enabling rapid iteration on new ideas.

  • Safety, Alignment, and Clinical Guardrails Work with clinicians and internal experts to encode clinical guidelines into prompts, reward functions, tools, and filters. Proactively identify and reduce harmful or low-quality behaviors through targeted experiments, red teaming, and mitigations.

  • Own Research Roadmap and Experiment Velocity Run high-quality experiments from hypothesis to analysis to improve our understanding of what matters and what works. Shape and execute a focused R&D roadmap.

  • Collaboration with Clinicians, Product, and Engineering. Translate product and clinical requirements into concrete model and system changes. Partner with full-stack product engineers so that new AI capabilities are easy to integrate and maintain in the product.

Competencies
  • LLM and Applied ML Depth. Demonstrates strong experience with large language models, including fine-tuning, training data design, and model selection. Knows how to move core metrics on conversation quality and user outcomes, rather than chasing generic benchmarks. Can look at evals, transcripts, and metrics and quickly form grounded hypotheses for improvement.

  • Ships clean, maintainable, quality code. No only do you know how transformers work, but you are also an engineer that has experience shipping production-level code and/or maintaining an AI system in production.

  • Data Engineering Skills. Can set up production-level data pipelines for training new models, evals, analysis, etc.

  • Scientific Mindset. You formulate hypotheses, and you are good at evaluating them (eg through experiments, data analysis, etc). You are consistently learning at the cutting edge, and you’re able to leverage and communicate those learnings to make the entire company more successful.

  • Ruthless Prioritizer. You are keenly aware of how to provide company value and to prioritize projects accordingly. Resistant to nerd-sniping.

  • Quality Obsessive: Refuses to ship subpar work, continuously improving the codebase.

  • Fast: Prioritizes speed by leveraging AI, breaking down complex tasks, shipping early, optimizing for learnings, iterating quickly, and avoiding over-engineering.

  • Strong communicator. You can work collaboratively in a positive way. Sees others perspectives. Strong opinions, loosely held. Focused on user/business value, not ego.

Great to have
  • Personal or other experience with therapy or coaching

  • Domain knowledge of psychology, neuroscience, therapy, or coaching.

Life at The Path
  • Work Model - San Francisco hybrid (Tue–Thur in office Union Square). Full-time

  • Benefits - comprehensive health/dental/vision/life insurance , flexible PTO, and WFH reimbursement

  • Our team thrives in a high-trust culture that fosters individuality, creativity, agency, and development

 
 
Our Commitment to Diversity and Inclusion

The Path is proud to be an equal opportunity employer. We celebrate diversity and encourage applications from everyone; especially those whose identities are traditionally underrepresented in tech. We do not discriminate on race, religion, color, gender expression or identity, sexual orientation, ancestry, national origin, citizenship, age, marital status, veteran status, disability status, political belief, or any other characteristic protected by law.

 
Application Process

We’re excited to review your application! If your experience aligns with business needs, our hiring team will reach out to schedule an initial screening call and walk you through the next steps of our interview process.

While we try our best to respond to everyone, the high volume of applications may mean we’re unable to provide individual updates if we decide not to move forward.

Similar Jobs

7 Days Ago
In-Office
106K-177K Annually
Senior level
106K-177K Annually
Senior level
Artificial Intelligence • Healthtech • Machine Learning • Natural Language Processing • Biotech • Pharmaceutical
Build and deploy production-grade AI and machine learning systems supporting biomedical research and drug discovery. Own technical solutions from architecture through deployment, including data pipelines, retrieval systems, knowledge systems, inference services, evaluation frameworks, and human-in-the-loop workflows. Partner with scientists and engineers to transform prototypes into scalable, secure, reliable platforms. Lead technical delivery, make architecture tradeoffs, improve engineering standards, mentor teammates, and promote responsible AI practices.
Top Skills: Agentic AiAi PlatformsAPIsCi/CdCloud ComputingContainersDistributed InferenceDistributed TrainingHigh-Performance ComputingInference OptimizationJaxKnowledge GraphsLarge Language ModelsMachine LearningMlopsModel ServingMultimodal LearningOrchestrationPythonPyTorchRetrieval-Augmented GenerationScientific Machine LearningVersion Control
7 Days Ago
In-Office
139K-232K Annually
Senior level
139K-232K Annually
Senior level
Artificial Intelligence • Healthtech • Machine Learning • Natural Language Processing • Biotech • Pharmaceutical
Build and deploy production-grade AI and machine learning systems supporting biomedical research, including multimodal models, intelligent agents, retrieval, knowledge systems, evaluation frameworks, and inference services. Own architecture through production, make scalability and reliability tradeoffs, establish responsible AI practices, and drive technical delivery. Collaborate with scientists, computational biologists, and platform teams while mentoring engineers and improving engineering standards, observability, and system performance.
Top Skills: Agentic AiAi EvaluationAPIsCi/CdCloud ComputingContainersDistributed InferenceDistributed TrainingGpu TrainingHigh-Performance Computing (Hpc)JaxKnowledge GraphsLarge Language Models (Llms)MlopsMultimodal LearningOrchestrationPythonPyTorchRetrieval-Augmented Generation (Rag)Scientific Machine Learning
16 Days Ago
Easy Apply
In-Office
San Francisco, CA, USA
Easy Apply
250K-400K Annually
Entry level
250K-400K Annually
Entry level
Enterprise Web • Mobile • Professional Services • Software
Conduct research on AI evaluation, rubrics, benchmarks, human judgment, preference data, and agent performance. Design rigorous experiments, validate measurement frameworks, build evaluation pipelines, and create long-horizon real-user benchmarks. Collaborate with engineers, delivery teams, customers, academics, and external labs, while publishing methods and benchmark findings. The role combines applied statistics, messy human data, research prototyping, technical writing, and deployment of research methods in live customer programs.
Top Skills: Ai Coding ToolsElixirKotlinLlm-As-Judge EvaluationPythonReactSwift

What you need to know about the San Francisco Tech Scene

San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.

Key Facts About San Francisco Tech

  • Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Google, Apple, Salesforce, Meta
  • Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
  • Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
  • Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
  • Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine

Sign up now Access later

Create Free Account

Please log in or sign up to report this job.

Create Free Account