Member of Technical Staff – Model Training
Inflection AI - Palo Alto, CA
Posted Jun 23, 2025
Benefits
- Parental leave
- Not verified
- Non-birth-parent leave
- Not verified
- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
- Not verified
- Relocation assistance
- Not verified
- Childcare support
- Not verified
- Learning budget
- Not verified
- Verification
- Not verified
- Salary
- $175K-$350K not verified - source not recorded; timestamp not recorded
- 401(k) match
- Not verified
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Market context
- U.S. role benchmark (BLS OEWS)
- $116,543 U.S. median for this role
- Projected growth (BLS Employment Projections)
- +9.8% - Much faster than average
125% above the BLS role benchmark for software engineering aggregate.
Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.
Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.
Schedule
- Shift type
- Not verified
- Weekend work
- Not verified
Application
- Cover letter
- Not verified
- Assessment
- Not verified
- Deadline
- Not stated
Where they hire
State eligibility is not yet verified.
About this role
Member of Technical Staff – Model Training Palo Alto, CA About Inflection AI Inflection AI is a Public Benefit Corporation empowering people with human-centered, emotionally intelligent AI. We're shaping the future of AI by combining emotional intelligence (EQ) and raw intelligence (IQ) to elevate people's potential. Inflection AI created Pi, the world's first emotionally intelligent AI, to help people work through decisions, emotions, and challenges. Pi is a personal AI agent powered by Inflection AI's foundation model, proving that AI can be personal, empathetic, and contextually aware. About the Role As a Model Training engineer, you will design, build, and scale the post-training pipelines that turn a general LLM into a brand-fluent, production-ready assistant. Your innovations in fine-tuning and preference optimization (RLHF, DPO, GRPO, RLAIF) will directly improve reliability, alignment, and cost. This is a good role for you if you: - Have hands-on experience training and fine-tuning large transformer models on multi-GPU / multi-node clusters. - Are fluent in PyTorch and its ecosystem tools (Torchtune, FSDP, DeepSpeed) and enjoy digging into distributed-training internals, mixed precision, and memory-efficiency tricks. - Have shipped or published work in RLHF, DPO, GRPO, or RLAIF and understand their practical trade-offs. - Care deeply about training tools, pipelines, and reproducibility-you automate the boring parts so you can iterate on the fun parts. - Balance research curiosity with product pragmatism-you know when to run an ablation and when to ship. - Communicate crisply with both technical and non-technical teammates. - Have a bachelor's degree or equivalent
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