Member of Technical Staff – Model Training

Benefits

Parental leave

Not verified

Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

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Salary

$175K-$350K not verified - timestamp not recorded

401(k) match

Reported not verified - source URL not recorded; timestamp not recorded

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Market context

U.S. role benchmark (BLS OEWS)

$57,704 U.S. median for this role

Projected growth (BLS Employment Projections)

+0.9% - Slower

355% above the BLS role benchmark for sales aggregate.

Posted salary is far from this role benchmark; treat it as low confidence.

Matched to SOC 41-2031 - Sales aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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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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