Member of Technical Staff - Post-Training Engineer

What We're Looking For

• 3–7 years in ML engineering with hands-on post-training experience: you've personally run SFT or preference-optimization jobs on open-weight models and shipped the result, not just read the papers.
• Depth in at least one modality beyond text — vision-language, speech, video, or geospatial — and the appetite to expand into the others. Nobody arrives knowing all of them; we care that you've gone deep once and can do it again.
• Fluency with the modern training stack: PyTorch, Hugging Face ecosystem (transformers, PEFT, TRL or similar), experiment tracking, GPU training workflows.
• Strong evaluation instincts — you design the eval before the training run, and you explain regressions from error analysis, not vibes.
• Data-centric mindset: you know most post-training wins come from better data, and you have the tooling sense to build curation pipelines.
• Solid Python and the engineering discipline to make training reproducible.

Nice to Have

• Experience tuning models for agentic behavior: tool use, function calling, multi-turn dialogue, or RL on agent trajectories.
• VLM fine-tuning (Qwen, Nemotron, Gemma class) or speech-model adaptation (Whisper-class ASR, speech LLMs, TTS) for Indic languages.
• DPO/RLHF/GRPO experience, reward modeling, or preference-data design.
• Inference optimization: vLLM, quantization, multi-adapter serving.
• Remote sensing, GIS, or video understanding background.
• Domains where provenance and correctness are non-negotiable — legal, fintech, healthcare.

Your First 90 Days

Days 1–30: Ground truth. Ship an improvement to a production model in your first two weeks. Read documents, listen to call recordings, study failure transcripts until you understand why this data breaks pretrained models. Own quality for one model surface end to end.

Days 31–60: Own a modality. Take full ownership of one post-training track — document VLMs, voice, or agent behavior — including its data pipeline, eval suite, and a measurable quality lift shipped to production.

Days 61–90: Shape the stack. Make the call on a structural bet — preference optimization rollout, a new modality's training approach, adapter consolidation — backed by evidence from your first 60 days. By now you should be setting post-training direction across Terra, not just executing it.

Why This Role

• Post-training scope that frontier labs split across whole teams — text, vision, speech, and agents, all yours to shape.
• A data distribution nobody else has: your work can't be replicated by anyone scraping the internet.
• Direct stakes — Terra's judgments decide whether property purchases are safe, with lenders and families relying on the output.
• Small, senior team; backed by Y Combinator and top investors, with real revenue and real users.

A meeting lands on your calendar

If they’re a yes, I book the chat. You show up — that’s the whole job-hunt.