AI Engineer - Training & Alignment

Eekee AI is building the first proactive, pre-EAP mental-health layer for the modern workplace a private AI that helps employees build meaning, clarity, and resilience before burnout becomes a crisis.

We're not building another meditation app.

We're not building another reactive support tool.

Were building the product that sits before all of that the first line of mental-health engagement inside companies.

Role Description

Own training, alignment, and deployment of our meaning-at-work coach. Youll design data pipelines, fine-tune models, build safety layers, set evals, and ship to production.

What Youll Do

• Build data flow: sourcing, cleaning, consent/PII scrubbing, augmentation, labeling guidelines
• Fine-tune/align (SFT, DPO/RLHF) for coach behavior: listen reflect one powerful question next step
• Implement safety: crisis/HR escalation classifiers, toxicity/harassment filters, privacy guardrails
• Add context: retrieval for work scenarios; prompts/tools for Question of the Day and Vent
• Create evals: conversation quality, protocol adherence, safety, bias, latency, cost
• Optimize inference: distillation, quantization, caching, batching; observable, canaried rollouts
• Document: model cards, red-team results, alignment notes

Minimum Qualifications

• 1+ years applied ML/LLMs; shipped fine-tuned models
• Strong in PyTorch/JAX and serving (vLLM/Triton/Ray/K8s)
• Experience with SFT/DPO/RLHF, synthetic data, eval harnesses
• Built safety/quality classifiers and RAG systems
• Pragmatic on latency/cost; solid profiling chops
• Clear technical writing

Nice to Have

• Coaching/org-psych or agent-design background
• On-device/edge inference; multilingual safety
• Model cards, red-teaming, compliance awareness
• Zero-to-one startup experience

Stack

Models: Gemma/Anthropic, Llama/Mistral, LoRA/QLoRA, GGUF

Tooling: PyTorch, HF, vLLM, Triton, Ray, W&B

RAG: embeddings + FAISS/pgvector, guardrails

Infra: K8s, cloud GPUs, Terraform; analytics + experiment tracking

30/60/90 Day goals

• 30: Offline eval harness; baseline fine-tune matching coach style; first safety classifiers; model card v0
• 60: Prod daily question + vent flows w/ guardrails; cut token cost by 3050%; A/B live
• 90: Distilled model in prod; red-team playbook; documented alignment/escalation; +20% conv-quality score