OverviewWe are looking for a passionate ML Engineer to implement AI solutions aimed at achieving business goals (CLAi automates a multitude of business operations simple and complex alike. That includes document reading, data entry, CRM management, calendar scheduling, and automated appointment booking.).This role offers the opportunity to work on cutting-edge project and collaborate with a team of talented researchers and engineers in a stimulating and dynamic environment.Minimum qualifications(must have hands-on experience with all of the below):LLMs (generation, alignment, extraction)Supervised fine-tuning (SFT) on ≥7B models with PEFT (LoRA/QLoRA) and full fine-tunes.Preference optimization (DPO/ORPO/PPO/RLAIF), rejection sampling, reward-model training.Constrained/structured decoding (regex/CFG/JSON-Schema), logit biasing, n-gram blocking, spec decoding.Distillation/compression (teacher-student, pruning, quant-aware training).RAG modeling side: dual-encoders, cross-encoders/rerankers (e.g., ColBERT/SPLADE), hard-negative mining.Speech (ASR/diarization/VAD; optional TTS):- Fine-tuning Conformer/Transducer/CTC or Whisper/wav2vec2/HuBERT on domain audio, incl. streaming/chunking.- Robust segmentation (VAD), diarization (x-vectors/ECAPA-TDNN), punctuation & inverse text normalization.- Data augmentation (SpecAugment, speed/tempo, noise/reverb), forced/CTC alignment and lexicon handling.Data & evaluation:- Large-scale corpus building: language ID, dedup/near-dedup (LSH/MinHash), toxicity/PII filters, perplexity/quality filters.- Golden sets & adversarial suites; metrics for WER/CER, entity F1, extraction validity, factuality/hallucination, helpful-harmlessness.- Reproducible experiments: seeds, checkpoints, ablations, learning-curve analysis, compute budgeting; crisp experiment reports.Systems for training (efficiency)- Distributed/memory-efficient training with FSDP/DeepSpeed ZeRO, gradient checkpointing, mixed precision, packing/bucketing, sequence-length curricula.- Dataset pipelines: HF Datasets, WebDataset, streaming Parquet/TFRecords; tokenizer optimization and dataset QA.Core foundations- Solid math (linear algebra, probability, optimization) and ability to reason about loss design and bias/variance.- Expert PyTorch (or JAX), custom modules/losses, profiling (cProfile/torch.profiler), multi-GPU runs.- Habit of rigorous evals with automated harnesses and regression gates.Nice to have- Preference optimization at scale (PPO/DPO), safety classifiers.- Quantization (GPTQ/AWQ/INT8) with minimal quality loss; ONNX/TensorRT; Triton/CUDA kernels.- Multilingual modeling, phonetic/lexicon work for low-resource accents.- Active learning & data programming (cleanlab/Snorkel). #J-18808-Ljbffr