Senior Machine Learning Engineer
Tasks
- Apply quantization for inference
- Build canary shadow and AB testing
- Build feedback loops for policy refinement
- Build multi stage inference pipelines
- Build offline evaluation frameworks
- Build online evaluation systems
- Build preference optimization pipelines
- Create synthetic data pipelines
- Design adversarial training pipelines
- Distill frontier teacher models
- Fine-tune language models
- Generate synthetic data with frontier teachers
- Implement GPU based shared inference pools
- Implement speculative decoding
- Integrate models into real time enforcement path
- Mine failures and diagnose root causes
- Optimize KV cache routing
- Optimize accuracy latency and cost
- Own model training lifecycle
- Perform drift detection and calibration monitoring
- Serve models for real time enforcement
- Train anomaly and severity models
- Translate customer requirements into ML problems
Perks/Benefits
- N/A
Skills/Tech-stack
AB Testing | Adversarial Machine Learning | Calibration monitoring | Canary Releases | Closed Loop | Closed Loop ML | Continuous batching | DPO | Data Curation | Deep learning | Distillation | Drift Detection | FP8 | Fine Tuning | GRPO | INT8 | Inference Optimization | Injection Defense | KV cache | LLM-as-a-Judge | Language Models | Large Language Models | LoRA | Machine Learning | Model Evaluation | Preference optimization | Prompt injection | Prompt injection defense | PyTorch | Python | Quantization aware training | RLAIF | RLHF | Red Teaming | SGLang | Shadow testing | Speculative decoding | Supervised Fine Tuning | Synthetic data | Telemetry | TensorRT-LLM | VLLM
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Regions
Countries
States
Cities
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