Machine Learning Engineer | Python | Pytorch | Distributed Training | Optimisation | GPU | Hybrid, San Jose, CA

Machine Learning Engineer | Python | Pytorch | Distributed Training | Optimisation | GPU | Hybrid, San Jose, CA

Tags

52

FAIR MATCH

Experience Level

72

Skill

Industry Exp.

58

Staffing & Recruiting

Hiring Manager

Tom Goldberg

Insider Connection @Enigma

2 email credits available today

Responsibilities

Productize and optimize models from Research into reliable, performant, and cost-efficient services with clear SLOs (latency, availability, cost)

Scale training across nodes/GPUs (DDP/FSDP/ZeRO, pipeline/tensor parallelism) and own throughput/time-to-train using profiling and optimization

Implement model-efficiency techniques (quantization, distillation, pruning, KV-cache, Flash Attention) for training and inference without materially degrading quality

Build and maintain model-serving systems (vLLM/Triton/TGI/ONNX/TensorRT/AITemplate) with batching, streaming, caching, and memory management

Integrate with vector/feature stores and data pipelines (FAISS/Milvus/Pinecone/pgvector; Parquet/Delta) as needed for production

Define and track performance and cost KPIs; run continuous improvement loops and capacity planning

Partner with ML Ops on CI/CD, telemetry/observability, model registries; partner with Scientists on reproducible handoffs and evaluations

Preferred

Master's

or equivalent industry experience

PyTorch (primary), TensorFlow

distributed training experience a plus

Benefits

Hybrid work arrangement

Here at Enigma, we specialize in Generative AI recruitment, specifically focused on Machine Learning and Software Engineering disciplines.

London, England, GB

https://www.enigma-rec.ai

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Qualifications

Bachelors in computer science, Electrical/Computer Engineering, or a related field required

Strong systems/ML engineering with exposure to distributed training and inference optimization

3–5 years in ML/AI engineering roles owning training and/or serving in production at scale

Demonstrated success delivering high-throughput, low-latency ML services with reliability and cost improvements

Experience collaborating across Research, Platform/Infra, Data, and Product functions

Familiarity with deep learning frameworks: PyTorch (primary), TensorFlow

Exposure to large model training techniques (DDP, FSDP, ZeRO, pipeline/tensor parallelism)

distributed training experience

experience profiling and optimizing code execution and model inference: (PTQ/QAT/AWQ/GPTQ), pruning, distillation, KV-cache optimization, Flash Attention

SQL/NoSQL, vector stores (FAISS/Milvus/Pinecone/pgvector), Parquet/Delta, object stores

Write performant, maintainable code

Understanding of the full ML lifecycle: data collection, model training, deployment, inference, optimization, and evaluation

Distributed training experience

Experience profiling and optimizing code execution and model inference: (PTQ/QAT/AWQ/GPTQ), pruning, distillation, KV-cache optimization, Flash Attention

Master's

Or equivalent industry experience

PyTorch (primary), TensorFlow

Distributed training experience a plus

3–5 years in ML/AI engineering roles owning training and/or serving in production at scale.

Required skills

Represents the skills you have, click on the tags, PyTorch, TensorFlow, Distributed Training, Model Inference Optimization, Quantization, Pruning, Knowledge Distillation, KV-Cache Optimization, Flash Attention, SQL, NoSQL, FAISS

Preferred skills

Master's, or equivalent industry experience, PyTorch (primary), TensorFlow, distributed training experience a plus

Experience

3-5 years