Enigma · 7 hours ago
Machine Learning Engineer | Python | Pytorch | Distributed Training | Optimisation | GPU | Hybrid, San Jose, CA
San Jose, CA
Full-time
Hybrid
Mid, Senior Level
3+ years expEnigma is a company focused on machine learning solutions, and they are seeking a Machine Learning Engineer to optimize and productize models from research into efficient services. The role involves scaling training across nodes/GPUs, implementing model-efficiency techniques, and maintaining model-serving systems.
Staffing & Recruiting
Hiring Manager
Tom Goldberg
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
Qualification
Required
Bachelors in computer science, Electrical/Computer Engineering, or a related field required; Master's preferred (or equivalent industry experience)
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 a plus
Optimization: experience profiling and optimizing code execution and model inference: (PTQ/QAT/AWQ/GPTQ), pruning, distillation, KV-cache optimization, Flash Attention
Scalable serving: autoscaling, load balancing, streaming, batching, caching; collaboration with platform engineers
Data & storage: 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
Company
Enigma
Here at Enigma, we specialize in Generative AI recruitment, specifically focused on Machine Learning and Software Engineering disciplines.
2-10 employees
H1B Sponsorship
Enigma has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
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Trends of Total Sponsorships
2025 (1)
2024 (3)
2023 (3)
2022 (3)
2021 (5)
2020 (3)
Funding
Current Stage
Early StageCompany data provided by crunchbase