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Machine Learning Engineer | Python | Pytorch | Distributed Training | Optimisation | GPU | Hybrid, San Jose, CA Title: Machine Learning Engineer Location: San Jose, CA 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. Educational Qualifications: 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. Industry Experience: 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. Technical Skills: 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. Machine Learning Engineer | Python | Pytorch | Distributed Training | Optimisation | GPU | Hybrid, San Jose, CA
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.