AI - Machine Learning Engineer

Title: Machine Learning Engineer

Location : Remote or Location: San Jose, CA (hybrid onsite 3x per week – Tues, Wed, Thurs)

Target Start Date : ASAP

Type: (C, CTH, D) Direct Hire

Pay Rate / Salary (Ranges OK) : 240,000 -250,000
See Remarks

Title: Machine Learning Engineer

Job Description

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.

Welcome to ConsultNet, a premier national provider of technology talent and solutions. Our expertise spans across project services, contract-to-hire, direct search, and managed services onshore, nearshore, and hybrid. For over 25 years, we have connected thousands of consultants with meaningful roles through a personal, communication-driven approach, partnering with a diverse client base to build high-performing teams and create lasting impact. Our comprehensive service offerings cover a wide range of technology and engineering positions across key markets nationwide. Learn more at .

We champion equality and inclusivity, proudly supporting an Equal Opportunity Employer policy. We welcome applicants regardless of Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Age, Genetic Information, Disability, Protected Veteran Status, or any other status protected by law.

evel: Engineer, Senior, Lead, Staff, or Principal (level set during the interview process based on candidate calibration)

Location: San Jose, CA (hybrid onsite 3x per week – Tues, Wed, Thurs)

***Note: Relocation is required within 6-months of start date, we offer relocation assistance.

Office Location: 11 W St John St, Floor 9, San Jose, CA 95113