Machine Learning Engineer

Insight Global; United States; Remote; USD 185,000 - 215,000; Full time

Job summary

A health technology company is seeking an experienced Machine Learning Engineer for a full-time role. The position is remote and requires expertise in ML/AI engineering, particularly with tools like PyTorch. Successful candidates will excel in model training, optimization, and deployment, contributing to cutting-edge health tech solutions while enjoying comprehensive benefits and a competitive salary.

Benefits

Medical, dental, and vision insurance

401k with employer matching

Paid sick leave

Qualifications

• 3–5 years in ML/AI engineering roles.
• Experience with high-throughput, low-latency ML services.
• Familiarity with advanced training techniques.

Responsibilities

• Develop ML models and optimize their performance.
• Collaborate across various teams on ML projects.
• Deploy models at scale with robust serving pipelines.

Skills

ML/AI engineering

Pytorch

TensorFlow

SQL

NoSQL

Model Optimization

Distributed Training

End-to-End ML lifecycle

Education

Bachelor's in computer science or related field

Tools

PyTorch

TensorFlow

SQL databases

NoSQL databases

FAISS

Job description

Salary

Salary: $200,000/yr + depending on experience

Job Description

Insight Global is seeking an experienced, driven Machine Learning Engineer to join an established health technology company located remotely in the PST or CST time zone. This is a full‑time, permanent role with a competitive salary, bonus, and comprehensive benefits.

Required Skills & 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.
• Bachelor’s 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.

In this role you’ll need

Deep Learning Frameworks: Hands‑on experience with PyTorch (main focus) and familiarity with TensorFlow.

Large‑Scale Model Training: Exposure to advanced training techniques like Distributed Data Parallel (DDP), Fully Sharded Data Parallel (FSDP), ZeRO, and model parallelism (pipeline/tensor). Experience with distributed training is a strong plus.

Model Optimization: Skilled in improving model performance through techniques like quantization (PTQ, QAT, AWQ, GPTQ), pruning, knowledge distillation, KV‑cache tuning, and using efficient attention mechanisms like Flash Attention.

Scalable Model Serving: Understanding of how to deploy models at scale, including autoscaling, load balancing, streaming, batching, and caching. Comfortable working alongside platform engineers to build robust serving pipelines.

Data & Storage Systems: Proficient with both SQL and NoSQL databases, vector databases (e.g., FAISS, Milvus, Pinecone, pgvector), and data formats like Parquet and Delta. Familiar with object storage systems.

Code Quality: Writes efficient, clean, and maintainable code with a focus on performance.

End‑to‑End ML Lifecycle: Solid grasp of the full machine learning workflow—from data collection and model training to deployment, inference, optimization, and evaluation.

Benefit packages for this role will start on the 31st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.