Applied Research Engineer

Awake Solutions; United States; Hybrid; USD 120,000 - 190,000; Full time

Benefits offered by this job

Equity

Cloud credits

GPU credits

Career development

Job summary

Awake Solutions seeks an experienced machine learning engineer to transform research ideas into dependable production systems. You will reproduce models, optimize inference, and deploy scalable services, balancing latency, throughput, and cost for real-world applications.

You will collaborate with researchers to improve models, monitor deployments, and automate pipelines. Global remote and hybrid work options are available, with equity, and cloud/GPU credits to support prototyping and publishing

Qualifications

• 3+ years of ML engineering or applied research experience.
• Strong Python skills.
• Experience deploying ML models on cloud services and using container orchestration such as Kubernetes.
• Familiarity with model monitoring, A/B testing, and staged rollout methods.

Responsibilities

• Convert research concepts into scalable production services and demonstrations.
• Work with researchers to reproduce models and improve inference performance.
• Optimize latency, throughput, and operating cost for deployment needs.
• Instrument experiments and maintain reproducible model deployment pipelines.
• Coordinate with product colleagues to prioritize research based on user impact.

Skills

Python

ML engineering

Model optimization

Kubernetes

Cloud deployment

A/B testing

Tools

Docker

Job description

Role overview

Turn research ideas and state-of-the-art models into dependable systems that can deliver value in real products. This applied role bridges research and engineering: you’ll reproduce and optimize models, build scalable services and demos, and adapt deployments to practical constraints such as latency, throughput, and cost.

Responsibilities

• Convert research concepts into scalable production services and demonstrations.
• Work with researchers to reproduce models and improve their inference performance.
• Optimize latency, throughput, and operating cost for deployment needs.
• Instrument experiments and maintain reproducible model deployment pipelines.
• Coordinate with product colleagues to prioritize research based on user impact.

Requirements

• At least three years of machine learning engineering or applied research experience, with strong Python skills.
• Hands-on knowledge of model optimization, quantization, distillation, and inference engineering.
• Experience deploying machine learning models on cloud services and using container orchestration such as Kubernetes.
• Familiarity with model monitoring, A/B testing, and staged rollout methods.

Benefits and work setup

Full-time role with global remote or hybrid options. The package includes salary and equity, cloud and GPU credits for prototyping, learning and publication support, and career development across research and product tracks.