Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
AI Phone - On-device AI / ML Systems Engineer
United States
Actively hiring, first seen by us today- Pay
- Pay not listedSimilar roles state $166K–$240K
- Work setting
Remote — work setting source
Location type: Remote
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
ActAI - Realware — team source
Department: ActAI - Realware
From the employer’s posting
What you’ll work on
Full postingEvaluate suitable open-weight and commercially available models for on-device execution.
Optimize inference latency, memory consumption and power usage.
Evaluate quantization, compression and model distillation techniques.
From the employer’s posting
Responsibilities Evaluate suitable open-weight and commercially available models for on-device execution. Benchmark model performance on target mobile hardware.
Integrate models into Android applications and system services. Optimize inference latency, memory consumption and power usage. Evaluate quantization, compression and model distillation techniques.
Optimize inference latency, memory consumption and power usage. Evaluate quantization, compression and model distillation techniques. Work with CPU, GPU and NPU acceleration.
What you’ll bring
All qualificationsCore experience
- Experience deploying and optimizing models.
- Experience with on-device LLMs.
- Strong Python and C++ skills.
- Understanding of neural network inference.
- Experience with model quantization and performance optimization.
- Understanding of hardware constraints and inference benchmarks.
Qualification wording
Experience deploying and optimizing models.
Experience with on-device LLMs.
Strong Python and C++ skills.
Understanding of neural network inference.
Experience with model quantization and performance optimization.
Understanding of hardware constraints and inference benchmarks.
Posting record
Report an errorActively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 4, 2026today
- Recorded sightings
- 1
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 0
Early. Similar jobs usually come down between day 6 and day 69.
From 4,296 similar postings that came down. Coming down is not always a hire.
Bjak is hiring
85 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- ML Engineer hiring is steady
- Its jobs tend to stay up a long time
- 6% of open jobs were reposted
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- C++
- Python
- TensorFlow
Where the posting names them
- Experience deploying and optimizing models. - Strong Python and C++ skills. - Understanding of neural network inference.
- Qualcomm QNN, MediaTek NeuroPilot or similar acceleration frameworks. - ONNX Runtime, LiteRT, TensorFlow Lite or ExecuTorch. - Android NDK.
Your next step
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
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- No account needed. “Autofill from resume” at the top of the form fills the key fields from your file.
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Has Bjak posted this job before?
Yes. We’ve followed 5 other AI Phone - On-device AI / ML Systems Engineer jobs there since November 2026. All 5 are still up.
Large employers often post one title for several teams, so these can be separate openings.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
About
We are reimagining the phone. AI phones need to be proactive, intuitive, intelligent and low cost. This is a mission critical for rest of the world to adopt intelligence.
There are over 5 billion people using everyday applications such as email, messaging, notes, tasks, and calendars, yet most are not AI-native. Our mission is to build proactive applications and devices that bring intelligence into daily conversations, coordination and workflows, with minimal prompting.
Responsibilities
Evaluate suitable open-weight and commercially available models for on-device execution.
Benchmark model performance on target mobile hardware.
Integrate models into Android applications and system services.
Optimize inference latency, memory consumption and power usage.
Evaluate quantization, compression and model distillation techniques.
Work with CPU, GPU and NPU acceleration.
Evaluate local model capabilities for different workflows.
Build hybrid local/cloud model routing mechanisms.
Identify which tasks can be handled locally and which require cloud inference.
Work with AI agent engineers to integrate local models into the agent runtime.
Requirements
Strong ML systems or inference engineering experience.
Experience deploying and optimizing models.
Strong Python and C++ skills.
Understanding of neural network inference.
Experience with model quantization and performance optimization.
Understanding of hardware constraints and inference benchmarks.
Nice to have
Experience with on-device LLMs.
Qualcomm QNN, MediaTek NeuroPilot or similar acceleration frameworks.
ONNX Runtime, LiteRT, TensorFlow Lite or ExecuTorch.
Android NDK.
Mobile or edge AI deployment.
Multimodal model optimization.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
United States
- Evaluate local model capabilities for different workflows. - Build hybrid local/cloud model routing mechanisms. - Identify which tasks can be handled locally and which require cloud inference.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.