Time since we first saw it
Day 21
Similar jobs usually come down between day 6 and day 71.
At Bjak, jobs usually come down by about day 45.
From 4,633 similar postings that came down, and 9 of Bjak’s. Coming down is not always a hire.
Machine Learning Engineer, Applied AI
- Pay
- Pay not listed
- 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 Engineering › ActAI — team source
Department: ActAI Engineering › ActAI
From the employer’s posting
What you’ll work on
Full postingBuild and improve classification, ranking, retrieval and personalization models
Improve search relevance and context retrieval with embeddings, hybrid search and reranking
Develop evaluation pipelines, labeled datasets and metrics to measure model quality
Build and maintain ML training, inference and deployment pipelines
Debug production issues, monitor model performance and ship improvements
From the employer’s posting
Build and improve ML models for classification, ranking, retrieval and personalization.
Improve search relevance and context retrieval using embeddings, hybrid search and reranking.
Develop evaluation pipelines, labeled datasets and metrics to measure model quality.
Build and maintain ML training, inference and deployment pipelines.
Debug production issues, monitor model performance and ship improvements based on real-world feedback.
What you’ll bring
All qualificationsCore experience
- Hands-on experience building and deploying ML models in production
- Strong foundations in machine learning and modern neural architectures
- Experience in classification, ranking, retrieval, recommendation or personalization
- Understanding of model evaluation, experimentation and performance metrics
- Comfortable writing production-quality code in Python, PyTorch or JAX
Qualification wording
Hands-on experience building and deploying ML models in production.
Strong foundations in machine learning and modern neural architectures.
Experience in classification, ranking, retrieval, recommendation systems or personalization.
Understanding of model evaluation, experimentation and performance metrics.
Comfortable writing production-quality code and working with real-world datasets.
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 16, 202621 days ago
- Recorded sightings
- 67
- Last seen by us
- Nov 5, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
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.
- Python
- PyTorch
Where the posting names them
Tech Stack - Python - PyTorch / JAX
- Python - PyTorch / JAX - Embeddings, vector search and ranking models
Your next step
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
Applying through Ashby
- No account needed. “Autofill from resume” at the top of the form fills the key fields from your file.
Already applied? Track this application
Has Bjak posted this job before?
Yes. We’ve followed 11 other Machine Learning Engineer, Applied AI jobs there since October 2026. One came down after 14 days. 10 are still up.
Large employers often post one title for several teams, so these can be separate openings.
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
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
There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.
Our first application is an agent-first mobile communication app. We believe building applications that users use daily for communication, coordination and collaboration needs will help integrate AI into their lives. The net effect is to eliminate misinformation, achieve low coordination costs, enable high quality tasks and collaboration at scale with minimal or little prompting.
Role
Build and improve ML systems that power our AI-native applications.
Work on classification, retrieval, ranking, personalization and evaluation, taking ownership from model development to production deployment.
This role is for engineers who enjoy solving practical ML problems, improving model quality and shipping reliable systems used by real users.
Focus
Build and improve ML models for classification, ranking, retrieval and personalization.
Develop efficient classifiers and optimize model accuracy, precision, latency and cost.
Improve search relevance and context retrieval using embeddings, hybrid search and reranking.
Build personalization models that learn from user behavior while respecting privacy.
Develop evaluation pipelines, labeled datasets and metrics to measure model quality.
Fine-tune and adapt existing models when supported by sufficient data.
Build and maintain ML training, inference and deployment pipelines.
Debug production issues, monitor model performance and ship improvements based on real-world feedback.
Tech Stack
Python
PyTorch / JAX
Embeddings, vector search and ranking models
Production ML systems and inference pipelines
Ideal Experience
Strong foundations in machine learning and modern neural architectures.
Hands-on experience building and deploying ML models in production.
Experience in classification, ranking, retrieval, recommendation systems or personalization.
Understanding of model evaluation, experimentation and performance metrics.
Comfortable writing production-quality code and working with real-world datasets.
Able to balance model quality, latency, cost and reliability.
Strong ownership, problem-solving skills
Outcomes
ML models in production meet expected accuracy, relevance, latency and reliability targets.
Classification and ranking improvements deliver measurable gains in model quality.
Evaluation pipelines identify regressions and support reliable model releases.
Training, inference and data pipelines are robust, reproducible and maintainable.
Model improvements are driven by real-world feedback and measurable results.
Collaborates effectively with engineering and product teams to deliver reliable ML-powered features.
How We Work
The best products in the world are built by small, world-class teams. We are a high-talent-density, hands-on team. We make decisions collectively and move quickly, balancing high-quality work with continuous learning.
Joining our team requires the ability to bring structure, exercise judgment and execute independently. Our goal is to put a truly magical product into the hands of our users.
Interview Process
If there appears to be a fit, we'll reach out to schedule 3, but no more than 4 interviews.
Applications are evaluated by our technical team members. Interviews will be conducted virtually and/or onsite.
We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us.
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
Portugal
- Develop efficient classifiers and optimize model accuracy, precision, latency and cost. - Improve search relevance and context retrieval using embeddings, hybrid search and reranking. - Build personalization models that learn from user behavior while respecting privacy.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.