Time since we first saw it
Day 3
Early. Similar jobs usually come down between day 8 and day 76.
From 5,460 similar postings that came down. Coming down is not always a hire.
Senior ML Engineer
Ramat Gan, Tel Aviv District, Israel
Actively hiring, first seen by us 3 days ago- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingBuild data ingestion and normalization from multiple data sources
Handle feature engineering and feature selection
Run model training and evaluation at large scale, fully automated
Own a feature through all its phases
From the employer’s posting
You will create innovative solutions for data ingestion and normalization from multiple data sources
feature engineering and feature selection
actual model training and evaluation. All in large scale and completely automated.
Ability to design and own a feature through all its phases.
What you’ll bring
All qualificationsCore experience
- 10+ years of experience with Python, Java, or Scala
- 5+ years as a Machine Learning Engineer
- Hands-on experience designing, training, and deploying ML models
- Experience with distributed compute frameworks such as Spark, Dask, or Ray
- Strong understanding of distributed systems and object-oriented design
Qualification wording
10+ years of experience with Python/Java/Scala.
5+ years experience as a Machine Learning Engineer.
Hands-on experience designing, training, and deploying machine-learning models
Distributed Compute frameworks such as Spark, Dask, Ray etc
Strong understanding of distributed systems, object-oriented programming and design patteri
Posting record
Report an errorActively hiringFirst seen by us 3 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 10, 20263 days ago
- Recorded sightings
- 4
- Last seen by us
- Nov 13, 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
Pecan AI is hiring
3 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee 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.
- Java
- Python
- Scala
- Databricks
- Docker
- SageMaker
- Snowflake
- Spark
- Airflow
- Dask
- LightGBM
- NumPy
- pandas
- scikit-learn
- XGBoost
Where the posting names them
- 5+ years experience as a Machine Learning Engineer. - 10+ years of experience with Python/Java/Scala. - Strong understanding of distributed systems, object-oriented programming and design patteri
Compare Pecan vs. Amazon SageMaker and Databricks Pecan vs Google Vertex AI and Snowflake Pecan vs Akkio vs Peak AI Pecan vs Alteryx vs Qlik Predict Pecan vs DataRobot vs Dataiku Product
MLOps - Hands-on experience with open source ML libraries like: catboost, lightgbm, xgboost, scikit-learn, NumPy, Pandas, Microservices architecture, cloud technologies, Docker/K8s. - Ability to design and own a feature through all its phases.
- Strong understanding of distributed systems, object-oriented programming and design patteri - Distributed Compute frameworks such as Spark, Dask, Ray etc - Hands-on experience designing, training, and deploying machine-learning models
- Building data pipelines using Apache Airflow - Hands on experience with Spark, SparkSQL, Spark streaming and other Spark related projects Apply for this job
- BSc./MSc. In CS or similar – an advantage - Building data pipelines using Apache Airflow - Hands on experience with Spark, SparkSQL, Spark streaming and other Spark related projects
Your next step
Complete your application on www.pecan.ai. The employer’s form will show what is required.
Already applied? Track this application
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 10% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 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
Pecan is an automated AI-based predictive analytics platform. It simplifies and accelerates the process of building and deploying machine learning models in various business use-cases, such as life-time value, Churn, demand forecast and more. Pecan connects to the raw data and completely automates the data preparation, engineering and prepossessing phases, as well as the model training and evaluation lifecycle. It was acknowledged as one of Israel’s 50 most promising startups two years in a row
Company Highlights:
Series C company with over $117M raised to date. Tier-1 investors: Google Ventures (GV), Insight Partners, GGV, Dell Ventures, Mindset and S Capital.
50 employees
Customers across CPG, retail, healthcare, mobile apps, fintech, insurance, and consumer services. Marquee customers include Johnson & Johnson, and SciPlay..
Responsibilities:
Work on pecans’s core data and machine learning infrastructure, which is at the heart of Pecan’s offering. You will create innovative solutions for data ingestion and normalization from multiple data sources, feature engineering and feature selection, as well as actual model training and evaluation. All in large scale and completely automated.
Who You Are:
A problem solver at heart, you have a passion for excellence, you love to learn but know when it’s time to deliver and make ends meet. You aren’t threatened by a complex, dynamic and demanding environment. “There is no I in team”, is a motto you believe in deeply and you are always looking out for your peers. You know how to take ownership and drive projects to completion.
What We’re Looking For:
- 5+ years experience as a Machine Learning Engineer.
- 10+ years of experience with Python/Java/Scala.
- Strong understanding of distributed systems, object-oriented programming and design patteri
- Distributed Compute frameworks such as Spark, Dask, Ray etc
- Hands-on experience designing, training, and deploying machine-learning models
MLOps
- Hands-on experience with open source ML libraries like: catboost, lightgbm, xgboost, scikit-learn, NumPy, Pandas, Microservices architecture, cloud technologies, Docker/K8s.
- Ability to design and own a feature through all its phases.
Bonus:
- BSc./MSc. In CS or similar – an advantage
- Building data pipelines using Apache Airflow
- Hands on experience with Spark, SparkSQL, Spark streaming and other Spark related projects
Apply for this job
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With a deep understanding of security concerns and regulations, Pecan is committed to keeping your information secure, private, and encrypted at all times. We employ active and passive security measures, ranging from rigid internal compartmentalization to advanced endpoint and network-protection mechanisms. Learn more
Source details
Source notes
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
Ramat Gan, Tel Aviv District, Israel
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.