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Entry Level Machine Learning Engineer Jobs in Natick, MA

OneScreen

Founding Machine Learning Engineer

Boston, MA · On-site

About the role You'll be the founding ML engineer who owns our matching algorithms from exploration ... Real ranking and matching modeling fluency - learning-to-rank, retrieval and re-rank patterns, not ...

SynergisticIT

AI and Machine Learning Engineer

Cambridge, MA · On-site

$125K - $150K/yr

Machine Learning And Artificial Intelligence Developer Synergistic IT is a full-service staffing and placement firm servicing clients in America for the past 12+ years. We are dedicated towards ...

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Entry Level Machine Learning Engineer information

See Natick, MA salary details

$31K

$71.6K

$121.8K

How much do entry level machine learning engineer jobs pay per year?

As of Jun 17, 2026, the average yearly pay for entry level machine learning engineer in Natick, MA is $71,608.00, according to ZipRecruiter salary data. Most workers in this role earn between $53,200.00 and $81,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Entry Level Machine Learning Engineer position, and why are they important?

To thrive as an Entry Level Machine Learning Engineer, you need a solid understanding of machine learning algorithms, programming languages like Python, and a degree in computer science, engineering, or a related field. Familiarity with tools such as TensorFlow, PyTorch, scikit-learn, and version control systems like Git is highly valuable, and completing online courses or certifications can further demonstrate your skills. Strong analytical thinking, attention to detail, and effective communication are important soft skills in this role. These abilities are essential because they enable you to build accurate models, work collaboratively with teams, and communicate insights to stakeholders.

What are some typical projects or tasks an Entry Level Machine Learning Engineer might work on?

As an Entry Level Machine Learning Engineer, you’ll often work on tasks such as data preprocessing, feature engineering, and assisting in training and evaluating models under the guidance of senior engineers or data scientists. You may help develop prototypes, automate data collection pipelines, and collaborate with software engineers to integrate machine learning solutions into products. Working in this role typically involves frequent collaboration in a team environment, participating in code reviews, and learning best practices for scalable model deployment. These foundational experiences are designed to build your technical expertise and set the stage for future growth within the field.

What is an Entry Level Machine Learning Engineer job?

An Entry Level Machine Learning Engineer is responsible for developing, testing, and deploying machine learning models under the guidance of senior engineers. They work with datasets, implement algorithms, and optimize model performance. Their role often involves data preprocessing, feature engineering, and collaborating with data scientists and software engineers. Strong programming skills in Python, knowledge of ML frameworks like TensorFlow or PyTorch, and an understanding of statistics and algorithms are essential. This position serves as a foundation for building expertise in artificial intelligence and data-driven decision-making.

What job categories do people searching Entry Level Machine Learning Engineer jobs in Natick, MA look for? The top searched job categories for Entry Level Machine Learning Engineer jobs in Natick, MA are:
What cities near Natick, MA are hiring for Entry Level Machine Learning Engineer jobs? Cities near Natick, MA with the most Entry Level Machine Learning Engineer job openings:
Entry Level Machine Learning Engineer jobs near you
Founding Machine Learning Engineer

Founding Machine Learning Engineer

OneScreen

Boston, MA • On-site

Full-time

Posted 3 days ago

Job description

About Onescreen
Onescreen is the modern platform for out-of-home advertising - making it easier for brands and agencies to plan, buy, and measure OOH campaigns across thousands of vendors and formats. We move fast, operate lean, and hold ourselves to a high standard on every campaign we run.
About the role
You'll be the founding ML engineer who owns our matching algorithms from exploration through production and the data platform that feeds them. You'll design and ship the models that rank OOH inventory against advertiser personas, markets, and dayparts. You'll own our data warehouse shape and the pipelines that fill it. You'll publish the ranking and matching APIs that downstream products, agents, and automation surfaces consume.
What you'll do
  • Design and ship matching and ranking models for OOH inventory: candidate generation, re-ranking, geospatial-aware scoring.
  • Own the data warehouse layer end to end: staging, marts, feature pipelines, freshness, lineage.
  • Stand up offline and online evaluation infrastructure - measure the gap between them, don't assume it.
  • Publish ranking and matching APIs for product surfaces, with latency and quality SLOs.
  • Instrument model monitoring: drift detection, prediction distribution, feature freshness, retraining triggers.

Qualifications
The hard requirement: you have owned a production ranking, matching, or recommendation system end-to-end. You chose the model, designed the features, made the evaluation methodology calls, and were on the hook when it drifted. We care about that ownership scope more than years on a résumé - title and compensation are scaled to your demonstrated expertise.
Beyond that:
  • Strong production Python (NumPy, Pandas, FastAPI, SQLAlchemy).
  • Strong SQL and modern data warehouse experience (BigQuery preferred).
  • Real ranking and matching modeling fluency - learning-to-rank, retrieval and re-rank patterns, not just classification.
  • Evaluation methodology rigor: holdouts, leakage prevention, online vs. offline gap measurement.
  • Comfort owning the data pipeline as well as the model.
  • Bias toward shipping. Clear writer. Self-directed.
Nice to have
  • Geospatial data experience (H3, PostGIS, GeoPandas)
  • Mobility or location data experience
  • Embedding-based retrieval (pgvector, FAISS, vector databases)
  • Bandits, contextual bandits, or online learning
  • A/B testing infrastructure design
  • Causal inference
  • dbt
  • Ad-tech or OOH domain familiarity

Apply