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Quantum Machine Learning Engineer Jobs in Grafton, 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 ...

Cognex

Senior Machine Learning Engineer

Natick, MA · On-site

$115K - $225K/yr

Cognex is a global leader in the exciting and growing field of machine vision. Our employees ... Job Summary We are seeking an experienced AI/ML engineer with strong research and developmentskills ...

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All JobsQuantum Machine Learning Engineer JobsQuantum Machine Learning Engineer Jobs in Grafton, MA

Quantum Machine Learning Engineer information

See Grafton, MA salary details

$31.9K

$130.4K

$195.9K

How much do quantum machine learning engineer jobs pay per year?

As of Aug 12, 2026, the average yearly pay for quantum machine learning engineer in Grafton, MA is $130,399.00, according to ZipRecruiter salary data. Most workers in this role earn between $102,800.00 and $157,000.00 per year, depending on experience, location, and employer.

What is a quantum machine learning engineer?

A Quantum Machine Learning Engineer is a professional who combines expertise in quantum computing and machine learning to develop algorithms and solutions that leverage quantum hardware for advanced data processing tasks. They work on designing, implementing, and testing quantum algorithms that can solve problems faster or more efficiently than classical computers. Their work often involves collaborating with physicists, data scientists, and software engineers to bridge the gap between quantum theory and practical applications. This role requires strong backgrounds in quantum mechanics, computer science, and statistical learning techniques.

How do quantum machine learning engineers typically collaborate with classical machine learning teams and quantum hardware specialists?

Quantum Machine Learning Engineers often serve as a bridge between classical machine learning experts and quantum hardware specialists. They work closely with data scientists to adapt machine learning algorithms for quantum environments and collaborate with hardware teams to ensure algorithms are optimized for specific quantum processors. Regular cross-functional meetings, code reviews, and joint problem-solving sessions are common, fostering a highly collaborative work environment. This collaboration is essential for successfully integrating quantum solutions into existing workflows and advancing the organization's quantum computing initiatives.

What are the key skills and qualifications needed to thrive as a quantum machine learning engineer?

To thrive as a Quantum Machine Learning Engineer, you need a strong background in quantum computing, machine learning, linear algebra, and programming (often Python or C++), typically supported by an advanced degree in physics, computer science, or a related field. Familiarity with platforms like Qiskit, Cirq, or TensorFlow Quantum, and knowledge of quantum algorithms and cloud-based quantum computing services are essential. Creative problem-solving, analytical thinking, and strong collaboration skills help distinguish top performers in this interdisciplinary field. Mastery of these skills enables innovation in developing and deploying quantum machine learning solutions to solve complex, cutting-edge problems.
What job categories do people searching Quantum Machine Learning Engineer jobs in Grafton, MA look for? The top searched job categories for Quantum Machine Learning Engineer jobs in Grafton, MA are:
What cities near Grafton, MA are hiring for Quantum Machine Learning Engineer jobs? Cities near Grafton, MA with the most Quantum Machine Learning Engineer job openings:
Quantum Machine Learning Engineer jobs near you
Infographic showing various Quantum Machine Learning Engineer job openings in Grafton, MA as of August 2026, with employment types broken down into 100% Full Time. Highlights an 67% In-person, and 33% Remote job distribution, with an average salary of $130,399 per year, or $62.7 per hour.

Founding Machine Learning Engineer

OneScreen

Boston, MA • On-site

Full-time

Re-posted 28 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