1

Founding Machine Learning Engineer Jobs in Massachusetts

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 ...

As a machine learning engineer, you will develop natural language processing systems that help our customers understand their contracts. You will work with a wide range of structured and unstructured ...

Machine Learning Engineer

Somerville, MA · On-site

$170K - $200K/yr

We're looking for a Senior Machine Learning Engineer to help advance the state of voice understanding at Modulate. In this role, you'll design, train, evaluate, and deploy cutting-edge machine ...

Machine Learning Engineer

Somerville, MA · On-site +1

$170K - $200K/yr

We're looking for a Senior Machine Learning Engineer to help advance the state of voice understanding at Modulate. In this role, you'll design, train, evaluate, and deploy cutting-edge machine ...

Machine Learning Engineer

Somerville, MA · On-site +1

$170K - $200K/yr

We're looking for a Senior Machine Learning Engineer to help advance the state of voice understanding at Modulate. In this role, you'll design, train, evaluate, and deploy cutting-edge machine ...

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... Design and implement complex data engineering processes to support innovative data science modeling

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... Design and implement complex data engineering processes to support innovative data science modeling

Machine Learning Engineer - Computer Vision & Robotics Tycho.AI is redefining the future of autonomous intelligence. Spun out of MIT and backed by DoD contracts, we are building breakthrough AI and ...

Sr. Lead Machine Learning Engineer

Cambridge, MA · On-site +1

$112K - $147K/yr

Sr. Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE) , you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale.

Senior Machine Learning Engineer

Boston, MA · On-site +1

$133K - $175K/yr

Position Summary The Machine Learning Engineer will be responsible for the end-to-end development and deployment of Large language and machine learning models, with a primary focus on data ...

Senior Machine Learning Engineer

Boston, MA · On-site +1

$133K - $175K/yr

Position Summary The Machine Learning Engineer will be responsible for the end-to-end development and deployment of Large language and machine learning models, with a primary focus on data ...

Senior Machine Learning Engineer Job Duties: Design and implement image processing solutions to enhance operational workflows and fraud detection. Duties include: * Design, develop, and maintain AI ...

The Core for Computational Biomedicine (CCB) in the Department of Biomedical Informatics (DBMI) at Harvard Medical School (HMS) is looking for a Machine Learning Engineer with advanced expertise to ...

The Core for Computational Biomedicine (CCB) in the Department of Biomedical Informatics (DBMI) at Harvard Medical School (HMS) is looking for a Machine Learning Engineer with advanced expertise to ...

next page

Showing results 1-20

Founding Machine Learning Engineer information

What is a Founding Machine Learning Engineer?

A Founding Machine Learning Engineer is one of the first technical team members at a startup who specializes in designing, building, and deploying machine learning systems. This role involves working closely with the founders to set the technical direction, build core AI products, and establish best practices for data and model development. In addition to hands-on coding and experimentation, a Founding Machine Learning Engineer often influences product decisions and helps shape the company's engineering culture. The role typically requires a blend of deep technical expertise, startup agility, and a willingness to tackle both high-level strategy and low-level engineering tasks.

What engineer makes $500,000 a year?

A founding machine learning engineer at top tech companies or successful startups can earn $500,000 or more annually, often including base salary, bonuses, and equity. Such roles typically require advanced skills in deep learning, data modeling, and experience with large-scale systems, along with a strong track record of innovation and leadership.

What are some unique challenges and expectations for a Founding Machine Learning Engineer in an early-stage startup?

As a Founding Machine Learning Engineer, you'll face the unique challenge of building the company's machine learning infrastructure from the ground up, often with limited resources and rapidly evolving requirements. You'll be expected to wear many hats, from designing and deploying models to setting up data pipelines and collaborating closely with product and engineering teams. Your role will also involve making critical decisions about technology stacks and best practices that will shape the company's technical direction. Additionally, you'll have significant influence on the company's culture and have ample opportunities for growth as the team expands.

What is a founding ML engineer?

A founding machine learning engineer is a key technical team member involved in building and developing the company's initial machine learning systems and infrastructure. They typically have strong skills in programming, data modeling, and deploying ML models, often working closely with product teams during the startup or early-stage company formation. This role requires a combination of technical expertise and entrepreneurial mindset to shape the company's AI capabilities from the ground up.

Is a machine learning engineer still in demand?

Yes, machine learning engineers are in high demand due to the increasing adoption of AI and data-driven solutions across industries. They are sought after for their skills in algorithms, programming, and tools like Python and TensorFlow, with job growth expected to continue as AI applications expand.

Which 5 jobs will survive AI?

Founding Machine Learning Engineers are likely to continue playing a crucial role as AI advances, focusing on developing and deploying complex models that require specialized skills in programming, data science, and system architecture. Jobs that involve high levels of creativity, strategic decision-making, and human interaction—such as healthcare professionals, educators, skilled trades, and roles in management—are also expected to persist despite AI automation. These positions typically require emotional intelligence, critical thinking, and adaptability that AI cannot easily replicate.

What are the key skills and qualifications needed to thrive as a Founding Machine Learning Engineer, and why are they important?

To thrive as a Founding Machine Learning Engineer, you need deep expertise in machine learning algorithms, software engineering, and data science, often supported by a degree in computer science or a related field. Familiarity with tools such as Python, TensorFlow or PyTorch, cloud platforms, and experience deploying ML models in production are typically required. Strong problem-solving abilities, entrepreneurial mindset, and excellent communication skills set standout candidates apart. These skills and qualities are vital for driving innovation, building scalable solutions from scratch, and collaborating within a fast-paced startup environment.
What cities in Massachusetts are hiring for Founding Machine Learning Engineer jobs? Cities in Massachusetts with the most Founding Machine Learning Engineer job openings:
Founding Machine Learning Engineer

Founding Machine Learning Engineer

OneScreen

Boston, MA • On-site

Full-time

Posted 25 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