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Ml Inference Jobs in Massachusetts (NOW HIRING)

Catalyst Labs

ML Engineer

Boston, MA · On-site

... inference, and monitoring in production environments. • Participate in continuous improvement of the ML infrastructure and processes for scalability and performance. Qualifications : Required : • ...

CookUnity

Director, Data Science/ML

Boston, MA · On-site

Develop causal inference methodologies to understand true incrementality of product changes ... Proven track record building and deploying ML models in production , particularly in ...

SwiftCruit

Principal ML Ops Engineer

Cambridge, MA · On-site

$120 - $160/hr

  • Medical

  • Dental

  • Vision

  • Retirement

Collaborate with researchers to productionize models and accelerate training/inference pipelines. * Establish ML Ops best practices, internal standards, and cross‑team tooling. * Mentor engineers ...

Pragmatike

Principal AI/ML Engineer

Cambridge, MA · On-site

... inference, and evaluation pipelines for structured and unstructured data • Own model performance ... ML codebases primarily in Python • Monitor, debug, and improve live production models • ...

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Ml Inference information

What is ML inference?

ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.

What is the difference between Ml Inference vs Data Scientist?

AspectML InferenceData Scientist
Required CredentialsKnowledge of machine learning models, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentDeploying models in production, real-time data processingData analysis, model development, research
Industry UsageAI product deployment, software companiesResearch institutions, tech firms, consulting

ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.

What are some common challenges faced by ML inference engineers when deploying models to production?

ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.

What are the key skills and qualifications needed to thrive in ML inference?

To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.

Is ML inference a high paying job?

ML inference roles are generally well-paying, especially for those with skills in machine learning frameworks, programming, and cloud platforms. Salaries vary based on experience, location, and industry, but they tend to be higher than average for tech-related positions.
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Ml Inference jobs near you

Distinguished Systems Developer - SQL Engine

InterSystems

Boston, MA • On-site

$49.25 - $67.25/hr

Full-time

Re-posted 10 days ago

Job description

Job Summary:
InterSystems is a creative data technology provider redefining the future of analytics and intelligent data platforms. The role involves designing core foundations of a data platform, focusing on optimizing query processing for traditional and AI-driven workloads.
Responsibilities:
• Query parsing, validation, and transformation
• Logical/physical optimization over multi-model data (row, columnar, stream, document, vector)
• Plan generation and adaptive execution strategies
• Code generation and vectorized execution
• Compiler-accelerated storage access patterns for AI workflows
• Collaborative integration with our multi-model storage engine
• Architect and implement query optimization and execution components that scale across petabytes
• Design tooling and frameworks that make AI and analytics convergence seamless
• Collaborate with platform and storage teams to leverage multi-model indexing and caching strategies
• Contribute to building programmable, intelligent data pipelines that drive downstream ML model performance
• Mentor engineers, guide technical debates, and influence long-term system direction
• Champion a culture of quality, rigor, and innovation
Qualifications:
Required:
• 10+ years of experience in systems-level or database internals engineering
• Proven expertise in SQL query engines, optimizers, execution runtimes, or data compiler toolchains
• Experience with multi-model data systems (SQL + JSON, vector search, graph traversal)
• Expert in at least one systems programming languages, or LLVM-based codegen environments
• Strong grasp of relational algebra, compiler construction, and data-intensive workloads
• Passion for designing software that balances performance, correctness, and usability
Preferred:
• Familiarity with AI infrastructure or ML inference pipelines is a strong plus
• PHD degree or published research experience is highly advantageous
Company:
InterSystems is a vendor of software and technology for high-performance database management, integration, and health information systems. Founded in 1978, the company is headquartered in Cambridge, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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