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Ml Inference Jobs in Massachusetts (NOW HIRING)

Third Way Health

AI / ML Engineer

Cambridge, MA · On-site

Develop predictive, real-time analytics systems that combine streaming data, ML inference, and event-driven triggers to surface insights and automate actions at scale. * Implement and maintain end-to ...

Third Way Health

AI / ML Engineer

Cambridge, MA · On-site

... inference, and continuous learning. • Develop predictive, real-time analytics systems that combine streaming data, ML inference, and event-driven triggers to surface insights and automate actions ...

SimpliSafe

Staff Embedded ML Engineer, Edge AI

Boston, MA · On-site

$142K - $187K/yr

As a key contributor, you will lead the on-device inference and performance optimization of ML models powering outdoor monitoring in the home security space. This role is less about inventing new CV ...

SimpliSafe

Staff Embedded ML Engineer, Edge AI

Boston, MA · On-site

$142K - $187K/yr

  • Medical

  • Retirement

As a key contributor, you will lead the on-device inference and performance optimization of ML models powering outdoor monitoring in the home security space. This role is less about inventing new CV ...

SimpliSafe

Staff Embedded ML Engineer, Edge AI

Boston, MA · On-site

$142K - $187K/yr

  • Medical

  • Retirement

As a key contributor, you will lead the on-device inference and performance optimization of ML models powering outdoor monitoring in the home security space. This role is less about inventing new CV ...

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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.
What are popular job titles related to Ml Inference jobs in Massachusetts? For Ml Inference jobs in Massachusetts, the most frequently searched job titles are:
What job categories do people searching Ml Inference jobs in Massachusetts look for? The top searched job categories for Ml Inference jobs in Massachusetts are:
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Ml Inference jobs near you

AI / ML Engineer

Third Way Health

Cambridge, MA • On-site

Full-time

Re-posted 17 days ago

Job description

Who we are
Third Way Health helps medical practices across the United States improve the patient experience while reducing the administrative burden on practice owners. We enable practices to enhance the experience of their patients by providing them with a leading service automation platform and a world class team of service representatives. What unites us is our passion to support physicians and provide better access for patients from all backgrounds.
About the position
We're seeking a Senior ML Engineer to build next-generation AI systems that help millions of patients access care faster. You'll architect production ML infrastructure handling thousands of hours of service interactions daily in a highly regulated healthcare environment. This is a high-impact individual contributor role-ideal for someone eager to "own the outcome" and push the boundaries of "high tech + high touch" care experiences.
Responsibilities
  • Architect and build large-scale AI systems that integrate high-volume voice, text, and contextual event streams with extensive knowledge bases to deliver real-time recommendations, automations, and decision support.
  • Design and operate workflow-oriented AI systems, including DAG-based execution graphs, stateful pipelines, and agent-driven workflows with clear observability, reproducibility, and fault tolerance.
  • Build agent architectures spanning agent-to-agent coordination, feedback loops, tool-calling systems, and long-running autonomous workflows, balancing control, safety, and adaptability.
  • Design and implement data models, feature pipelines, and APIs to support model training, low-latency inference, and continuous learning.
  • Develop predictive, real-time analytics systems that combine streaming data, ML inference, and event-driven triggers to surface insights and automate actions at scale.
  • Implement and maintain end-to-end ML platforms, including model training, evaluation, deployment, online inference, monitoring, and drift detection.
  • Partner closely with product managers, data scientists, and QA engineers to translate experimental models into reliable, production-grade AI services.
  • Identify, diagnose, and resolve performance and scaling bottlenecks across data pipelines, inference services, and orchestration layers as production workloads grow.

Required skills and qualifications
  • 5+ years of software engineering experience, with 3+ years focused on machine learning or applied AI systems.
  • Strong proficiency in Python, particularly for ML pipelines, frameworks, inference services, and APIs (e.g., scikit-learn, Sanic API, PyTorch Lightning, Pydantic AI, LangGraph, Bedrock, OpenAI / Anthropic SDKs).
  • Experience designing ML-centric data architectures, including feature stores, vector databases, and time-series systems for monitoring and analytics.
  • Hands-on experience with cloud-native inference: containerized model serving, autoscaling, GPU/accelerator workloads, and low-latency production deployments.
  • Experience operating end-to-end MLOps platforms (e.g., MLflow, Kubeflow), including CI/CD for models, experiment tracking, and rollout strategies.
  • Solid understanding of workflow orchestration (graph-based execution, retries, state management) in ML and agent-based systems.
  • Excellent communication skills, with the ability to collaborate effectively across engineering, product, and non-technical stakeholders.
  • Strong interest in healthcare innovation and building AI systems that meaningfully improve health outcomes.
  • Working knowledge of AI safety, bias detection, and responsible AI practices.

Desired skills and qualifications
  • Experience building AI systems in healthcare or regulated environments, with familiarity with standards such as HIPAA, GDPR, or FDA guidance.
  • Proven experience leading complex technical initiatives and mentoring junior engineers.
  • Strong applied knowledge of event-driven architectures and streaming systems (Kafka, Pub/Sub, Kinesis, RabbitMQ).
  • Hands-on experience designing and operating vector search, RAG pipelines, and hybrid retrieval systems.
  • Experience with agent frameworks, multi-agent coordination patterns, and long-running agent loops in production environments.
    Familiarity with real-time analytics stacks combining streaming data, ML inference, and operational dashboards.

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