Headquartered in Philadelphia, Pennsylvania, Comcast was established in 1963 as a single-system cable company. Over the years, Comcast experienced tremendous gr
Sr. Machine Learning Engineer, AI Recommendation & Search
Location
Pennsylvania
Posted
1 day ago
Salary
$117.1K - $274.4K / year
Seniority
Senior
Job Description
Sr. Machine Learning Engineer, AI Recommendation & Search
Comcast
Make your mark at Comcast -- a Fortune 30 global media and technology company. From the connectivity and platforms we provide, to the content and experiences we create, we reach hundreds of millions of customers, viewers, and guests worldwide. Become part of our award-winning technology team that turns big ideas into cutting-edge products, platforms, and solutions that our customers love. We create space to innovate, and we recognize, reward, and invest in your ideas, while ensuring you can proudly bring your authentic self to the workplace. Join us. You'll do the best work of your career right here at Comcast. (In most cases, Comcast prefers to have employees on-site collaborating unless the team has been designated as virtual due to the nature of their work. If a position is listed with both office locations and virtual offerings, Comcast may be willing to consider candidates who live greater than 100 miles from the office for the remote option.) Job Summary This job entails directing advanced machine learning projects for decision automation and pattern recognition. It includes setting goals, designing prototypes, and leading research. The role manages sophisticated solutions, authors critical documentation, and mentors engineers. High-level machine learning expertise and innovation capacity are essential. Job Description Responsibilities: - Leading the development of machine learning programs to identify patterns and automate decision-making processes - Setting technical objectives for machine learning assignments and overseeing their execution - Designing machine learning prototypes in collaboration with product teams and stakeholders - Conducting research and studies to inform product development and application of machine learning techniques - Implementing end-to-end machine learning solutions, including optimization technologies, and managing live deployments - Aggregating and analyzing data from multiple sources to discover patterns for feature engineering and model automation - Authoring technical documentation, including white papers and technical manuals, and contributing to intellectual property with patents and APIs - Evaluating machine learning solutions from internal and external partners, conducting case studies and reporting on outcomes - Collaborating with external teams and representing the work group in resolving technical challenges related to machine learning projects - Mentoring junior engineers in machine learning methodologies and leading by example in technical expertise and innovation - Consistent exercise of independent judgment and discretion in matters of significance. - Regular, consistent and punctual attendance. Must be able to work nights and weekends, variable schedule(s) as necessary. - Other duties and responsibilities as assigned. Employees at all levels are expected to: - Understand our Operating Principles; make them the guidelines for how you do your job. - Own the customer experience think and act in ways that put our customers first, give them seamless digital options at every touchpoint, and make them promoters of our products and services. - Know your stuff be enthusiastic learners, users and advocates of our game-changing technology, products and services, especially our digital tools and experiences. - Win as a team make big things happen by working together and being open to new ideas. - Be an active part of the Net Promoter System a way of working that brings more employee and customer feedback into the company by joining huddles, making call backs and helping us elevate opportunities to do better for our customers. - Drive results and growth. - Support a culture of inclusion in how you work and lead. - Do what's right for each other, our customers, investors and our communities. Disclaimer: This information has been designed to indicate the general nature and level of work performed by employees in this role. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications. Comcast is an equal opportunity workplace. We will consider all qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, veteran status, genetic information, or any other basis protected by applicable law. Comcast will consider for employment applicants with arrest or conviction records in accordance with the requirements of applicable law, including the San Francisco Fair Chance Ordinance, the Los Angeles Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Please note that federal state, or local laws and regulations may restrict or prohibit Comcast from hiring individuals convicted of certain crimes. Additionally, an applicant's criminal history may have a direct, adverse, and negative relationship on the job duties of this position, which may result in the withdrawal of a conditional offer of employment. Skills: Customer Experience (CX); Machine Learning (ML); Agentic AI; Agentic Orchestration; Generative AI Agents Salary: National Pay Range: $117,098.24 USD-$274,449.00 USD Illinois Pay Range: $124,416.88 USD - $241,515.12 USD Colorado Pay Range: $131,735.52 USD - $252,493.08 USD Hawaii Pay Range: $153,691.44 USD - $230,537.16 USD Washington DC Pay Range: $168,328.72 USD - $252,493.08 USD Maryland Pay Range: $139,054.16 USD - $252,493.08 USD Minnesota Pay Range: $131,735.52 USD - $230,537.16 USD New York Pay Range: $139,054.16 USD - $274,449.00 USD Washington Pay Range: $131,735.52 USD - $263,471.04 USD New Jersey Pay Range: $146,372.80 USD - $263,471.04 USD Vermont Pay Range: $139,054.16 USD - $219,559.20 USD Massachusetts Pay Range: $146,372.80 USD - $263,471.04 USD California Pay Range: $131,735.52 USD - $243,954.66 Comcast intends to offer the selected candidate base pay within this range, dependent on job-related, non-discriminatory factors such as experience. The application window is 30 days from the date job is posted, unless the number of applicants requires it to close sooner or later. The application window is 30 days from the date job is posted, unless the number of applicants requires it to close sooner or later. Base pay is one part of the Total Rewards that Comcast provides to compensate and recognize employees for their work. Most sales positions are eligible for a Commission under the terms of an applicable plan, while most non-sales positions are eligible for a Bonus. Additionally, Comcast provides best-in-class Benefits to eligible employees. We believe that benefits should connect you to the support you need when it matters most, and should help you care for those who matter most. That's why we provide an array of options, expert guidance and always-on tools, that are personalized to meet the needs of your reality - to help support you physically, financially and emotionally through the big milestones and in your everyday life. Please visit the compensation and benefits summary on our careers site for more details. Education Bachelor's Degree While possessing the stated degree is preferred, Comcast also may consider applicants who hold some combination of coursework and experience, or who have extensive related professional experience. Relevant Work Experience 7-10 Years
Related Guides
Related Job Pages
More Machine Learning Engineer Jobs
• Design, build, and maintain complex, distributed systems for the full machine learning lifecycle (from ingestion to production monitoring); • Implement and evolve our MLOps architecture, including Feature Store concepts, Model Serving, and automated CI/CD pipelines; • Manage and apply cloud infrastructure for the team's projects using Infrastructure as Code (IaC) practices; • Develop internal tools and frameworks to optimize the team's workflows; • Ensure adoption of software engineering best practices (testing, clean code, sustainable architecture); • Collaborate with business areas (Product, Customer Success, and Sales/Marketing) to translate commercial needs into viable technical solutions.
• Stand up and operate BetMGM's ML platform on AWS (SageMaker Training, Model Registry, Pipelines, Endpoints, Batch Transform) and Snowflake (Snowpark ML, Cortex), with Terraform-managed infrastructure. • Build self-service scaffolds that let data scientists ship a model end-to-end without a ticket queue — cookie-cutter project templates with CI, drift monitoring, alerting, IaC, and Snowflake connectivity pre-baked. • Design and operate batch scoring pipelines — SageMaker Batch Transform, dbt-orchestrated scoring against Snowflake, Snowpark ML — with explicit freshness and cost SLAs. • Design and operate real-time inference paths — SageMaker real-time endpoints, Lambda + Bedrock for GenAI, API Gateway — with stated latency budgets (typically sub-100ms) and graceful degradation under load. • Own the feature store (SageMaker Feature Store, Tecton, or Feast) with guaranteed online/offline parity — training-serving skew is treated as an incident, not a tradeoff. • Build CI/CD for ML — model registry, automated retraining triggers, model versioning, lineage from feature → training run → deployed model → live prediction. • Implement champion/challenger, shadow deployments, and canary releases as platform primitives so individual model teams do not reinvent them per project. • Stand up drift detection, data quality, and model performance monitoring (Evidently, Arize, or SageMaker Model Monitor — pick one and standardize) with paging that routes to humans who can fix it. • Own MLOps incident response — production model failures are SEV events with postmortems. • Right-size endpoints, batch caching, request batching, and autoscaling. State cost-per-prediction targets up front and meet them. • Integrate LLM APIs (Bedrock, Anthropic, OpenAI) into production paths — RAG pipelines, agent eval frameworks, prompt versioning, cost and latency observability.
Machine Learning Engineer
ChaosChaos creates technology that empowers architects, artists, and designers to visualize anything they can imagine.
• Design, develop, and optimize machine learning models in one or more solutions, including asset generation and capture, render enhancement, scene intelligence, agentic design workflows, and intuitive design interactions. • Investigate and bring techniques from a variety of AI research areas, such as diffusion, super-resolution, conditioned generation, plus neural and differentiable rendering, into artists’ hands. • Evaluate, integrate, and orchestrate off-the-shelf third-party foundation models to accelerate feature development and deployment. • Mentor other engineers and contribute to the growth of the team’s knowledge and expertise in machine learning. • Collaborate with cross-functional teams and our ML Product Manager to define the product requirements and scope of delivery of solutions to product teams. • Work closely with our MLOps Engineer to develop and maintain pipelines for distributed training, inference optimization/quantization/serving, experiment tracking, model versioning & validation, and deployment to the cloud (AWS/Azure/GCP). • Implement appropriate model evaluation tests, data curation processes, and apply dataset-rights awareness, and responsible AI/governance. • Stay updated and share knowledge on the latest developments in machine learning, generative AI, natural language processing, and 3D visualization, and implement cutting-edge techniques to enhance our solutions. • Ensure high-quality code and documentation, following best practices in software development and machine learning.
Senior Machine Learning Engineer
Sigma Software GroupWe support enterprises, product houses, and startups with custom software solutions development and IT consulting.
• Design, develop, and optimize scalable Machine Learning models for advertising intelligence and forecasting systems • Analyze large-scale historical and real-time datasets to improve forecasting accuracy and monetization strategies • Build and maintain distributed data processing pipelines using Spark and related Big Data technologies • Develop production-ready ML solutions using Python within AWS cloud infrastructure • Research, evaluate, and implement Machine Learning algorithms suitable for high-load AdTech environments • Improve model performance, scalability, reliability, and operational efficiency • Collaborate with Data Engineers, Product Managers, and distributed engineering teams to deliver end-to-end ML solutions • Contribute to ML architecture decisions, experimentation approaches, and engineering best practices • Monitor, validate, and optimize ML model quality and forecasting performance in production environments • Produce technical documentation related to ML pipelines, models, and distributed systems • Mentor engineers and support technical growth within the team • Drive innovation in Machine Learning and AdTech technologies




