Machine Learning Engineer

Machine Learning Engineer (Senior, Lead, Staff & Principal) About Encore Talent Solutions Encore Talent Solutions is a trusted professional services firm dedicated to helping organizations achieve their goals by providing exceptional talent solutions. We partner closely with our clients to understand their unique culture and operational needs, delivering proactive support during times of growth, transition, and change. Our mission is to connect top talent with meaningful opportunities to drive business success. Job Description We are seeking Senior, Lead, Staff, and Principal Machine Learning Engineers to join our dynamic team. In this role, you will play a key part in deploying, optimizing, and scaling AI and machine learning models in production environments. You will bridge the gap between research and engineering by transforming cutting‑edge ML innovations into reliable, high-performance, and cost‑efficient services that power enterprise AI applications. You will collaborate with AI researchers, MLOps engineers, platform teams, software engineers, data engineers, and product leaders to deliver scalable AI solutions while improving training infrastructure, inference performance, and operational reliability across the full machine learning lifecycle. Key Responsibilities Productize machine learning models developed by research teams into scalable, reliable, and highly available production services with clearly defined service level objectives (SLOs) for latency, availability, and cost. Design, optimize, and scale distributed model training across multi-node and multi-GPU environments using modern distributed training techniques. Improve model efficiency through optimization techniques such as quantization, pruning, distillation, KV‑cache optimization, Flash Attention, and inference acceleration. Build, deploy, and maintain high-performance model serving infrastructure using modern inference frameworks and serving platforms. Design scalable inference pipelines supporting batching, streaming, autoscaling, caching, load balancing, and memory optimization. Integrate production AI systems with vector databases, feature stores, data lakes, and enterprise data pipelines. Define, monitor, and continuously improve performance, reliability, utilization, and cost metrics for production AI systems. Partner closely with MLOps teams to improve CI/CD pipelines, telemetry, observability, model registries, deployment automation, and production monitoring. Collaborate with Machine Learning Scientists to ensure reproducible training, model evaluation, and seamless production handoffs. Develop clean, maintainable, well-tested, and performant production code while contributing to engineering best practices. Participate in architecture reviews, technical design discussions, and mentoring of engineering team members (Lead, Staff, and Principal levels). Required Qualifications Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, or a related technical field. Master’s degree preferred; equivalent industry experience will be considered. Senior Level: 3–5 years of experience developing, deploying, and supporting production machine learning systems. Lead, Staff, and Principal Levels: Progressive experience leading large-scale ML infrastructure initiatives, optimizing production AI platforms, mentoring engineers, and driving technical strategy. Demonstrated experience deploying high-throughput, low-latency machine learning services in production environments. Strong proficiency in: Python PyTorch (primary) TensorFlow Experience with distributed training techniques including: Distributed Data Parallel (DDP) Fully Sharded Data Parallel (FSDP) ZeRO optimization Pipeline parallelism Tensor parallelism Experience optimizing model performance using: Quantization (PTQ, QAT, AWQ, GPTQ) Model pruning Knowledge distillation KV‑cache optimization Flash Attention Experience with scalable model serving technologies such as: vLLM NVIDIA Triton Inference Server Hugging Face Text Generation Inference (TGI) ONNX Runtime TensorRT AITemplate Strong understanding of: Full machine learning lifecycle Model deployment Inference optimization Performance profiling Production monitoring Capacity planning Experience writing performant, maintainable, production-quality software. Excellent analytical, problem-solving, communication, and collaboration skills. Preferred Qualifications Experience with vector databases and retrieval systems including: FAISS Milvus Pinecone pgvector Experience with SQL, NoSQL databases, Parquet, Delta Lake, and object storage technologies. Experience with MLOps platforms, model registries, CI/CD pipelines, Infrastructure as Code (IaC), and Kubernetes. Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform. Experience building and operating Large Language Model (LLM) infrastructure and generative AI applications. Experience collaborating across Research, Platform Engineering, Infrastructure, Data Engineering, and Product organizations. Experience mentoring engineers, establishing engineering standards, and influencing technical direction across multiple teams. Familiarity with distributed systems architecture, GPU optimization, and high-performance computing environments. Work Environment & Location Location: Remote, Hybrid, or Onsite (based on client requirements) Travel: Minimal, as required Collaborative team environment with opportunities to build and scale next-generation AI platforms, influence engineering strategy, and work on cutting‑edge machine learning technologies supporting enterprise-scale AI solutions. Equal Opportunity Employer Encore Talent Solutions is an Equal Opportunity Employer. We respect and seek to empower each individual and support the diverse cultures, perspectives, skills, and experiences within our workforce. #J-18808-Ljbffr