Lead Software Platform Engineer, MLOps

🕒 August 4

🏄 California, Massachusetts – Remote

🏄 California – Remote 🍂 Massachusetts – Remote

💵 $200k - $270k / year

⏰ Full Time

🟠 Senior

🏗️ Platform Engineer

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 18%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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TetraScience

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2015

🏥 Healthcare

💼 Consulting

📦 Logistics

Healthcare • Consulting • Logistics

TetraScience is a company dedicated to transforming raw scientific data into AI-native datasets for advanced scientific applications. By collaborating closely with leading biopharmaceutical companies, TetraScience enhances productivity, accelerates insights, and ensures data integrity across the scientific value chain. Their platform offers solutions for next-generation lab data management, AI-driven scientific outcomes, and compliance with industry standards. As the first company to provide a data and AI cloud built specifically for science, TetraScience enables its clients to liberate, unify, and transform their data, overcoming traditional data silos and boosting scientific productivity by providing a flexible, open, and collaborative infrastructure.

📋 Description

• Own the technical architecture of the AI/ML platform and its customer-facing service and API surface. • Own the end-to-end model and prompt lifecycle across Databricks MLflow and AWS Bedrock, including registration, versioning, staged promotion, rollback, and multi-model serving. • Design inference infrastructure for real-time and batch AI workloads, including routing, batching, caching, concurrency control, GPU/accelerator capacity planning, large binary inputs, and graceful degradation. • Integrate AI/LLM systems using RAG and architect agentic layers with tool/function calling, MCP tooling, and agent runtimes. • Design platform security, including guardrails, prompt-injection and tool-abuse defenses, PII/PHI handling, and tenant data boundaries. • Build AI evaluation and quality infrastructure, including eval harnesses, golden datasets, CI regression gates, A/B and shadow deployment, and drift/hallucination detection. • Establish monitoring, alerting, logging, distributed tracing, and SLI/SLO/SLA practices for the AI platform. • Design reproducibility and lineage for validated pharmaceutical environments, including versioned data, code, prompts, model artifacts, and audit trails. • Contribute to infrastructure-as-code and deployment automation using CloudFormation and AWS CDK. • Own production readiness, performance, reliability, cost efficiency, incident response, and runbooks with Applied AI, data engineering, and platform teams. • Act as subject-matter expert and design authority; lead design reviews, write reference architectures and documentation, and mentor engineers. • Evaluate emerging AI infrastructure frameworks, serving runtimes, model providers, and data types, making build-versus-buy decisions.

🎯 Requirements

• 10+ years of professional experience in software engineering and infrastructure engineering, with a proven track record of designing, building, and scaling distributed, cloud-native systems in production. • Demonstrated experience as a technical leader or architect, accountable for key decisions on system design, scalability, performance, and cost optimization. • Experience designing security into a multi-tenant platform, including authorization boundaries between tenants and handling sensitive data such as PII and PHI, with awareness of LLM-specific risks like prompt injection and tool abuse. • Extensive experience building and maintaining AI/ML infrastructure in production as a multi-tenant product with external users. • Deep, hands-on experience taking LLM-based systems to production, including RAG architecture, retrieval and embedding design, prompt and model versioning, and tool or function calling. • Expert-level coding skills in TypeScript and Python building robust APIs and backend services. • Production-level experience with a model registry and serving stack, ideally Databricks MLflow. • Experience treating AI evaluation as a release gate, including eval harnesses, regression gates, and drift or quality monitoring. • Proficiency in API-first design, including REST and OpenAPI specifications. • Solid working knowledge of AWS and containerized workloads such as Docker. • Familiarity with infrastructure-as-code such as CloudFormation or CDK, CI/CD pipelines, and deployment automation. • Experience defining observability and SLI/SLO/SLA practice, including monitoring, alerting, and distributed tracing. • Ability to articulate ideas clearly to customers and cross-functional teams, influence technical direction, and mentor engineers. • Nice to have: familiarity with emerging LLM frameworks, production agentic orchestration, MCP, LLM cost monitoring, multimodal model inputs, fine-tuning or model optimization, regulated/validated environments, and scientific, life sciences, or laboratory data domains. • Visa sponsorship is not currently provided for this position.

🏖️ Benefits

• 100% employer-paid benefits for all eligible employees and immediate family members • Unlimited paid time off (PTO) • 401K • Flexible working arrangements - Remote work • Company paid Life Insurance, LTD/STD • A culture of continuous improvement where you can grow your career and get coaching

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