Top Tech Jobs & Startup Jobs in Boston, MA

Design, implement, and maintain Okta identity and access management solutions, integrating Okta with cloud/SaaS applications. Build SSO and MFA deployments, automate provisioning using Workflows/SCIM/JIT, and create automation via REST APIs and webhooks. Perform audits, monitor identities and access, troubleshoot integrations, and collaborate cross-functionally to meet business requirements.

Lead Elastic’s strategy, vision, and multi-quarter roadmap for vector databases, embedding and reranking models, vector indexing, semantic ingestion, and search capabilities. Conduct market and competitive research, define product metrics, prioritize features, and partner closely with engineering, cloud, and applied research teams. Guide technical trade-offs involving recall, latency, index size, ingestion throughput, model lifecycle, and total cost of ownership across serverless and self-managed deployments.

Lead the technical vision and development of Elasticsearch’s core search infrastructure, including indexing, retrieval, query execution, and relevance. Research advances in search systems and algorithms, guide long-term technical roadmaps, collaborate across teams, mentor expert engineers, and contribute hands-on to high-performance distributed software. Serve as a subject-matter authority while driving projects from concept through completion.

Lead Elasticsearch performance engineering from architecture through production. Develop performance models, optimize distributed systems, profile bottlenecks, build benchmarks, detect regressions, and improve scalability across stateful and serverless architectures. Design AI-assisted optimization and benchmarking tools, collaborate across teams, define performance strategies, and mentor engineers. The role requires deep Java, JVM, concurrency, distributed systems, and search or data-store performance expertise.

Develop and optimize Elasticsearch performance across distributed and serverless architectures. Responsibilities include implementing performance models, profiling systems, identifying bottlenecks, building benchmarks and regression detection, improving logging, metrics, vector search, and ES|QL performance, and creating AI-assisted optimization tools. The role also involves architectural and code-level optimization, technical analysis, peer reviews, and guidance for fellow engineers.

Drive net-new Mid Market SaaS revenue by building outbound pipeline, qualifying opportunities with MEDDPICC, delivering executive demos/value stories, negotiating multi-year consumption deals, and partnering with Solutions Architects, Customer Success, Marketing, and RevOps to close complex, multi-stakeholder opportunities.

Design and build autonomous, enterprise-grounded agentic workflows using RAG and ESRE. Integrate and fine-tune LLMs with internal APIs and SaaS tools. Provision scalable cloud infrastructure via Terraform, implement CI/CD, secure network architectures, and deploy observability and tracing to monitor agent behavior. Provide technical leadership, documentation, and enable internal teams to leverage the Elastic Stack for automated business task completion.

Lead AMER sales enablement by diagnosing performance gaps using Gong/Salesforce/Clari data, designing targeted remediation, delivering blended training (MEDDPICC, value selling, pipeline forecasting, GenAI usage), embedding methodology through deal clinics, driving tool adoption, measuring enablement impact with RevOps, and partnering cross-functionally to connect training to sales outcomes.

Design, implement, and optimize Elasticsearch core storage and query engine components for observability workloads. Drive architectural improvements, resolve performance bottlenecks in indexing and serialization, optimize data layouts for object storage, lead cross-organizational initiatives, mentor engineers, triage community issues, and write idiomatic modern Java in a distributed team.

Develop and maintain core Elasticsearch infrastructure and shared platform features. Design scalable APIs, solve performance and concurrency issues, improve observability and CI/CD, automate developer workflows, lead cross-team projects, support complex operational investigations, and use AI to accelerate development and debugging.